P1164: IMAGE-BASED DETECTION OF HIGH-GRADE B CELL LYMPHOMAS (DH-L)

Abstract

Background: Aggressive B cell Non-Hodgkin Lymphomas (B-NHL) are divided into two main categories: diffuse large B cell lymphoma (DLBCL) accounting for 90% of cases, and high-grade B-cell lymphoma (HGBL). Diagnosis of high-grade lymphoma with MYC and BCL2 and/or BCL6 rearrangements (double-hit lymphoma-DHL) is confirmed by fluorescence in situ hybridization (FISH) analysis, demonstrating c-MYC-rearrangement in combination with BCL-2 / BCL-6 rearrangement in lymphoma cells. Accurate and rapid diagnosis of DHL is obligatory, when considering more aggressive treatment regimens (other than R-CHOP), suggested in these patients. Aims: To establish a novel tool for diagnosing DLBCL and DHL, directly on the scanned Hematoxylin and Eosin (H&E) biopsy slides, by applying digital imaging technologies supported by machine learning algorithms. Methods: H&E whole slide images, prepared from biopsies obtained mainly from lymph nodes, but also from extra-nodal organs of patients with aggressive B cell lymphoma histology, were collected from the pathology department at Tel-Aviv Medical center (TASMC). Cases were randomly divided into a training (n=43) and a validation (n=35) set. On-the-fly augmentation was applied to images, together with advanced Convolutional Neural Network (CNN) analysis, to generate the aggressive B-NHL classifier (powered by Imagene-AI). Proprietary multiple instance learning (MIL) algorithms and training scheme, based on cases ranking, rather than absolute values, were applied. The classifier was validated through a testing set in a blinded study scheme, and results were compared to the FISH results for c-MYC and BCL2/6 rearrangements (Figure 1A). Results: The classifier training set was composed of 36 DLBCL NOS and 7 DHL cases. The aggressive B-NHL classifier was validated on a cohort of 35 cases, including 14 DHL cases (diagnosed by FISH) and 21 DLBCL cases. The model demonstrated 100% sensitivity and 90.48% specificity, with an accuracy rate of 94.29% and Area Under the Curve (AUC) of 0.99 (Figure 1B). Two cases were concluded as false positive, including one case that demonstrated rearrangements in substantial percentages of cells, just below the required positivity threshold. Image:Summary/Conclusion: Herein, we demonstrate for the first time a machine learning-based genomic testing solution for the detection and classification of aggressive B-NHL, based on H&E stained slide images. Implementation of this solution in clinical practice can support the diagnosis of DHL, which currently has both technical and financial challenges. Applying the proposed accessible and standardized AI-based method, can improve and facilitate the diagnosis of DHL, thereby directly improving patient care.