Abstract
Simple SummaryWe presented a machine learning approach for accurate quantification of nuclear morphometrics and differential diagnosis of primary intestinal T-cell lymphomas. The human interpretable machine learning approach can be easily applied to other lymphomas and potentially even broader disease categories. This approach not only brings deeper insights into lymphoma phenotypes, but also paves the way for future discoveries concerning their relationship with disease classification and outcome.The aim of this study was to investigate the feasibility of using machine learning techniques based on morphological features in classifying two subtypes of primary intestinal T-cell lymphomas (PITLs) defined according to the WHO criteria: monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL) versus intestinal T-cell lymphoma, not otherwise specified (ITCL-NOS), which is considered a major challenge for pathological diagnosis. A total of 40 histopathological whole-slide images (WSIs) from 40 surgically resected PITL cases were used as the dataset for model training and testing. A deep neural network was trained to detect and segment the nuclei of lymphocytes. Quantitative nuclear morphometrics were further computed from these predicted contours. A decision-tree-based machine learning algorithm, XGBoost, was then trained to classify PITL cases into two disease subtypes using these nuclear morphometric features. The deep neural network achieved an average precision of 0.881 in the cell segmentation work. In terms of classifying MEITL versus ITCL-NOS, the XGBoost model achieved an area under receiver operating characteristic curve (AUC) of 0.966. Our research demonstrated an accurate, human-interpretable approach to using machine learning algorithms for reducing the high dimensionality of image features and classifying T cell lymphomas that present challenges in morphologic diagnosis. The quantitative nuclear morphometric features may lead to further discoveries concerning the relationship between cellular phenotype and disease status.
Highlights
Primary intestinal T-cell lymphomas (PITLs) are rare and pose a challenge for diagnosis
Due to the inconsistency between phenotypic and morphological features of primary intestinal T-cell lymphomas (PITLs), it might not be easy to make a straightforward distinction between monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL) and ITCL-NOS in some cases
Our results demonstrate that: (1) deep learning algorithms could accurately segment nuclear boundaries, and the morphometric features could be quantified; (2) there were significant differences in quantitative morphological features between the neoplastic cells of MEITL and ITCL-NOS; (3) The XGBoost model based on the extracted morphological profile showed a superior performance to convolutional neural network (CNN) applied directly to images in classifying
Summary
Primary intestinal T-cell lymphomas (PITLs) are rare and pose a challenge for diagnosis. MEITL, previously called type II EATL, is characterized by monomorphic small- to medium-sized tumor cells with an epitheliotropic growth pattern and the expression of CD8, CD56, and cytotoxic markers [2,3,4]. There are cases showing a METIL immunophenotype but with a more pleomorphic nuclear feature or cases showing a MEITL cellular morphology but with an atypical immunophenotype. These cases with an inconsistency between the immunophenotype and the cellular morphology patterns were regarded as borderline in the study. A major challenge for pathological diagnosis is the distinction between MEITL and ITCL-NOS, as the major diagnostic criterion relies on morphological evaluation
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