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You searched for subject:(radiomics). Showing records 1 – 30 of 33 total matches.

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Georgia Tech

1. Jeong, Jiwoong. Machine-Learning-Based Classification of Gliblastoma Using Dynamic Susceptibility Enhanced MR Image Derived Delta-Radiomic Features.

Degree: MS, Mechanical Engineering, 2018, Georgia Tech

 Purpose: Glioblastoma (GBM) is the most aggressive cancer with poor prognosis due to its heterogeneity. The purpose of this study is to improve the tissue… (more)

Subjects/Keywords: Delta-radiomics; Machine-Learning; DSC MRI; Glioblastoma

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jeong, J. (2018). Machine-Learning-Based Classification of Gliblastoma Using Dynamic Susceptibility Enhanced MR Image Derived Delta-Radiomic Features. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61094

Chicago Manual of Style (16th Edition):

Jeong, Jiwoong. “Machine-Learning-Based Classification of Gliblastoma Using Dynamic Susceptibility Enhanced MR Image Derived Delta-Radiomic Features.” 2018. Masters Thesis, Georgia Tech. Accessed December 15, 2019. http://hdl.handle.net/1853/61094.

MLA Handbook (7th Edition):

Jeong, Jiwoong. “Machine-Learning-Based Classification of Gliblastoma Using Dynamic Susceptibility Enhanced MR Image Derived Delta-Radiomic Features.” 2018. Web. 15 Dec 2019.

Vancouver:

Jeong J. Machine-Learning-Based Classification of Gliblastoma Using Dynamic Susceptibility Enhanced MR Image Derived Delta-Radiomic Features. [Internet] [Masters thesis]. Georgia Tech; 2018. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/1853/61094.

Council of Science Editors:

Jeong J. Machine-Learning-Based Classification of Gliblastoma Using Dynamic Susceptibility Enhanced MR Image Derived Delta-Radiomic Features. [Masters Thesis]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61094


University of Toronto

2. Zhang, Yucheng. Deep Radiomics Analytics Pipeline for Prognosis of Pancreatic Ductal Adenocarcinoma.

Degree: 2019, University of Toronto

Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer… (more)

Subjects/Keywords: Pancreatic Cancer; PDAC; Radiomics; Transfer Learning; 0566

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APA (6th Edition):

Zhang, Y. (2019). Deep Radiomics Analytics Pipeline for Prognosis of Pancreatic Ductal Adenocarcinoma. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/98471

Chicago Manual of Style (16th Edition):

Zhang, Yucheng. “Deep Radiomics Analytics Pipeline for Prognosis of Pancreatic Ductal Adenocarcinoma.” 2019. Masters Thesis, University of Toronto. Accessed December 15, 2019. http://hdl.handle.net/1807/98471.

MLA Handbook (7th Edition):

Zhang, Yucheng. “Deep Radiomics Analytics Pipeline for Prognosis of Pancreatic Ductal Adenocarcinoma.” 2019. Web. 15 Dec 2019.

Vancouver:

Zhang Y. Deep Radiomics Analytics Pipeline for Prognosis of Pancreatic Ductal Adenocarcinoma. [Internet] [Masters thesis]. University of Toronto; 2019. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/1807/98471.

Council of Science Editors:

Zhang Y. Deep Radiomics Analytics Pipeline for Prognosis of Pancreatic Ductal Adenocarcinoma. [Masters Thesis]. University of Toronto; 2019. Available from: http://hdl.handle.net/1807/98471

3. Desseroit, Marie-Charlotte. Caractérisation et exploitation de l'hétérogénéité intra-tumorale des images multimodales TDM et TEP : Quantization and exploitation of intra-tumoral heterogeneity on PET and CT images.

Degree: Docteur es, Biologie-santé, 2016, Brest

L’imagerie multi-modale Tomographie par émission de positons (TEP)/ Tomodensitométrie(TDM) est la modalité d’imagerie la plus utilisée pour le diagnostic et le suivi des patients en… (more)

Subjects/Keywords: Oncologie; TEP/TDM; Traitement d’images; Radiomics; Nomogramme; SVM; Oncology; PET/CT; PET/CT image processing; Radiomics; Nomogram; SVM; 572; 616.075 7

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APA (6th Edition):

Desseroit, M. (2016). Caractérisation et exploitation de l'hétérogénéité intra-tumorale des images multimodales TDM et TEP : Quantization and exploitation of intra-tumoral heterogeneity on PET and CT images. (Doctoral Dissertation). Brest. Retrieved from http://www.theses.fr/2016BRES0129

Chicago Manual of Style (16th Edition):

Desseroit, Marie-Charlotte. “Caractérisation et exploitation de l'hétérogénéité intra-tumorale des images multimodales TDM et TEP : Quantization and exploitation of intra-tumoral heterogeneity on PET and CT images.” 2016. Doctoral Dissertation, Brest. Accessed December 15, 2019. http://www.theses.fr/2016BRES0129.

MLA Handbook (7th Edition):

Desseroit, Marie-Charlotte. “Caractérisation et exploitation de l'hétérogénéité intra-tumorale des images multimodales TDM et TEP : Quantization and exploitation of intra-tumoral heterogeneity on PET and CT images.” 2016. Web. 15 Dec 2019.

Vancouver:

Desseroit M. Caractérisation et exploitation de l'hétérogénéité intra-tumorale des images multimodales TDM et TEP : Quantization and exploitation of intra-tumoral heterogeneity on PET and CT images. [Internet] [Doctoral dissertation]. Brest; 2016. [cited 2019 Dec 15]. Available from: http://www.theses.fr/2016BRES0129.

Council of Science Editors:

Desseroit M. Caractérisation et exploitation de l'hétérogénéité intra-tumorale des images multimodales TDM et TEP : Quantization and exploitation of intra-tumoral heterogeneity on PET and CT images. [Doctoral Dissertation]. Brest; 2016. Available from: http://www.theses.fr/2016BRES0129


University of South Florida

4. Geiger, Benjamin. Change Descriptors for Determining Nodule Malignancy in Lung CT Screening Images.

Degree: 2018, University of South Florida

 Computed tomography (CT) imagery is an important weapon in the fight against lung cancer; various forms of lung cancer are routinely diagnosed from CT imagery.… (more)

Subjects/Keywords: Classification; Computer-Aided Diagnosis; Lung Cancer; Prognosis; Radiomics; Computer Sciences

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APA (6th Edition):

Geiger, B. (2018). Change Descriptors for Determining Nodule Malignancy in Lung CT Screening Images. (Thesis). University of South Florida. Retrieved from https://scholarcommons.usf.edu/etd/7505

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Geiger, Benjamin. “Change Descriptors for Determining Nodule Malignancy in Lung CT Screening Images.” 2018. Thesis, University of South Florida. Accessed December 15, 2019. https://scholarcommons.usf.edu/etd/7505.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Geiger, Benjamin. “Change Descriptors for Determining Nodule Malignancy in Lung CT Screening Images.” 2018. Web. 15 Dec 2019.

Vancouver:

Geiger B. Change Descriptors for Determining Nodule Malignancy in Lung CT Screening Images. [Internet] [Thesis]. University of South Florida; 2018. [cited 2019 Dec 15]. Available from: https://scholarcommons.usf.edu/etd/7505.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Geiger B. Change Descriptors for Determining Nodule Malignancy in Lung CT Screening Images. [Thesis]. University of South Florida; 2018. Available from: https://scholarcommons.usf.edu/etd/7505

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Cambridge

5. Yan, Jiun-Lin. Characterising peritumoural progression of glioblastoma using multimodal MRI.

Degree: PhD, 2017, University of Cambridge

 Glioblastoma is a highly malignant tumor which mostly recurs locally around the resected contrast enhancement. However, it is difficult to identify tumor invasiveness pre-surgically, especially… (more)

Subjects/Keywords: 616.99; Glioblastoma; MRI; Tumour Progression; Diffusion tensor imaging; MRS; Radiomics

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APA (6th Edition):

Yan, J. (2017). Characterising peritumoural progression of glioblastoma using multimodal MRI. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/267740 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.725530

Chicago Manual of Style (16th Edition):

Yan, Jiun-Lin. “Characterising peritumoural progression of glioblastoma using multimodal MRI.” 2017. Doctoral Dissertation, University of Cambridge. Accessed December 15, 2019. https://www.repository.cam.ac.uk/handle/1810/267740 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.725530.

