When diagnosing and reporting lung adenocarcinoma (LAC), pathologists currently include an assessment of histologic tumor growth patterns because the predominant growth pattern has been reported to impact prognosis. However, the subjective nature of manual slide evaluation contributes to suboptimal inter-pathologist variability in tumor growth pattern assessment. We applied a deep learning approach to identify and automatically delineate areas of four tumor growth patterns (solid, acinar, micropapillary, and cribriform) and non-tumor areas in whole slide images (WSI) from resected LAC specimens. We trained a DenseNet model using patches from 109 slides collected at two institutions. The model was tested using 56 WSIs including 20 that were collected at a third institution. Using the same slide set, the concordance between the DenseNet model and an experienced pathologist (blinded to the DenseNet results) in determining the predominant tumor growth pattern was substantial (kappa score = 0.603). Using a subset of 36 test slides that were manually annotated for tumor growth patterns, we also measured the F1-score for each growth pattern: 0.95 (solid), 0.78 (acinar), 0.76 (micropapillary), 0.28 (cribriform) and 0.97 (non-tumor). Our results suggest that DenseNet assessment of WSIs with solid, acinar, and micropapillary predominant tumor growth is more robust than for the WSIs with predominant cribriform growth which are less frequently encountered.
Diffuse large B-cell lymphoma (DLBCL) is the most common type of B-cell lymphoma. It is characterized by a heterogeneous morphology, genetic changes and clinical behavior. A small specific subgroup of DLBCL, harbouring a MYC gene translocation is associated with worse patient prognosis and outcome. Typically, the MYC translocation is assessed with a molecular test (FISH), that is expensive and time-consuming. Our hypothesis is that genetic changes, such as translocations could be visible as changes in the morphology of an HE-stained specimen. However, it has not proven possible to use morphological criteria for the detection of a MYC translocation in the diagnostic setting due to lack of specificity.
In this paper, we apply a deep learning model to automate detection of the MYC translocations in DLBCL based on HE-stained specimens. The proposed method works at the whole-slide level and was developed based on a multicenter data cohort of 91 patients. All specimens were stained with HE, and the MYC translocation was confirmed using fluorescence in situ hybridization (FISH). The system was evaluated on an additional 66 patients, and obtained AUROC of 0.83 and accuracy of 0.77. The proposed method presents proof of a concept giving insights in the applicability of deep learning methods for detection of a genetic changes in DLBCL. In future work we will evaluate our algorithm for automatic pre-screen of DLBCL specimens to obviate FISH analysis in a large number of patients.