Digital image analysis for tumor cellularity and gleason grade to tumor volume analysis in prostate cancer.
Degree: MS, Medical Sciences, 2018, Boston University
PURPOSE: This study was undertaken to compare HALO™ software image analysis measurements of cellularity with visual estimations from the pathologist and to outline a protocol for future experimental determinations of cellularity using HALO™. Secondly, this study investigated the clinically challenging prostate cancers of Gleason score 7 by analyzing a large database of radical prostatectomy (RP) specimens with regard to their Gleason grade composition and percentage tumor volume composition. The importance of these values of tumor cellularity, prostate volume, and tumor volume data were discussed in terms of future diagnostic endeavors. Finally, this study provided a brief background on prostate cancer, prostate cancer epidemiology, digital pathology, and the limitations and difficulties in the technological transition to digital pathology. All work for this study was done at Dana-Farber Cancer Institute (Boston, MA).
METHODS: In the first part of this study, histological slides were acquired by radical prostatectomy (RP) and contained 12 tumor foci of varying degrees and sizes. These slides were scanned and imported into the HALO™ image analysis software. The tumor foci, previously demarcated by a pathologist, were annotated by hand in HALO™. An algorithm for image analysis was created by training classifiers to recognize and differentiate between epithelial tissue, stromal tissue, glass, and other. This process was accomplished by classifying 62 regions which were tested for accuracy before becoming the components of an algorithm to analyze the entire annotation layer. Each tumor focus was analyzed individually, and the results were exported into Microsoft® Excel from which relevant data were extracted. Cellularity was calculated by the percentage of tumor area that the algorithm characterized as epithelial. Cellularity values derived from HALO™ measurements for each tumor focus were compared with the visual estimations of cellularity provided by the pathologist using Pearson's correlation analysis.
In the second part of this study, a database of 1386 slides containing tumors with Gleason scores between 6 and 9 was compiled from 140 RP cases. The average percentages of Gleason grades 3, 4, and 5 in each case were determined. The percentage of each slide that was occupied by the tumor was also averaged for each case, yielding an average percentage of tumor volume for each case. The average Gleason grade 3, 4, or 5 percentage for each case was plotted against the associated average tumor volume percentage of that case. The cases of Gleason score 7 (3+4, 4+3) were then isolated and plotted in a similar manner. Pearson’s correlation analysis was used to determine the degree of linear correlation between the two variables in each plot.
Results: In the first part of this study, a statistically significant positive correlation between the cellularity estimations of the pathologist and the HALO™ cellularity measurements was found (r = 0.92, p < 0.01, n =12).
In the second part of this study, there was a statistically…
Advisors/Committee Members: Loda, Massimo F. (advisor), Spencer, Jean L. (advisor).
Subjects/Keywords: Pathology; Gleason grade; Gleason score; Cellularity; Image analysis; Prostate cancer; Tumor volume
to Zotero / EndNote / Reference
APA (6th Edition):
Chaniotakis, S. (2018). Digital image analysis for tumor cellularity and gleason grade to tumor volume analysis in prostate cancer. (Masters Thesis). Boston University. Retrieved from http://hdl.handle.net/2144/31173
Chicago Manual of Style (16th Edition):
Chaniotakis, Sotiris. “Digital image analysis for tumor cellularity and gleason grade to tumor volume analysis in prostate cancer.” 2018. Masters Thesis, Boston University. Accessed September 28, 2020.
MLA Handbook (7th Edition):
Chaniotakis, Sotiris. “Digital image analysis for tumor cellularity and gleason grade to tumor volume analysis in prostate cancer.” 2018. Web. 28 Sep 2020.
Chaniotakis S. Digital image analysis for tumor cellularity and gleason grade to tumor volume analysis in prostate cancer. [Internet] [Masters thesis]. Boston University; 2018. [cited 2020 Sep 28].
Available from: http://hdl.handle.net/2144/31173.
Council of Science Editors:
Chaniotakis S. Digital image analysis for tumor cellularity and gleason grade to tumor volume analysis in prostate cancer. [Masters Thesis]. Boston University; 2018. Available from: http://hdl.handle.net/2144/31173