TY - JOUR
T1 - Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies
AU - Irshaid, Lina
AU - Bleiberg, Jonathan
AU - Weinberger, Ethan
AU - Garritano, James
AU - Shallis, Rory M.
AU - Patsenker, Jonathan
AU - Lindenbaum, Ofir
AU - Kluger, Yuval
AU - Katz, Samuel G.
AU - Xu, Mina L.
N1 - Publisher Copyright:
© 2022 College of American Pathologists. All rights reserved.
PY - 2022/1/2
Y1 - 2022/1/2
N2 - Context.—Large cell transformation (LCT) of indolent B-cell lymphomas, such as follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), signals a worse prognosis, at which point aggressive chemotherapy is initiated. Although LCT is relatively straightforward to diagnose in lymph nodes, a marrow biopsy is often obtained first given its ease of procedure, low cost, and low morbidity. However, consensus criteria for LCT in bone marrow have not been established. Objective.—To study the accuracy and reproducibility of a trained convolutional neural network in identifying LCT, in light of promising machine learning tools that may introduce greater objectivity to morphologic analysis. Design.—We retrospectively identified patients who had a diagnosis of FL or CLL who had undergone bone marrow biopsy for the clinical question of LCT. We scored morphologic criteria and correlated results with clinical disease progression. In addition, whole slide scans were annotated into patches to train convolutional neural networks to discriminate between small and large tumor cells and to predict the patient’s probability of transformation. Results.—Using morphologic examination, the proportion of large lymphoma cells (≥10% in FL and Department of 30% in CLL), chromatin pattern, distinct nucleoli, and proliferation index were significantly correlated with LCT in FL and CLL. Compared to pathologist-derived estimates, machine-generated quantification demonstrated better reproducibility and stronger correlation with final outcome data. Conclusions.—These histologic findings may serve as indications of LCT in bone marrow biopsies. The pathologist—augmented with machine system appeared to be the most predictive, arguing for greater efforts to validate and implement these tools to further enhance physician practice.
AB - Context.—Large cell transformation (LCT) of indolent B-cell lymphomas, such as follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), signals a worse prognosis, at which point aggressive chemotherapy is initiated. Although LCT is relatively straightforward to diagnose in lymph nodes, a marrow biopsy is often obtained first given its ease of procedure, low cost, and low morbidity. However, consensus criteria for LCT in bone marrow have not been established. Objective.—To study the accuracy and reproducibility of a trained convolutional neural network in identifying LCT, in light of promising machine learning tools that may introduce greater objectivity to morphologic analysis. Design.—We retrospectively identified patients who had a diagnosis of FL or CLL who had undergone bone marrow biopsy for the clinical question of LCT. We scored morphologic criteria and correlated results with clinical disease progression. In addition, whole slide scans were annotated into patches to train convolutional neural networks to discriminate between small and large tumor cells and to predict the patient’s probability of transformation. Results.—Using morphologic examination, the proportion of large lymphoma cells (≥10% in FL and Department of 30% in CLL), chromatin pattern, distinct nucleoli, and proliferation index were significantly correlated with LCT in FL and CLL. Compared to pathologist-derived estimates, machine-generated quantification demonstrated better reproducibility and stronger correlation with final outcome data. Conclusions.—These histologic findings may serve as indications of LCT in bone marrow biopsies. The pathologist—augmented with machine system appeared to be the most predictive, arguing for greater efforts to validate and implement these tools to further enhance physician practice.
UR - http://www.scopus.com/inward/record.url?scp=85117844521&partnerID=8YFLogxK
U2 - 10.5858/ARPA.2020-0510-OA
DO - 10.5858/ARPA.2020-0510-OA
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C2 - 34086849
AN - SCOPUS:85117844521
SN - 0003-9985
VL - 146
SP - 182
EP - 193
JO - Archives of Pathology and Laboratory Medicine
JF - Archives of Pathology and Laboratory Medicine
IS - 2
ER -