Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas

Cancers

Ren-Ching Wang¹  Chih-Hao Li²  Shih-Sung Chuang³  Wei-Hsiang Yu⁴  Chao-Yuan Yeh⁴ 

[1] Department of Pathology and Laboratory Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan;Department of Pathology, Chang Gung Memorial Hospital, Kaohsiung 83301, Taiwan;Department of Pathology, Chi-Mei Medical Center, Tainan 71004, Taiwan;aetherAI, Co., Ltd., Taipei 115, Taiwan;

关键词: artificial intelligence;    digital pathology;    quantitative morphology;    primary intestinal T-cell lymphoma;    convolutional neural network;    human-interpretable AI;   

DOI  :  10.3390/cancers13215463

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown