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Differentiation among prostate cancer patients with Gleason score of 7 using histopathology whole-slide image and genomic data
Author(s): Jian Ren; Kubra Karagoz; Michael Gatza; David J. Foran; Xin Qi
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Paper Abstract

Prostate cancer is the most common non-skin related cancer affecting 1 in 7 men in the United States. Treatment of patients with prostate cancer still remains a difficult decision-making process that requires physicians to balance clinical benefits, life expectancy, comorbidities, and treatment-related side effects. Gleason score (a sum of the primary and secondary Gleason patterns) solely based on morphological prostate glandular architecture has shown as one of the best predictors of prostate cancer outcome. Significant progress has been made on molecular subtyping prostate cancer delineated through the increasing use of gene sequencing. Prostate cancer patients with Gleason score of 7 show heterogeneity in recurrence and survival outcomes. Therefore, we propose to assess the correlation between histopathology images and genomic data with disease recurrence in prostate tumors with a Gleason 7 score to identify prognostic markers. In the study, we identify image biomarkers within tissue WSIs by modeling the spatial relationship from automatically created patches as a sequence within WSI by adopting a recurrence network model, namely long short-term memory (LSTM). Our preliminary results demonstrate that integrating image biomarkers from CNN with LSTM and genomic pathway scores, is more strongly correlated with patients recurrence of disease compared to standard clinical markers and engineered image texture features. The study further demonstrates that prostate cancer patients with Gleason score of 4+3 have a higher risk of disease progression and recurrence compared to prostate cancer patients with Gleason score of 3+4.

Paper Details

Date Published: 6 March 2018
PDF: 6 pages
Proc. SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 1057904 (6 March 2018); doi: 10.1117/12.2293193
Show Author Affiliations
Jian Ren, Rutgers Univ. (United States)
Kubra Karagoz, Rutgers Cancer Institute of New Jersey (United States)
Michael Gatza, Rutgers Cancer Institute of New Jersey (United States)
David J. Foran, Rutgers Cancer Institute of New Jersey (United States)
Xin Qi, Rutgers Cancer Institute of New Jersey (United States)


Published in SPIE Proceedings Vol. 10579:
Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications
Jianguo Zhang; Po-Hao Chen, Editor(s)

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