MLA Handbook (7th Edition):

Yan, Jiun-Lin. “Characterising peritumoural progression of glioblastoma using multimodal MRI.” 2017. Web. 15 Dec 2019.

Vancouver:

Yan J. Characterising peritumoural progression of glioblastoma using multimodal MRI. [Internet] [Doctoral dissertation]. University of Cambridge; 2017. [cited 2019 Dec 15]. Available from: https://www.repository.cam.ac.uk/handle/1810/267740 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.725530.

Council of Science Editors:

Yan J. Characterising peritumoural progression of glioblastoma using multimodal MRI. [Doctoral Dissertation]. University of Cambridge; 2017. Available from: https://www.repository.cam.ac.uk/handle/1810/267740 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.725530


KTH

6. Buizza, Giulia. Classifying patients' response to tumour treatment from PET/CT data: a machine learning approach.

Degree: Technology and Health (STH), 2017, KTH

  Early assessment of tumour response has lately acquired big interest in the medical field, given the possibility to modify treatments during their delivery. Radiomics aims… (more)

Subjects/Keywords: treatment response; PET/CT; Radiomics; feature extraction; support vector machines

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APA (6th Edition):

Buizza, G. (2017). Classifying patients' response to tumour treatment from PET/CT data: a machine learning approach. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200916

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Buizza, Giulia. “Classifying patients' response to tumour treatment from PET/CT data: a machine learning approach.” 2017. Thesis, KTH. Accessed December 15, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200916.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Buizza, Giulia. “Classifying patients' response to tumour treatment from PET/CT data: a machine learning approach.” 2017. Web. 15 Dec 2019.

Vancouver:

Buizza G. Classifying patients' response to tumour treatment from PET/CT data: a machine learning approach. [Internet] [Thesis]. KTH; 2017. [cited 2019 Dec 15]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200916.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Buizza G. Classifying patients' response to tumour treatment from PET/CT data: a machine learning approach. [Thesis]. KTH; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200916

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Cambridge

7. Yan, Jiun-Lin. Characterising Peritumoural Progression of Glioblastoma using Multimodal MRI .

Degree: 2017, University of Cambridge

 Glioblastoma is a highly malignant tumor which mostly recurs locally around the resected contrast enhancement. However, it is difficult to identify tumor invasiveness pre-surgically, especially… (more)

Subjects/Keywords: Glioblastoma; MRI; Tumour Progression; Diffusion tensor imaging; MRS; Radiomics

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APA (6th Edition):

Yan, J. (2017). Characterising Peritumoural Progression of Glioblastoma using Multimodal MRI . (Thesis). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/267740

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yan, Jiun-Lin. “Characterising Peritumoural Progression of Glioblastoma using Multimodal MRI .” 2017. Thesis, University of Cambridge. Accessed December 15, 2019. https://www.repository.cam.ac.uk/handle/1810/267740.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yan, Jiun-Lin. “Characterising Peritumoural Progression of Glioblastoma using Multimodal MRI .” 2017. Web. 15 Dec 2019.

Vancouver:

Yan J. Characterising Peritumoural Progression of Glioblastoma using Multimodal MRI . [Internet] [Thesis]. University of Cambridge; 2017. [cited 2019 Dec 15]. Available from: https://www.repository.cam.ac.uk/handle/1810/267740.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yan J. Characterising Peritumoural Progression of Glioblastoma using Multimodal MRI . [Thesis]. University of Cambridge; 2017. Available from: https://www.repository.cam.ac.uk/handle/1810/267740

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of South Florida

8. Oliver, Jasmine Alexandria. Increasing 18F-FDG PET/CT Capabilities in Radiotherapy for Lung and Esophageal Cancer via Image Feature Analysis.

Degree: 2016, University of South Florida

 Positron Emission Tomography (PET) is an imaging modality that has become increasingly beneficial in Radiotherapy by improving treatment planning (1). PET reveals tumor volumes that… (more)

Subjects/Keywords: Fiducials; Imaging; Radiomics; Texture Analysis; Bioimaging and Biomedical Optics; Nuclear; Physics

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APA (6th Edition):

Oliver, J. A. (2016). Increasing 18F-FDG PET/CT Capabilities in Radiotherapy for Lung and Esophageal Cancer via Image Feature Analysis. (Thesis). University of South Florida. Retrieved from https://scholarcommons.usf.edu/etd/6123

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Oliver, Jasmine Alexandria. “Increasing 18F-FDG PET/CT Capabilities in Radiotherapy for Lung and Esophageal Cancer via Image Feature Analysis.” 2016. Thesis, University of South Florida. Accessed December 15, 2019. https://scholarcommons.usf.edu/etd/6123.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Oliver, Jasmine Alexandria. “Increasing 18F-FDG PET/CT Capabilities in Radiotherapy for Lung and Esophageal Cancer via Image Feature Analysis.” 2016. Web. 15 Dec 2019.

Vancouver:

Oliver JA. Increasing 18F-FDG PET/CT Capabilities in Radiotherapy for Lung and Esophageal Cancer via Image Feature Analysis. [Internet] [Thesis]. University of South Florida; 2016. [cited 2019 Dec 15]. Available from: https://scholarcommons.usf.edu/etd/6123.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Oliver JA. Increasing 18F-FDG PET/CT Capabilities in Radiotherapy for Lung and Esophageal Cancer via Image Feature Analysis. [Thesis]. University of South Florida; 2016. Available from: https://scholarcommons.usf.edu/etd/6123

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Virginia Commonwealth University

9. Mahon, Rebecca N. Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty.

Degree: PhD, Medical Physics, 2018, Virginia Commonwealth University

  ADVANCED IMAGING ANALYSIS FOR PREDICTING TUMOR RESPONSE AND IMPROVING CONTOUR DELINEATION UNCERTAINTY By Rebecca Nichole Mahon, MS A dissertation submitted in partial fulfillment of… (more)

Subjects/Keywords: machine learning; radiomics; MRI; lung cancer; convolutional neural networks; Investigative Techniques

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APA (6th Edition):

Mahon, R. N. (2018). Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty. (Doctoral Dissertation). Virginia Commonwealth University. Retrieved from https://scholarscompass.vcu.edu/etd/5516

Chicago Manual of Style (16th Edition):

Mahon, Rebecca N. “Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty.” 2018. Doctoral Dissertation, Virginia Commonwealth University. Accessed December 15, 2019. https://scholarscompass.vcu.edu/etd/5516.

MLA Handbook (7th Edition):

Mahon, Rebecca N. “Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty.” 2018. Web. 15 Dec 2019.

Vancouver:

Mahon RN. Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty. [Internet] [Doctoral dissertation]. Virginia Commonwealth University; 2018. [cited 2019 Dec 15]. Available from: https://scholarscompass.vcu.edu/etd/5516.

Council of Science Editors:

Mahon RN. Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty. [Doctoral Dissertation]. Virginia Commonwealth University; 2018. Available from: https://scholarscompass.vcu.edu/etd/5516

10. Chirra, Prathyush V, Chirra. EMPIRICAL EVALUATION OFCROSS-SITE REPRODUCIBILITY ANDDISCRIMINABILITY OF RADIOMICFEATURES FOR CHARACTERIZINGTUMOR APPEARANCE ON PROSTATEMRI.

