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Article

Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering

1
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
2
Department of Pathology, University of Rochester Medical Center, Rochester, NY 14642, USA
3
Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
4
Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
5
Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78705, USA
6
Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
7
Department of Technology and Digital Office, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Gianpietro Semenzato
Cancers 2022, 14(10), 2398; https://doi.org/10.3390/cancers14102398
Received: 22 March 2022 / Revised: 21 April 2022 / Accepted: 25 April 2022 / Published: 13 May 2022
Distinguishing between chronic lymphocytic leukemia (CLL), accelerated CLL (aCLL), and full-blown transformation to diffuse large B-cell lymphoma (Richter transformation; RT) has significant clinical implications. Identifying cellular phenotypes via unsupervised clustering provides the most robust analytic performance in analyzing digitized pathology slides. This study serves as a proof of concept that using an unsupervised machine learning scheme can enhance diagnostic accuracy.
Identifying the progression of chronic lymphocytic leukemia (CLL) to accelerated CLL (aCLL) or transformation to diffuse large B-cell lymphoma (Richter transformation; RT) has significant clinical implications as it prompts a major change in patient management. However, the differentiation between these disease phases may be challenging in routine practice. Unsupervised learning has gained increased attention because of its substantial potential in data intrinsic pattern discovery. Here, we demonstrate that cellular feature engineering, identifying cellular phenotypes via unsupervised clustering, provides the most robust analytic performance in analyzing digitized pathology slides (accuracy = 0.925, AUC = 0.978) when compared to alternative approaches, such as mixed features, supervised features, unsupervised/mixed/supervised feature fusion and selection, as well as patch-based convolutional neural network (CNN) feature extraction. We further validate the reproducibility and robustness of unsupervised feature extraction via stability and repeated splitting analysis, supporting its utility as a diagnostic aid in identifying CLL patients with histologic evidence of disease progression. The outcome of this study serves as proof of principle using an unsupervised machine learning scheme to enhance the diagnostic accuracy of the heterogeneous histology patterns that pathologists might not easily see. View Full-Text
Keywords: chronic lymphocytic leukemia (CLL); accelerated CLL; Richter transformation (RT); large cell transformation; disease progression; cellular feature engineering; unsupervised clustering; feature fusion; feature selection chronic lymphocytic leukemia (CLL); accelerated CLL; Richter transformation (RT); large cell transformation; disease progression; cellular feature engineering; unsupervised clustering; feature fusion; feature selection
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MDPI and ACS Style

Chen, P.; El Hussein, S.; Xing, F.; Aminu, M.; Kannapiran, A.; Hazle, J.D.; Medeiros, L.J.; Wistuba, I.I.; Jaffray, D.; Khoury, J.D.; Wu, J. Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering. Cancers 2022, 14, 2398. https://doi.org/10.3390/cancers14102398

AMA Style

Chen P, El Hussein S, Xing F, Aminu M, Kannapiran A, Hazle JD, Medeiros LJ, Wistuba II, Jaffray D, Khoury JD, Wu J. Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering. Cancers. 2022; 14(10):2398. https://doi.org/10.3390/cancers14102398

Chicago/Turabian Style

Chen, Pingjun, Siba El Hussein, Fuyong Xing, Muhammad Aminu, Aparajith Kannapiran, John D. Hazle, L. Jeffrey Medeiros, Ignacio I. