Combination possibility and deep learning model as clinical decision-aided approach for prostate cancer. (June 2020)

• Record Type: Journal Article
• Authors: Eminaga, Okyaz; Al-Hamad, Omran; Boegemann, Martin; Breil, Bernhard; Semjonow, Axel
• Other Names: Bian Jiang guest-editor.; Zhang Yaoyun guest-editor.; Tao Cui guest-editor.
• Is Part Of: Health informatics journal. Volume 26:Number 2(2020)
• Page Start: 945
• Page End: 962
• Publication Date: 2020-06
• Subjects: classification model -- combination and learn model -- deep learning -- pathology -- prediction model -- prostate cancer -- risk classification -- SEER -- wide learning; Medical informatics -- Periodicals; 610.285
• DOI: 10.1177/1460458219855884
• Languages: English
• ISSNs: 1460-4582

Abstract

This study aims to introduce as proof of concept a combination model for classification of prostate cancer using deep learning approaches. We utilized patients with prostate cancer who underwent surgical treatment representing the various conditions of disease progression. All possible combinations of significant variables from logistic regression and correlation analyses were determined from study data sets. The combination possibility and deep learning model was developed to predict these combinations that represented clinically meaningful patient's subgroups. The observed relative frequencies of different tumor stages and Gleason score Gls changes from biopsy to prostatectomy were available for each group. Deep learning models and seven machine learning approaches were compared for the classification performance of Gleason score changes and pT2 stage. Deep models achieved the highest F1 scores by pT2 tumors (0.849) and Gls change (0.574). Combination possibility and deep learning model is a useful decision-aided tool for prostate cancer and to group patients with prostate cancer into clinically meaningful groups.