Degree: MSs (Engineering), Biomedical Engineering, 2018, Case Western Reserve University School of Graduate Studies

Radiomics has enabled the development of a number of prognostic and predictive imagingbased tools, but there is limited work on benchmarking radiomic features across multiplesites… (more)

Subjects/Keywords: Biomedical Engineering; radiomics; discriminability; reproducibility; multisite; MRI; prostate; feature analysis; stability

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APA (6th Edition):

Chirra, Prathyush V, C. (2018). EMPIRICAL EVALUATION OFCROSS-SITE REPRODUCIBILITY ANDDISCRIMINABILITY OF RADIOMICFEATURES FOR CHARACTERIZINGTUMOR APPEARANCE ON PROSTATEMRI. (Masters Thesis). Case Western Reserve University School of Graduate Studies. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1528456281983062

Chicago Manual of Style (16th Edition):

Chirra, Prathyush V, Chirra. “EMPIRICAL EVALUATION OFCROSS-SITE REPRODUCIBILITY ANDDISCRIMINABILITY OF RADIOMICFEATURES FOR CHARACTERIZINGTUMOR APPEARANCE ON PROSTATEMRI.” 2018. Masters Thesis, Case Western Reserve University School of Graduate Studies. Accessed December 15, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1528456281983062.

MLA Handbook (7th Edition):

Chirra, Prathyush V, Chirra. “EMPIRICAL EVALUATION OFCROSS-SITE REPRODUCIBILITY ANDDISCRIMINABILITY OF RADIOMICFEATURES FOR CHARACTERIZINGTUMOR APPEARANCE ON PROSTATEMRI.” 2018. Web. 15 Dec 2019.

Vancouver:

Chirra, Prathyush V C. EMPIRICAL EVALUATION OFCROSS-SITE REPRODUCIBILITY ANDDISCRIMINABILITY OF RADIOMICFEATURES FOR CHARACTERIZINGTUMOR APPEARANCE ON PROSTATEMRI. [Internet] [Masters thesis]. Case Western Reserve University School of Graduate Studies; 2018. [cited 2019 Dec 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1528456281983062.

Council of Science Editors:

Chirra, Prathyush V C. EMPIRICAL EVALUATION OFCROSS-SITE REPRODUCIBILITY ANDDISCRIMINABILITY OF RADIOMICFEATURES FOR CHARACTERIZINGTUMOR APPEARANCE ON PROSTATEMRI. [Masters Thesis]. Case Western Reserve University School of Graduate Studies; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1528456281983062


KTH

11. Rahgozar, Parastu. Evaluation of a Radiomics Model for Classification of Lung Nodules.

Degree: Biotechnology and Health (CBH), 2019, KTH

  Lung cancer has been a major cause of death among types of cancers in the world. In the early stages, lung nodules can be… (more)

Subjects/Keywords: Lung Nodule; Radiomics; Tumor Classification; Medical Engineering; Medicinteknik

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APA (6th Edition):

Rahgozar, P. (2019). Evaluation of a Radiomics Model for Classification of Lung Nodules. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261623

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Rahgozar, Parastu. “Evaluation of a Radiomics Model for Classification of Lung Nodules.” 2019. Thesis, KTH. Accessed December 15, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261623.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Rahgozar, Parastu. “Evaluation of a Radiomics Model for Classification of Lung Nodules.” 2019. Web. 15 Dec 2019.

Vancouver:

Rahgozar P. Evaluation of a Radiomics Model for Classification of Lung Nodules. [Internet] [Thesis]. KTH; 2019. [cited 2019 Dec 15]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261623.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rahgozar P. Evaluation of a Radiomics Model for Classification of Lung Nodules. [Thesis]. KTH; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261623

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Texas Medical Center

12. Hunter, Luke. Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction.

Degree: MS, 2013, Texas Medical Center

Radiomics is the high-throughput extraction and analysis of quantitative image features. For non-small cell lung cancer (NSCLC) patients, radiomics can be applied to standard… (more)

Subjects/Keywords: Radiomics; NSCLC; imaging biomarkers; quantitative image features; Bioinformatics; Medicine and Health Sciences

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APA (6th Edition):

Hunter, L. (2013). Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction. (Masters Thesis). Texas Medical Center. Retrieved from http://digitalcommons.library.tmc.edu/utgsbs_dissertations/330

Chicago Manual of Style (16th Edition):

Hunter, Luke. “Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction.” 2013. Masters Thesis, Texas Medical Center. Accessed December 15, 2019. http://digitalcommons.library.tmc.edu/utgsbs_dissertations/330.

MLA Handbook (7th Edition):

Hunter, Luke. “Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction.” 2013. Web. 15 Dec 2019.

Vancouver:

Hunter L. Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction. [Internet] [Masters thesis]. Texas Medical Center; 2013. [cited 2019 Dec 15]. Available from: http://digitalcommons.library.tmc.edu/utgsbs_dissertations/330.

Council of Science Editors:

Hunter L. Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction. [Masters Thesis]. Texas Medical Center; 2013. Available from: http://digitalcommons.library.tmc.edu/utgsbs_dissertations/330


UCLA

13. Katrib, Amal. A Multilayered and Clinically-Informed Integration of the Transcriptome, Phenome, and Radiome in Multifactorial Disorder Assessment.

Degree: Physics and Biology in Medicine 009Y, 2018, UCLA

 Researchers continue to struggle in deciphering the underlying molecular machinery of complex, multifactorial, and comorbid medical disorders. Integrating multiple layers of data –from genomic to… (more)

Subjects/Keywords: Systematic biology; Bioinformatics; Medical imaging; Glioblastoma; Preterm Labor; Radiomics; RNA sequencing; Systems Biology; Transcriptomics

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APA (6th Edition):

Katrib, A. (2018). A Multilayered and Clinically-Informed Integration of the Transcriptome, Phenome, and Radiome in Multifactorial Disorder Assessment. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/4bh5416t

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Katrib, Amal. “A Multilayered and Clinically-Informed Integration of the Transcriptome, Phenome, and Radiome in Multifactorial Disorder Assessment.” 2018. Thesis, UCLA. Accessed December 15, 2019. http://www.escholarship.org/uc/item/4bh5416t.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Katrib, Amal. “A Multilayered and Clinically-Informed Integration of the Transcriptome, Phenome, and Radiome in Multifactorial Disorder Assessment.” 2018. Web. 15 Dec 2019.

Vancouver:

Katrib A. A Multilayered and Clinically-Informed Integration of the Transcriptome, Phenome, and Radiome in Multifactorial Disorder Assessment. [Internet] [Thesis]. UCLA; 2018. [cited 2019 Dec 15]. Available from: http://www.escholarship.org/uc/item/4bh5416t.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Katrib A. A Multilayered and Clinically-Informed Integration of the Transcriptome, Phenome, and Radiome in Multifactorial Disorder Assessment. [Thesis]. UCLA; 2018. Available from: http://www.escholarship.org/uc/item/4bh5416t

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Western Ontario

14. Mattonen, Sarah A. Radiomics for Response Assessment after Stereotactic Radiotherapy for Lung Cancer.

Degree: 2016, University of Western Ontario

 Stereotactic ablative radiotherapy (SABR) is a guideline-specified treatment option for patients with early stage non-small cell lung cancer. After treatment, patients are followed up regularly… (more)

Subjects/Keywords: Lung Cancer; Stereotactic Radiotherapy; Computed Tomography; Radiomics; Machine Learning; Classification; Medical Biophysics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Mattonen, S. A. (2016). Radiomics for Response Assessment after Stereotactic Radiotherapy for Lung Cancer. (Thesis). University of Western Ontario. Retrieved from https://ir.lib.uwo.ca/etd/3926

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Mattonen, Sarah A. “Radiomics for Response Assessment after Stereotactic Radiotherapy for Lung Cancer.” 2016. Thesis, University of Western Ontario. Accessed December 15, 2019. https://ir.lib.uwo.ca/etd/3926.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Mattonen, Sarah A. “Radiomics for Response Assessment after Stereotactic Radiotherapy for Lung Cancer.” 2016. Web. 15 Dec 2019.

