Deep Learning-Based Nuclear Morphometry Reveals an Independent Prognostic Factor in Mantle Cell Lymphoma.

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Abstract

Blastoid/pleomorphic morphology is associated with short survival in mantle cell lymphoma (MCL), but its prognostic value is overridden by proliferation index Ki-67 in multivariate analysis. Here we developed a nuclear segmentation model using deep learning, and nuclei of tumor cells in 103 MCL cases were automatically delineated. Eight nuclear morphometric attributes, including length, width, perimeter, area, length/width ratio, circularity, irregularity, and entropy, were extracted from each nucleus. The mean, variance, skewness, and kurtosis of each attribute were calculated for each case, resulting in 32 morphometric parameters. Compared to classic MCL, 17 morphometric parameters were significantly different in blastoid/pleomorphic MCL. Using univariate analysis, 16 morphometric parameters (including 14 significantly different between classic and blastoid/pleomorphic MCL) were significant prognostic factors. Using multivariate analysis, biologic MCL international prognostic index (bMIPI) risk group (P=0.025), low skewness of nuclear irregularity (P=0.020), and high mean of nuclear irregularity (P=0.047) were independent adverse prognostic factors. Furthermore, a morphometric score calculated from the skewness and mean of nuclear irregularity (P=0.0038) was an independent prognostic factor in addition to bMIPI risk group (P=0.025), and a summed morphometric bMIPI score was useful for risk stratification of MCL patients (P=0.000001). Our results demonstrate for the first time that a nuclear morphometric score is an independent prognostic factor in MCL. It is more robust than blastoid/pleomorphic morphology and can be objectively measured.Copyright © 2022. Published by Elsevier Inc.