A multi-modal deep learning model integrates clinical, pathomic, and radiomic features for glioma classification and grading.

Abstract

e14038 Background: Histopathologic glioma diagnosis relies on the interpretation of the morphologic appearance of tumor cells. This conventional approach suffers from documented inter-pathologist disagreements with minor discordances in 36% and serious discordances in 8.8% of cases (Bruner JM et al. Cancer. 1997). Machine learning applied to automatically extracted pathomic features offers the potential to reduce this inter-reader variability and improve classification accuracy. The addition of clinical history and radiomic features may further improve performance. This work compares the performance of automatic pathomic and radiomic feature analysis to human histologic impressions for glioma classification according to the 2021 WHO scheme. Methods: We developed and evaluated models for predicting the 2021 WHO Glioma class using data from The Cancer Genome Atlas and The Cancer Imaging Archive. We used a cohort of 112 glioma cases with complete data of molecular markers, digital slides, and MRI images. Using molecular markers, we divided this cohort into 2021 WHO types, including 61 glioblastomas, 38 astrocytomas, and 13 oligodendroglioma. We extracted 374 pathomic features summarizing the tumor-rich region on each digital slide. Neural network models were developed with pathomic features alone or in combination with clinical factors (sex, age, and Karnofsky index) and 670 radiomic features obtained from multi-sequence MRI exams. Accuracy was estimated with 5-fold cross validation. Feature importance analysis was carried out with recursive feature elimination to identify the top pathomic and radiomic features related to the specific glioma diagnosis. Results: In this cohort, the conventional histologic impression, reliant on pathologist interpretation, was successful in identifying the WHO glioma class with an accuracy of 0.77 +/- 0.08. The use of pathomics features alone, representing a fully automatic approach, had an accuracy of 0.83 +/- 0.08. The use of pathomics and radiomics information increased the accuracy to 0.88 +/- 0.08. Feature importance analysis revealed that the most important pathomic features were related to nuclear size, nuclear hematoxylin staining intensity, and nuclear circularity. Conclusions: In this pilot study, our machine learning model with pathomic features achieved promising performance for glioma classification. Combining clinical and radiomic features further enhanced performance. We found that the pathomic features that most correlated with the pathologic classification are the same key features that pathologists use in their diagnostic evaluation. While molecular testing is now the definitive reference information for final glioma classification, this pilot study suggests that pathomics analysis could have a valuable role during a pathologist’s initial histologic review.