Vancouver:

Mattonen SA. Radiomics for Response Assessment after Stereotactic Radiotherapy for Lung Cancer. [Internet] [Thesis]. University of Western Ontario; 2016. [cited 2019 Dec 15]. Available from: https://ir.lib.uwo.ca/etd/3926.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mattonen SA. Radiomics for Response Assessment after Stereotactic Radiotherapy for Lung Cancer. [Thesis]. University of Western Ontario; 2016. Available from: https://ir.lib.uwo.ca/etd/3926

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Universidade de Lisboa

15. Ribeiro, Cássia Oraboni. Identification and application of image biomarkers for the prediction of radiotherapy treatment response in head and neck cancer patients.

Degree: 2015, Universidade de Lisboa

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Radiações em Diagnóstico e Terapia)Universidade de Lisboa, Faculdade de Ciências, 2015

A radioterapia é uma modalidade de… (more)

Subjects/Keywords: Radioterapia; Radiomics; CT; Tumor; Biomarcadores de imagem; Teses de mestrado - 2015; Departamento de Física

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APA (6th Edition):

Ribeiro, C. O. (2015). Identification and application of image biomarkers for the prediction of radiotherapy treatment response in head and neck cancer patients. (Thesis). Universidade de Lisboa. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ul.pt:10451/20768

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ribeiro, Cássia Oraboni. “Identification and application of image biomarkers for the prediction of radiotherapy treatment response in head and neck cancer patients.” 2015. Thesis, Universidade de Lisboa. Accessed December 15, 2019. http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ul.pt:10451/20768.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ribeiro, Cássia Oraboni. “Identification and application of image biomarkers for the prediction of radiotherapy treatment response in head and neck cancer patients.” 2015. Web. 15 Dec 2019.

Vancouver:

Ribeiro CO. Identification and application of image biomarkers for the prediction of radiotherapy treatment response in head and neck cancer patients. [Internet] [Thesis]. Universidade de Lisboa; 2015. [cited 2019 Dec 15]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ul.pt:10451/20768.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ribeiro CO. Identification and application of image biomarkers for the prediction of radiotherapy treatment response in head and neck cancer patients. [Thesis]. Universidade de Lisboa; 2015. Available from: http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ul.pt:10451/20768

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

16. Badic, Bogdan. Caractérisation multiparamétrique des cancers colorectaux : Multiparametric characterization of colorectal cancer.

Degree: Docteur es, Analyse et traitement de l'information et des images médicales, 2018, Brest

L’imagerie est un outil pour réaliser le diagnostic, le bilan d’extension et le suivi thérapeutique de la grande majorité des tumeurs. La tomodensitométrie (TDM) est… (more)

Subjects/Keywords: Oncologie; TDM; Traitement d’images; Radiomique; Radiogénomique; Nomogramme; Oncology; CT; Image processing; Radiomics; Radiogenomics

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APA (6th Edition):

Badic, B. (2018). Caractérisation multiparamétrique des cancers colorectaux : Multiparametric characterization of colorectal cancer. (Doctoral Dissertation). Brest. Retrieved from http://www.theses.fr/2018BRES0070

Chicago Manual of Style (16th Edition):

Badic, Bogdan. “Caractérisation multiparamétrique des cancers colorectaux : Multiparametric characterization of colorectal cancer.” 2018. Doctoral Dissertation, Brest. Accessed December 15, 2019. http://www.theses.fr/2018BRES0070.

MLA Handbook (7th Edition):

Badic, Bogdan. “Caractérisation multiparamétrique des cancers colorectaux : Multiparametric characterization of colorectal cancer.” 2018. Web. 15 Dec 2019.

Vancouver:

Badic B. Caractérisation multiparamétrique des cancers colorectaux : Multiparametric characterization of colorectal cancer. [Internet] [Doctoral dissertation]. Brest; 2018. [cited 2019 Dec 15]. Available from: http://www.theses.fr/2018BRES0070.

Council of Science Editors:

Badic B. Caractérisation multiparamétrique des cancers colorectaux : Multiparametric characterization of colorectal cancer. [Doctoral Dissertation]. Brest; 2018. Available from: http://www.theses.fr/2018BRES0070


University of South Florida

17. Farhidzadeh, Hamidreza. Learning to Predict Clinical Outcomes from Soft Tissue Sarcoma MRI.

Degree: 2017, University of South Florida

 Soft Tissue Sarcomas (STS) are among the most dangerous diseases, with a 50% mortality rate in the USA in 2016. Heterogeneous responses to the treatments… (more)

Subjects/Keywords: Deep Learning; Ensemble Bag of Visual Words; Image Processing; Radiomics; Tumor Heterogeneity; Computer Engineering

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APA (6th Edition):

Farhidzadeh, H. (2017). Learning to Predict Clinical Outcomes from Soft Tissue Sarcoma MRI. (Thesis). University of South Florida. Retrieved from https://scholarcommons.usf.edu/etd/7398

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Farhidzadeh, Hamidreza. “Learning to Predict Clinical Outcomes from Soft Tissue Sarcoma MRI.” 2017. Thesis, University of South Florida. Accessed December 15, 2019. https://scholarcommons.usf.edu/etd/7398.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Farhidzadeh, Hamidreza. “Learning to Predict Clinical Outcomes from Soft Tissue Sarcoma MRI.” 2017. Web. 15 Dec 2019.

Vancouver:

Farhidzadeh H. Learning to Predict Clinical Outcomes from Soft Tissue Sarcoma MRI. [Internet] [Thesis]. University of South Florida; 2017. [cited 2019 Dec 15]. Available from: https://scholarcommons.usf.edu/etd/7398.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Farhidzadeh H. Learning to Predict Clinical Outcomes from Soft Tissue Sarcoma MRI. [Thesis]. University of South Florida; 2017. Available from: https://scholarcommons.usf.edu/etd/7398

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Wayne State University

18. Chetvertkov, Mikhail Aleksandrovich. Principal Component Analysis-Based Anatomical Motion Models For Use In Adaptive Radiation Therapy Of Head And Neck Cancer Patients.

Degree: PhD, 2016, Wayne State University

  Purpose: To develop standard and regularized principal component analysis (PCA) models of anatomical changes from daily cone beam CTs (CBCTs) of head and neck… (more)

Subjects/Keywords: Adaptive Radiation Therapy; Deformable Image Registration; Principal Component Analysis; Radiomics; Mathematics; Oncology; Physics

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APA (6th Edition):

Chetvertkov, M. A. (2016). Principal Component Analysis-Based Anatomical Motion Models For Use In Adaptive Radiation Therapy Of Head And Neck Cancer Patients. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/1522

Chicago Manual of Style (16th Edition):

Chetvertkov, Mikhail Aleksandrovich. “Principal Component Analysis-Based Anatomical Motion Models For Use In Adaptive Radiation Therapy Of Head And Neck Cancer Patients.” 2016. Doctoral Dissertation, Wayne State University. Accessed December 15, 2019. https://digitalcommons.wayne.edu/oa_dissertations/1522.

MLA Handbook (7th Edition):

Chetvertkov, Mikhail Aleksandrovich. “Principal Component Analysis-Based Anatomical Motion Models For Use In Adaptive Radiation Therapy Of Head And Neck Cancer Patients.” 2016. Web. 15 Dec 2019.

Vancouver:

Chetvertkov MA. Principal Component Analysis-Based Anatomical Motion Models For Use In Adaptive Radiation Therapy Of Head And Neck Cancer Patients. [Internet] [Doctoral dissertation]. Wayne State University; 2016. [cited 2019 Dec 15]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/1522.

