Abstract LB390: Enhancing prostate cancer diagnosis: A deep learning approach for DNA ploidy prediction from whole slide images

Abstract

Abstract Prostate cancer is distinguished by unique histological alterations in glandular architecture, observable in Whole Slide Images (WSIs). The disease is further characterized by pronounced genomic instability, exemplified by biomarkers such as DNA ploidy. While the Gleason Score effectively assesses the histological changes, the manual evaluation of DNA ploidy is subjective and time-consuming. Addressing the need for an objective diagnostic tool, our study developed a deep learning model aimed at accurately predicting DNA ploidy using publicly available WSI from the Cancer Genome Atlas (TCGA). Utilizing a ResNet-18 convolutional neural network, we trained the model on WSIs from 200 TCGA patients to extract features and predict total mRNA expression. The model was further refined using data from 19 TCGA patients, focusing on ploidy number prediction. Comparative analysis against a random reference model revealed significant improvements of ploidy prediction in Mean Absolute Error (MAE) by 43.52%, Mean Absolute Percentage Error (MAPE) by 43.59%, and Mean Squared Error (MSE) by 67.86%. These enhancements were statistically validated (p-values: MAE - 7.07E-06, MAPE - 7.69E-06, MSE - 3.14E-05) and substantiated by large Cohen's d values (MAE - 2.88, MAPE - 2.86, MSE - 2.42), confirming the model's advanced predictive accuracy and practical utility. This approach, validated further on a separate TCGA hold-out test set, signifies a major leap in prostate cancer diagnostics. By integrating mRNA prediction checkpoints, our model not only refines ploidy assessment but also sets the stage for correlating these predictions with patient clinical outcomes. These developments promise to enhance existing diagnostic methods, offering a more objective and efficient tool for DNA ploidy assessment in prostate cancer prognosis and treatment planning. Citation Format: Josselyn Sofia Vergara Cobos, Francisco Carrillo-Perez, Marija Pizurica, Noemi Andor, Olivier Gevaert. Enhancing prostate cancer diagnosis: A deep learning approach for DNA ploidy prediction from whole slide images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB390.