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

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 (MEIT...

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Autores principales:	Wei-Hsiang Yu, Chih-Hao Li, Ren-Ching Wang, Chao-Yuan Yeh, Shih-Sung Chuang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence digital pathology quantitative morphology primary intestinal T-cell lymphoma convolutional neural network human-interpretable AI Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/d780586919ad4d8ca7742f4a7cb443e0
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spelling	oai:doaj.org-article:d780586919ad4d8ca7742f4a7cb443e02021-11-11T15:33:32ZMachine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas10.3390/cancers132154632072-6694https://doaj.org/article/d780586919ad4d8ca7742f4a7cb443e02021-10-01T00:00:00Zhttps://www.mdpi.com/2072-6694/13/21/5463https://doaj.org/toc/2072-6694The 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.Wei-Hsiang YuChih-Hao LiRen-Ching WangChao-Yuan YehShih-Sung ChuangMDPI AGarticleartificial intelligencedigital pathologyquantitative morphologyprimary intestinal T-cell lymphomaconvolutional neural networkhuman-interpretable AINeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancers, Vol 13, Iss 5463, p 5463 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence digital pathology quantitative morphology primary intestinal T-cell lymphoma convolutional neural network human-interpretable AI Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	artificial intelligence digital pathology quantitative morphology primary intestinal T-cell lymphoma convolutional neural network human-interpretable AI Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Wei-Hsiang Yu Chih-Hao Li Ren-Ching Wang Chao-Yuan Yeh Shih-Sung Chuang Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas
description	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.
format	article
author	Wei-Hsiang Yu Chih-Hao Li Ren-Ching Wang Chao-Yuan Yeh Shih-Sung Chuang
author_facet	Wei-Hsiang Yu Chih-Hao Li Ren-Ching Wang Chao-Yuan Yeh Shih-Sung Chuang
author_sort	Wei-Hsiang Yu
title	Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas
title_short	Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas
title_full	Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas
title_fullStr	Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas
title_full_unstemmed	Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas
title_sort	machine learning based on morphological features enables classification of primary intestinal t-cell lymphomas
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d780586919ad4d8ca7742f4a7cb443e0
work_keys_str_mv	AT weihsiangyu machinelearningbasedonmorphologicalfeaturesenablesclassificationofprimaryintestinaltcelllymphomas AT chihhaoli machinelearningbasedonmorphologicalfeaturesenablesclassificationofprimaryintestinaltcelllymphomas AT renchingwang machinelearningbasedonmorphologicalfeaturesenablesclassificationofprimaryintestinaltcelllymphomas AT chaoyuanyeh machinelearningbasedonmorphologicalfeaturesenablesclassificationofprimaryintestinaltcelllymphomas AT shihsungchuang machinelearningbasedonmorphologicalfeaturesenablesclassificationofprimaryintestinaltcelllymphomas
_version_	1718435212515344384

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

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