Council of Science Editors:

Chetvertkov MA. Principal Component Analysis-Based Anatomical Motion Models For Use In Adaptive Radiation Therapy Of Head And Neck Cancer Patients. [Doctoral Dissertation]. Wayne State University; 2016. Available from: https://digitalcommons.wayne.edu/oa_dissertations/1522


Duke University

19. Lafata, Kyle. Novel Identification of Radiomic Biomarkers with Langevin Annealing .

Degree: 2018, Duke University

  As modern diagnostic imaging systems become increasingly more quantitative, new techniques and scientific disciplines are emerging as powerful avenues to personalized medicine. Leading this… (more)

Subjects/Keywords: Medical imaging; Computational physics; Mathematics; Data Clustering; Langevin Dynamics; Quantitative Imaging; Radiomics; Stochastic Dynamical Systems

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APA (6th Edition):

Lafata, K. (2018). Novel Identification of Radiomic Biomarkers with Langevin Annealing . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/17481

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lafata, Kyle. “Novel Identification of Radiomic Biomarkers with Langevin Annealing .” 2018. Thesis, Duke University. Accessed December 15, 2019. http://hdl.handle.net/10161/17481.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lafata, Kyle. “Novel Identification of Radiomic Biomarkers with Langevin Annealing .” 2018. Web. 15 Dec 2019.

Vancouver:

Lafata K. Novel Identification of Radiomic Biomarkers with Langevin Annealing . [Internet] [Thesis]. Duke University; 2018. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10161/17481.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lafata K. Novel Identification of Radiomic Biomarkers with Langevin Annealing . [Thesis]. Duke University; 2018. Available from: http://hdl.handle.net/10161/17481

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

20. Coroller, Thibaud P. Combining data science and medical imaging: Advancing cancer precision medicine with radiomics.

Degree: 2017, Datawyse / Universitaire Pers Maastricht

Radiomics is an emerging field of medical diagnostics that combines data science and medical imaging. By means of radiomics, a large amount of data can… (more)

Subjects/Keywords: radiomics; tumour phenotype; cancer; precision medicine

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APA (6th Edition):

Coroller, T. P. (2017). Combining data science and medical imaging: Advancing cancer precision medicine with radiomics. (Doctoral Dissertation). Datawyse / Universitaire Pers Maastricht. Retrieved from https://cris.maastrichtuniversity.nl/portal/en/publications/combining-data-science-and-medical-imaging(39283e13-36d8-4aba-b561-4dc9768e5b18).html ; urn:nbn:nl:ui:27-39283e13-36d8-4aba-b561-4dc9768e5b18 ; 39283e13-36d8-4aba-b561-4dc9768e5b18 ; 10.26481/dis.20171214tc ; urn:isbn:9789461597755 ; urn:nbn:nl:ui:27-39283e13-36d8-4aba-b561-4dc9768e5b18 ; https://cris.maastrichtuniversity.nl/portal/en/publications/combining-data-science-and-medical-imaging(39283e13-36d8-4aba-b561-4dc9768e5b18).html

Chicago Manual of Style (16th Edition):

Coroller, Thibaud P. “Combining data science and medical imaging: Advancing cancer precision medicine with radiomics.” 2017. Doctoral Dissertation, Datawyse / Universitaire Pers Maastricht. Accessed December 15, 2019. https://cris.maastrichtuniversity.nl/portal/en/publications/combining-data-science-and-medical-imaging(39283e13-36d8-4aba-b561-4dc9768e5b18).html ; urn:nbn:nl:ui:27-39283e13-36d8-4aba-b561-4dc9768e5b18 ; 39283e13-36d8-4aba-b561-4dc9768e5b18 ; 10.26481/dis.20171214tc ; urn:isbn:9789461597755 ; urn:nbn:nl:ui:27-39283e13-36d8-4aba-b561-4dc9768e5b18 ; https://cris.maastrichtuniversity.nl/portal/en/publications/combining-data-science-and-medical-imaging(39283e13-36d8-4aba-b561-4dc9768e5b18).html.

MLA Handbook (7th Edition):

Coroller, Thibaud P. “Combining data science and medical imaging: Advancing cancer precision medicine with radiomics.” 2017. Web. 15 Dec 2019.

Vancouver:

Coroller TP. Combining data science and medical imaging: Advancing cancer precision medicine with radiomics. [Internet] [Doctoral dissertation]. Datawyse / Universitaire Pers Maastricht; 2017. [cited 2019 Dec 15]. Available from: https://cris.maastrichtuniversity.nl/portal/en/publications/combining-data-science-and-medical-imaging(39283e13-36d8-4aba-b561-4dc9768e5b18).html ; urn:nbn:nl:ui:27-39283e13-36d8-4aba-b561-4dc9768e5b18 ; 39283e13-36d8-4aba-b561-4dc9768e5b18 ; 10.26481/dis.20171214tc ; urn:isbn:9789461597755 ; urn:nbn:nl:ui:27-39283e13-36d8-4aba-b561-4dc9768e5b18 ; https://cris.maastrichtuniversity.nl/portal/en/publications/combining-data-science-and-medical-imaging(39283e13-36d8-4aba-b561-4dc9768e5b18).html.

Council of Science Editors:

Coroller TP. Combining data science and medical imaging: Advancing cancer precision medicine with radiomics. [Doctoral Dissertation]. Datawyse / Universitaire Pers Maastricht; 2017. Available from: https://cris.maastrichtuniversity.nl/portal/en/publications/combining-data-science-and-medical-imaging(39283e13-36d8-4aba-b561-4dc9768e5b18).html ; urn:nbn:nl:ui:27-39283e13-36d8-4aba-b561-4dc9768e5b18 ; 39283e13-36d8-4aba-b561-4dc9768e5b18 ; 10.26481/dis.20171214tc ; urn:isbn:9789461597755 ; urn:nbn:nl:ui:27-39283e13-36d8-4aba-b561-4dc9768e5b18 ; https://cris.maastrichtuniversity.nl/portal/en/publications/combining-data-science-and-medical-imaging(39283e13-36d8-4aba-b561-4dc9768e5b18).html


Texas Medical Center

21. Fave, Xenia J; and#60;pand#62;orcid.org/0000-0003-0150-0843and#60;/pand#62. Detecting and Evaluating Therapy Induced Changes in Radiomics Features Measured from Non-Small Cell Lung Cancer to Predict Patient Outcomes.

Degree: PhD, 2017, Texas Medical Center

  The purpose of this study was to investigate whether radiomics features measured from weekly 4-dimensional computed tomography (4DCT) images of non-small cell lung cancers… (more)

Subjects/Keywords: Radiomics; Non-Small Cell Lung Cancer; Quantitative Imaging; Texture; Outcomes; Modeling; Medical Biomathematics and Biometrics; Multivariate Analysis; Other Physics

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APA (6th Edition):

Fave, X. J. a. o. (2017). Detecting and Evaluating Therapy Induced Changes in Radiomics Features Measured from Non-Small Cell Lung Cancer to Predict Patient Outcomes. (Doctoral Dissertation). Texas Medical Center. Retrieved from http://digitalcommons.library.tmc.edu/utgsbs_dissertations/778

Chicago Manual of Style (16th Edition):

Fave, Xenia J; and#60;pand#62;orcid org/0000-0003-0150-0843and#60;/pand#62. “Detecting and Evaluating Therapy Induced Changes in Radiomics Features Measured from Non-Small Cell Lung Cancer to Predict Patient Outcomes.” 2017. Doctoral Dissertation, Texas Medical Center. Accessed December 15, 2019. http://digitalcommons.library.tmc.edu/utgsbs_dissertations/778.

MLA Handbook (7th Edition):

Fave, Xenia J; and#60;pand#62;orcid org/0000-0003-0150-0843and#60;/pand#62. “Detecting and Evaluating Therapy Induced Changes in Radiomics Features Measured from Non-Small Cell Lung Cancer to Predict Patient Outcomes.” 2017. Web. 15 Dec 2019.

