Abstract 5302: Molecular subtype stratification for prostate cancer from mpMRI and histopathology images using convolutional neural networks and transfer learning

Abstract

Abstract Objective: Molecular subtypes have been found to be associated with prognosis and treatment response for prostate cancer (PCa) patients and have the potential to aid personalized treatment planning. We aim to stratify molecular subtypes by lesion characterization from multiparametric magnetic resonance images (mp-MRI) using convolutional neural networks (CNN) and knowledge transferred from lesion malignancy classification task. Methods: We identified 23 PCa patients with available mpMRI and molecular subtype information. Each patient may harbor multi-focal lesions of different molecular subtypes. Automated antibody based dual-color immunohistochemistry assays were developed for the simultaneous assessment of ERG-PTEN and ERG-SPINK1 status in PCa on the whole mount microscopic sections. In 7 patients 11 intraprostatic lesions (ILs) were identified as ERG+, 3 patients 3 ILs as SPINK1+, and 10 patients 11 ILs as triple negative (ERG-, SPINK1- and ETS-). We fed a CNN using intratumoral region of interest defined in a slice-by-slice manner from T2WI, ADC and DWIb50. The feature maps from the third convolutional layer were flattened into 7,744 dimensional vectors to represent each IL slice. To compensate for the small sample size, we utilized transfer learning, from the task of tumor malignancy stratification. The CNN model was pre-trained on mpMRI with 320 ILs from 201 patients for malignancy stratification using a different cohort we published previously, which was later fined tuned on this cohort on malignancy classification for domain adaptation purpose. Results: The clustering accuracy (Top 3 estimates) using cosine similarity metrics was shown in Table 1 for each molecular subtype category respectively. The preliminary results supported our hypothesis that task of lesion malignancy and molecular sub-types stratification were correlated in the imaging features derived from mpMRI. Table 1:Clustering accuracy for each molecular category. Each ILs input had 3 consecutive slices in a sequence, know as 2.5D, to incorporate lesion growth pattern.Accuracy (# of 2.5D image slices)Top 1 predictionsTop 2 predictionsTop 3 predictionsERG+ (21)0.670.900.95SPINK1+ (4)0.50.750.75Negative (17)0.650.760.76 Conclusions: This work showed the potential to classify the molecular subtypes of PCa from mp-MRI. The small sample size problem was tackled using transfer learning. Acknowledgement: The work was supported by a Research Scholar Grant: RSG-15-137-01-CCE from the American Cancer Society. Citation Format: Weiwei Zong, Eric N. Carver, Aharon Feldman, Nallasivam Palanisamy, Ning Wen. Molecular subtype stratification for prostate cancer from mpMRI and histopathology images using convolutional neural networks and transfer learning [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5302.