Version 1
: Received: 15 February 2024 / Approved: 25 February 2024 / Online: 26 February 2024 (17:21:21 CET)
How to cite:
Bamigbade, O. Gleason Score Prediction for the Severity of Prostate Metastasis Using Machine Learning. Preprints2024, 2024021411. https://doi.org/10.20944/preprints202402.1411.v1
Bamigbade, O. Gleason Score Prediction for the Severity of Prostate Metastasis Using Machine Learning. Preprints 2024, 2024021411. https://doi.org/10.20944/preprints202402.1411.v1
Bamigbade, O. Gleason Score Prediction for the Severity of Prostate Metastasis Using Machine Learning. Preprints2024, 2024021411. https://doi.org/10.20944/preprints202402.1411.v1
APA Style
Bamigbade, O. (2024). Gleason Score Prediction for the Severity of Prostate Metastasis Using Machine Learning. Preprints. https://doi.org/10.20944/preprints202402.1411.v1
Chicago/Turabian Style
Bamigbade, O. 2024 "Gleason Score Prediction for the Severity of Prostate Metastasis Using Machine Learning" Preprints. https://doi.org/10.20944/preprints202402.1411.v1
Abstract
Prostate cancer is the second most frequent malignancy (after lung cancer) in men worldwide. Prostate tissue biopsies are usually graded using scores according to the Gleason grading system. This Gleason score is the most popular prognostic marker that reveals the potential aggressiveness of the disease. However, inter-observer variability in rating by human assessors is a major limiting factor. Such variability could lead to missing a severe case or suggesting unnecessary treatments. This study explores the discriminative ability of artificial intelligence (deep learning models) for Gleason score assessment. The study was designed to use whole-slide images of digitized H&E-stained biopsies of prostate tissues to automate the grading process and provide a remotely accessible clinical decision support system. Custom convolutional neural network architectures were trained on 10,616 images of prostate tissues. Gaussian filters were applied to pre-process the images and improve model performance. Transfer learning was applied to train eight machine learning architectures namely: Xception, VGG16 & 19, ResNet101, MobileNet, DenseNet121, EfficientNetB5 & B7. Efficient NetB7 had the best performance 85.2% compared with ground-truth classification by experts. Performance improves as more data is available. The model was deployed and hosted as a web application API on Google cloud service to ensure remote access. Tissue biopsy images can be uploaded and the corresponding Gleason score recovered immediately. This system reduces diagnostics turnaround time, increase throughput, and compensate for limited skills, especially in low resource settings.
Keywords
gleason; prostate metastasis; machine learning; deep learning; expert system
Subject
Engineering, Bioengineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