Vancouver:

Fave XJao. Detecting and Evaluating Therapy Induced Changes in Radiomics Features Measured from Non-Small Cell Lung Cancer to Predict Patient Outcomes. [Internet] [Doctoral dissertation]. Texas Medical Center; 2017. [cited 2019 Dec 15]. Available from: http://digitalcommons.library.tmc.edu/utgsbs_dissertations/778.

Council of Science Editors:

Fave XJao. Detecting and Evaluating Therapy Induced Changes in Radiomics Features Measured from Non-Small Cell Lung Cancer to Predict Patient Outcomes. [Doctoral Dissertation]. Texas Medical Center; 2017. Available from: http://digitalcommons.library.tmc.edu/utgsbs_dissertations/778


University of South Florida

22. Basu, Satrajit. Developing Predictive Models for Lung Tumor Analysis.

Degree: 2012, University of South Florida

 A CT-scan of lungs has become ubiquitous as a thoracic diagnostic tool. Thus, using CT-scan images in developing predictive models for tumor types and survival… (more)

Subjects/Keywords: Classifiers; CT-scan; Feature Selection; Image Features; Radiomics; Support Vector Machine; American Studies; Arts and Humanities; Computer Sciences

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APA (6th Edition):

Basu, S. (2012). Developing Predictive Models for Lung Tumor Analysis. (Thesis). University of South Florida. Retrieved from https://scholarcommons.usf.edu/etd/3963

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Basu, Satrajit. “Developing Predictive Models for Lung Tumor Analysis.” 2012. Thesis, University of South Florida. Accessed December 15, 2019. https://scholarcommons.usf.edu/etd/3963.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Basu, Satrajit. “Developing Predictive Models for Lung Tumor Analysis.” 2012. Web. 15 Dec 2019.

Vancouver:

Basu S. Developing Predictive Models for Lung Tumor Analysis. [Internet] [Thesis]. University of South Florida; 2012. [cited 2019 Dec 15]. Available from: https://scholarcommons.usf.edu/etd/3963.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Basu S. Developing Predictive Models for Lung Tumor Analysis. [Thesis]. University of South Florida; 2012. Available from: https://scholarcommons.usf.edu/etd/3963

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of South Florida

23. Altazi, Badereldeen Abdulmajeed. 18F-FDG PET/CTCT-based Radiomics for the Prediction of Radiochemotherapy Treatment Outcomes of Cervical Cancer.

Degree: 2017, University of South Florida

 Cervical cancer remains the third most commonly diagnosed gynecological malignancy in the United States and throughout the world despite being potentially preventable. Patients diagnosed with… (more)

Subjects/Keywords: PET/CT; Cervical Cancer; Radiomics; Habitats; Radiochemotherapy; outcome prediction; Bioimaging and Biomedical Optics; Biophysics; Medicine and Health Sciences

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APA (6th Edition):

Altazi, B. A. (2017). 18F-FDG PET/CTCT-based Radiomics for the Prediction of Radiochemotherapy Treatment Outcomes of Cervical Cancer. (Thesis). University of South Florida. Retrieved from https://scholarcommons.usf.edu/etd/7390

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Altazi, Badereldeen Abdulmajeed. “18F-FDG PET/CTCT-based Radiomics for the Prediction of Radiochemotherapy Treatment Outcomes of Cervical Cancer.” 2017. Thesis, University of South Florida. Accessed December 15, 2019. https://scholarcommons.usf.edu/etd/7390.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Altazi, Badereldeen Abdulmajeed. “18F-FDG PET/CTCT-based Radiomics for the Prediction of Radiochemotherapy Treatment Outcomes of Cervical Cancer.” 2017. Web. 15 Dec 2019.

Vancouver:

Altazi BA. 18F-FDG PET/CTCT-based Radiomics for the Prediction of Radiochemotherapy Treatment Outcomes of Cervical Cancer. [Internet] [Thesis]. University of South Florida; 2017. [cited 2019 Dec 15]. Available from: https://scholarcommons.usf.edu/etd/7390.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Altazi BA. 18F-FDG PET/CTCT-based Radiomics for the Prediction of Radiochemotherapy Treatment Outcomes of Cervical Cancer. [Thesis]. University of South Florida; 2017. Available from: https://scholarcommons.usf.edu/etd/7390

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Universitat Politècnica de València

24. Ortiz Ramón, Rafael. Radiomics for diagnosis and assessing brain diseases: an approach based on texture analysis on magnetic resonance imaging .

Degree: 2019, Universitat Politècnica de València

 [ES] En los últimos años, los investigadores han intentado explotar la información de las imágenes médicas a través de la evaluación de parámetros cuantitativos para… (more)

Subjects/Keywords: Radiomics; Imagen por resonancia magnética; Análisis de texturas; Aprendizaje automático; Glioblastoma; Metástasis cerebral; Enfermedad de Alzheimer; Ictus cerebral

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APA (6th Edition):

Ortiz Ramón, R. (2019). Radiomics for diagnosis and assessing brain diseases: an approach based on texture analysis on magnetic resonance imaging . (Doctoral Dissertation). Universitat Politècnica de València. Retrieved from http://hdl.handle.net/10251/119118

Chicago Manual of Style (16th Edition):

Ortiz Ramón, Rafael. “Radiomics for diagnosis and assessing brain diseases: an approach based on texture analysis on magnetic resonance imaging .” 2019. Doctoral Dissertation, Universitat Politècnica de València. Accessed December 15, 2019. http://hdl.handle.net/10251/119118.

MLA Handbook (7th Edition):

Ortiz Ramón, Rafael. “Radiomics for diagnosis and assessing brain diseases: an approach based on texture analysis on magnetic resonance imaging .” 2019. Web. 15 Dec 2019.

Vancouver:

Ortiz Ramón R. Radiomics for diagnosis and assessing brain diseases: an approach based on texture analysis on magnetic resonance imaging . [Internet] [Doctoral dissertation]. Universitat Politècnica de València; 2019. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10251/119118.

Council of Science Editors:

Ortiz Ramón R. Radiomics for diagnosis and assessing brain diseases: an approach based on texture analysis on magnetic resonance imaging . [Doctoral Dissertation]. Universitat Politècnica de València; 2019. Available from: http://hdl.handle.net/10251/119118

25. Reuzé, Sylvain. Extraction et analyse de biomarqueurs issus des imageries TEP et IRM pour l'amélioration de la planification de traitement en radiothérapie : Extraction and analysis of biomarkers derived from PET and MR imaging for improved treatment planning in radiotherapy.

Degree: Docteur es, Recherche clinique, innovation technologique, santé publique, 2018, Paris Saclay

Au-delà des techniques conventionnelles de diagnostic et de suivi du cancer, l’analyse radiomique a pour objectif de permettre une médecine plus personnalisée dans le domaine… (more)

Subjects/Keywords: Hétérogénéité biologique; TEP; IRM; Radiomique; Radiothérapie; Planification de traitement; Biological heterogeneity; PET; MRI; Radiomics; Radiotherapy; Treatment planning

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APA (6th Edition):

Reuzé, S. (2018). Extraction et analyse de biomarqueurs issus des imageries TEP et IRM pour l'amélioration de la planification de traitement en radiothérapie : Extraction and analysis of biomarkers derived from PET and MR imaging for improved treatment planning in radiotherapy. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2018SACLS341

Chicago Manual of Style (16th Edition):

Reuzé, Sylvain. “Extraction et analyse de biomarqueurs issus des imageries TEP et IRM pour l'amélioration de la planification de traitement en radiothérapie : Extraction and analysis of biomarkers derived from PET and MR imaging for improved treatment planning in radiotherapy.” 2018. Doctoral Dissertation, Paris Saclay. Accessed December 15, 2019. http://www.theses.fr/2018SACLS341.

