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

Yu, Wei-Hsiang; Li, Chih‐Hao; Wang, Ren‐Ching; Yeh, Chao‐Yuan; Chuang, Shih‐Sung

doi:10.3390/cancers13215463

Cited by 12 publications

(12 citation statements)

References 31 publications

(36 reference statements)

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“…Our work represents the first pixel-level assessment of the size and shape differences between DLBCL and non-DLBCL. Prior studies that quantitatively assessed interpretable morphologic features in lymphoma diagnosis have used a smaller number of cases and fewer diagnostic categories (Gupta et al 2010; Lesty et al 1986; W.-H. Yu et al 2021).…”

Section: Discussionmentioning

confidence: 99%

LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype

Shankar

Yang

Krishna

et al. 2023

Preprint

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Lymphomas vary in terms of clinical behavior, morphology, and response to therapies and thus accurate classification is essential for appropriate management of patients. In this study, using a set of 670 cases of lymphoma obtained from a center in Guatemala City, we propose an interpretable machine learning method, LymphoML, for lymphoma subtyping into eight diagnostic categories. LymphoML sequentially applies steps of (1) object segmentation to extract nuclei, cells, and cytoplasm from H&E-stained TMA cores, (2) feature extraction of morphological, textural, and architectural features, and (3) aggregation of per-object features to create patch-level feature vectors for lymphoma classification. LymphoML achieves a diagnostic accuracy of 64.3% (95% CI: [55.8%, 72.9%]) among 8 lymphoma subtypes using only tissue microarray core sections stained with hematoxylin and eosin (H&E), at a level similar to experienced hematopathologists. We find that the best model's set of nuclear and cytoplasmic morphological, textural, and architectural features are most discriminative for diffuse large B-cell lymphoma (F1 score: 78.7%), classic Hodgkin lymphoma (F1 score: 74.5%), and mantle cell lymphoma (F1 score: 71.0%). We find that nuclear shape features provide the highest diagnostic yield, with nuclear texture, cytoplasmic, and architectural features providing smaller gains in accuracy. Finally, we find that combining information from the H&E-based model together with the results of a limited set of immunohistochemical (IHC) stains resulted in a similar diagnostic accuracy (85.3% ([79.1%, 90.7%])) as with a much larger set of IHC stains (86.1% ([79.8%, 91.5%])). Our work suggests a potential way to incorporate machine learning tools into clinical practice to reduce the number of expensive IHC stains while achieving a similar level of diagnostic accuracy.

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Section: Discussionmentioning

confidence: 99%

LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype

Shankar

Yang

Krishna

et al. 2023

Preprint

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“…Recently, it has been shown that morphometric parameters can be used to classify primary intestinal T-cell lymphoma. 23 Here, we analyzed the disease prognosis based on objective morphometric parameters extracted from tens of thousands of nuclei among 103 MCL patients to derive a significant and human-interpretable result.…”

Section: Discussionmentioning

confidence: 99%

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

Chuang

Yu²,

Lee³

et al. 2022

The American Journal of Pathology

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“…Yu et al collected 40 histological WSIs for dataset training in a DNN. They succeeded in extracting picture characteristics and categorizing T-cell lymphomas, such as intestinal T-cell lymphoma, which pose difficulties in morphologic diagnosis [ 46 ].…”

Section: Current Applications Of Ai In Hematologic Cytologymentioning

confidence: 99%

Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders

Gedefaw

Liu

et al. 2023

Cells

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Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.

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

Cited by 12 publications

References 31 publications

LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype

LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype

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

Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders

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