MLA Handbook (7th Edition):

Reuzé, Sylvain. “Extraction et analyse de biomarqueurs issus des imageries TEP et IRM pour l'amélioration de la planification de traitement en radiothérapie : Extraction and analysis of biomarkers derived from PET and MR imaging for improved treatment planning in radiotherapy.” 2018. Web. 15 Dec 2019.

Vancouver:

Reuzé S. Extraction et analyse de biomarqueurs issus des imageries TEP et IRM pour l'amélioration de la planification de traitement en radiothérapie : Extraction and analysis of biomarkers derived from PET and MR imaging for improved treatment planning in radiotherapy. [Internet] [Doctoral dissertation]. Paris Saclay; 2018. [cited 2019 Dec 15]. Available from: http://www.theses.fr/2018SACLS341.

Council of Science Editors:

Reuzé S. Extraction et analyse de biomarqueurs issus des imageries TEP et IRM pour l'amélioration de la planification de traitement en radiothérapie : Extraction and analysis of biomarkers derived from PET and MR imaging for improved treatment planning in radiotherapy. [Doctoral Dissertation]. Paris Saclay; 2018. Available from: http://www.theses.fr/2018SACLS341

26. Leijenaar, Ralph T. H. Radiomics: Images are more than meets the eye.

Degree: 2017, Datawyse / Universitaire Pers Maastricht

Radiomics is a process that aims to extract quantitative data through medical imaging, with the goal of integrating this data into clinical decision-making tools. These… (more)

Subjects/Keywords: radiomics; personalisation; methodology; potential; personalised medicine; standardisation

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APA (6th Edition):

Leijenaar, R. T. H. (2017). Radiomics: Images are more than meets the eye. (Doctoral Dissertation). Datawyse / Universitaire Pers Maastricht. Retrieved from https://cris.maastrichtuniversity.nl/portal/en/publications/radiomics(48c38416-26d6-494a-b2b7-81c2a637414a).html ; urn:nbn:nl:ui:27-48c38416-26d6-494a-b2b7-81c2a637414a ; 48c38416-26d6-494a-b2b7-81c2a637414a ; 10.26481/dis20171212rl ; urn:isbn:9789461597816 ; urn:nbn:nl:ui:27-48c38416-26d6-494a-b2b7-81c2a637414a ; https://cris.maastrichtuniversity.nl/portal/en/publications/radiomics(48c38416-26d6-494a-b2b7-81c2a637414a).html

Chicago Manual of Style (16th Edition):

Leijenaar, Ralph T H. “Radiomics: Images are more than meets the eye.” 2017. Doctoral Dissertation, Datawyse / Universitaire Pers Maastricht. Accessed December 15, 2019. https://cris.maastrichtuniversity.nl/portal/en/publications/radiomics(48c38416-26d6-494a-b2b7-81c2a637414a).html ; urn:nbn:nl:ui:27-48c38416-26d6-494a-b2b7-81c2a637414a ; 48c38416-26d6-494a-b2b7-81c2a637414a ; 10.26481/dis20171212rl ; urn:isbn:9789461597816 ; urn:nbn:nl:ui:27-48c38416-26d6-494a-b2b7-81c2a637414a ; https://cris.maastrichtuniversity.nl/portal/en/publications/radiomics(48c38416-26d6-494a-b2b7-81c2a637414a).html.

MLA Handbook (7th Edition):

Leijenaar, Ralph T H. “Radiomics: Images are more than meets the eye.” 2017. Web. 15 Dec 2019.

Vancouver:

Leijenaar RTH. Radiomics: Images are more than meets the eye. [Internet] [Doctoral dissertation]. Datawyse / Universitaire Pers Maastricht; 2017. [cited 2019 Dec 15]. Available from: https://cris.maastrichtuniversity.nl/portal/en/publications/radiomics(48c38416-26d6-494a-b2b7-81c2a637414a).html ; urn:nbn:nl:ui:27-48c38416-26d6-494a-b2b7-81c2a637414a ; 48c38416-26d6-494a-b2b7-81c2a637414a ; 10.26481/dis20171212rl ; urn:isbn:9789461597816 ; urn:nbn:nl:ui:27-48c38416-26d6-494a-b2b7-81c2a637414a ; https://cris.maastrichtuniversity.nl/portal/en/publications/radiomics(48c38416-26d6-494a-b2b7-81c2a637414a).html.

Council of Science Editors:

Leijenaar RTH. Radiomics: Images are more than meets the eye. [Doctoral Dissertation]. Datawyse / Universitaire Pers Maastricht; 2017. Available from: https://cris.maastrichtuniversity.nl/portal/en/publications/radiomics(48c38416-26d6-494a-b2b7-81c2a637414a).html ; urn:nbn:nl:ui:27-48c38416-26d6-494a-b2b7-81c2a637414a ; 48c38416-26d6-494a-b2b7-81c2a637414a ; 10.26481/dis20171212rl ; urn:isbn:9789461597816 ; urn:nbn:nl:ui:27-48c38416-26d6-494a-b2b7-81c2a637414a ; https://cris.maastrichtuniversity.nl/portal/en/publications/radiomics(48c38416-26d6-494a-b2b7-81c2a637414a).html


Case Western Reserve University

27. Penzias, Gregory. Identifying the Histomorphometric Basis of Predictive Radiomic Markers for Characterization of Prostate Cancer.

Degree: MSs (Engineering), Biomedical Engineering, 2017, Case Western Reserve University

Radiomics has shown promise for in vivo prediction of cancer risk, thus providing a potential avenue for reducing over-treatment and unnecessarily invasive biopsy-based diagnosis. Radiomics(more)

Subjects/Keywords: Biomedical Engineering; Computer Science; Medical Imaging; Radiology; Oncology; Engineering; radiomics; quantitative histomorphometry; prostate cancer; imaging biomarkers; digital pathology; data fusion; computer vision; image reconstruction; image stitching

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Penzias, G. (2017). Identifying the Histomorphometric Basis of Predictive Radiomic Markers for Characterization of Prostate Cancer. (Masters Thesis). Case Western Reserve University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1473415195867117

Chicago Manual of Style (16th Edition):

Penzias, Gregory. “Identifying the Histomorphometric Basis of Predictive Radiomic Markers for Characterization of Prostate Cancer.” 2017. Masters Thesis, Case Western Reserve University. Accessed December 15, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1473415195867117.

MLA Handbook (7th Edition):

Penzias, Gregory. “Identifying the Histomorphometric Basis of Predictive Radiomic Markers for Characterization of Prostate Cancer.” 2017. Web. 15 Dec 2019.

Vancouver:

Penzias G. Identifying the Histomorphometric Basis of Predictive Radiomic Markers for Characterization of Prostate Cancer. [Internet] [Masters thesis]. Case Western Reserve University; 2017. [cited 2019 Dec 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1473415195867117.

Council of Science Editors:

Penzias G. Identifying the Histomorphometric Basis of Predictive Radiomic Markers for Characterization of Prostate Cancer. [Masters Thesis]. Case Western Reserve University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1473415195867117


Case Western Reserve University

28. Prasanna, Prateek. NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS.

Degree: PhD, Biomedical Engineering, 2017, Case Western Reserve University

 Cancer is not a bounded, self-organized system. Most malignant tumors have heterogeneous growth, leading to disorderly proliferation well beyond the surgical margins. In fact, the… (more)

Subjects/Keywords: Biomedical Engineering; Biomedical Research; Radiomics; Texture; Brain; Necrosis; Recurrence; Machine Learning; Radiogenomics; GBM; CoLlAGe; Cancer; Image analysis; Habitat; Survival; Prognosis; Diagnosis; treatment evaluation

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APA (6th Edition):

Prasanna, P. (2017). NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS. (Doctoral Dissertation). Case Western Reserve University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case149624929700524

Chicago Manual of Style (16th Edition):

Prasanna, Prateek. “NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS.” 2017. Doctoral Dissertation, Case Western Reserve University. Accessed December 15, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case149624929700524.

MLA Handbook (7th Edition):

Prasanna, Prateek. “NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS.” 2017. Web. 15 Dec 2019.

Vancouver:

Prasanna P. NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS. [Internet] [Doctoral dissertation]. Case Western Reserve University; 2017. [cited 2019 Dec 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case149624929700524.

Council of Science Editors:

Prasanna P. NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS. [Doctoral Dissertation]. Case Western Reserve University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case149624929700524

29. Grossmann, Patrick Benedict Hans Juan. Defining the biological and clinical basis of radiomics: towards clinical imaging biomarkers.

Degree: 2018, Datawyse / Universitaire Pers Maastricht

 Artificial Intelligence is on the verge of gaining a seminal role in advanced medicine. This brings new hope for modern cancer care, where success of… (more)

Subjects/Keywords: AI; cancer; precision medicine; big data; machine learning; radiomics; genomics; diagnostic; survival

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Grossmann, P. B. H. J. (2018). Defining the biological and clinical basis of radiomics: towards clinical imaging biomarkers. (Doctoral Dissertation). Datawyse / Universitaire Pers Maastricht. Retrieved from https://cris.maastrichtuniversity.nl/portal/en/publications/defining-the-biological-and-clinical-basis-of-radiomics(16f46a96-b1bc-4c00-9f60-250678962f6d).html ; urn:nbn:nl:ui:27-16f46a96-b1bc-4c00-9f60-250678962f6d ; 16f46a96-b1bc-4c00-9f60-250678962f6d ; 10.26481/dis.20180308pg ; urn:isbn:9789461598103 ; urn:nbn:nl:ui:27-16f46a96-b1bc-4c00-9f60-250678962f6d ; https://cris.maastrichtuniversity.nl/portal/en/publications/defining-the-biological-and-clinical-basis-of-radiomics(16f46a96-b1bc-4c00-9f60-250678962f6d).html

Chicago Manual of Style (16th Edition):

Grossmann, Patrick Benedict Hans Juan. “Defining the biological and clinical basis of radiomics: towards clinical imaging biomarkers.” 2018. Doctoral Dissertation, Datawyse / Universitaire Pers Maastricht. Accessed December 15, 2019. https://cris.maastrichtuniversity.nl/portal/en/publications/defining-the-biological-and-clinical-basis-of-radiomics(16f46a96-b1bc-4c00-9f60-250678962f6d).html ; urn:nbn:nl:ui:27-16f46a96-b1bc-4c00-9f60-250678962f6d ; 16f46a96-b1bc-4c00-9f60-250678962f6d ; 10.26481/dis.20180308pg ; urn:isbn:9789461598103 ; urn:nbn:nl:ui:27-16f46a96-b1bc-4c00-9f60-250678962f6d ; https://cris.maastrichtuniversity.nl/portal/en/publications/defining-the-biological-and-clinical-basis-of-radiomics(16f46a96-b1bc-4c00-9f60-250678962f6d).html.

MLA Handbook (7th Edition):

Grossmann, Patrick Benedict Hans Juan. “Defining the biological and clinical basis of radiomics: towards clinical imaging biomarkers.” 2018. Web. 15 Dec 2019.

Vancouver:

Grossmann PBHJ. Defining the biological and clinical basis of radiomics: towards clinical imaging biomarkers. [Internet] [Doctoral dissertation]. Datawyse / Universitaire Pers Maastricht; 2018. [cited 2019 Dec 15]. Available from: https://cris.maastrichtuniversity.nl/portal/en/publications/defining-the-biological-and-clinical-basis-of-radiomics(16f46a96-b1bc-4c00-9f60-250678962f6d).html ; urn:nbn:nl:ui:27-16f46a96-b1bc-4c00-9f60-250678962f6d ; 16f46a96-b1bc-4c00-9f60-250678962f6d ; 10.26481/dis.20180308pg ; urn:isbn:9789461598103 ; urn:nbn:nl:ui:27-16f46a96-b1bc-4c00-9f60-250678962f6d ; https://cris.maastrichtuniversity.nl/portal/en/publications/defining-the-biological-and-clinical-basis-of-radiomics(16f46a96-b1bc-4c00-9f60-250678962f6d).html.

Council of Science Editors:

Grossmann PBHJ. Defining the biological and clinical basis of radiomics: towards clinical imaging biomarkers. [Doctoral Dissertation]. Datawyse / Universitaire Pers Maastricht; 2018. Available from: https://cris.maastrichtuniversity.nl/portal/en/publications/defining-the-biological-and-clinical-basis-of-radiomics(16f46a96-b1bc-4c00-9f60-250678962f6d).html ; urn:nbn:nl:ui:27-16f46a96-b1bc-4c00-9f60-250678962f6d ; 16f46a96-b1bc-4c00-9f60-250678962f6d ; 10.26481/dis.20180308pg ; urn:isbn:9789461598103 ; urn:nbn:nl:ui:27-16f46a96-b1bc-4c00-9f60-250678962f6d ; https://cris.maastrichtuniversity.nl/portal/en/publications/defining-the-biological-and-clinical-basis-of-radiomics(16f46a96-b1bc-4c00-9f60-250678962f6d).html

30. Krafft, Shane P. Utilizing Computed Tomography Image Features to Advance Prediction of Radiation Pneumonitis.

Degree: PhD, 2016, Texas Medical Center

  Improving outcomes for non-small-cell lung cancer patients treated with radiation therapy (RT) requires optimizing the balance between local tumor control and risk of normal… (more)

Subjects/Keywords: Radiation pneumonitis; radiomics; texture analysis; computed tomography; lung; predictive modeling; Applied Statistics; Other Physics

…21 CHAPTER 3: CHARACTERIZING RADIOMICS FEATURES FOR ANALYSIS OF 4‐DIMENSIONAL CT IMAGES IN… …66 CHAPTER 5: QUANTIFYING CT RADIOMICS FEATURES IN REGIONALLY‐DEFINED SUBVOLUMES OF THE… …THE SPATIAL DIFFERENCES IN CT‐BASED LUNG RADIOMICS FEATURES… …104 CHAPTER 7: INCORPORATION OF CT LUNG RADIOMICS FEATURE DISTRIBUTIONS INTO… …120 CHAPTER 8: THE UTILITY OF QUANTITATIVE CT RADIOMICS FEATURES FOR IMPROVED PREDICTION OF… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Krafft, S. P. (2016). Utilizing Computed Tomography Image Features to Advance Prediction of Radiation Pneumonitis. (Doctoral Dissertation). Texas Medical Center. Retrieved from http://digitalcommons.library.tmc.edu/utgsbs_dissertations/697

Chicago Manual of Style (16th Edition):

Krafft, Shane P. “Utilizing Computed Tomography Image Features to Advance Prediction of Radiation Pneumonitis.” 2016. Doctoral Dissertation, Texas Medical Center. Accessed December 15, 2019. http://digitalcommons.library.tmc.edu/utgsbs_dissertations/697.

MLA Handbook (7th Edition):

Krafft, Shane P. “Utilizing Computed Tomography Image Features to Advance Prediction of Radiation Pneumonitis.” 2016. Web. 15 Dec 2019.

Vancouver:

Krafft SP. Utilizing Computed Tomography Image Features to Advance Prediction of Radiation Pneumonitis. [Internet] [Doctoral dissertation]. Texas Medical Center; 2016. [cited 2019 Dec 15]. Available from: http://digitalcommons.library.tmc.edu/utgsbs_dissertations/697.

Council of Science Editors:

Krafft SP. Utilizing Computed Tomography Image Features to Advance Prediction of Radiation Pneumonitis. [Doctoral Dissertation]. Texas Medical Center; 2016. Available from: http://digitalcommons.library.tmc.edu/utgsbs_dissertations/697

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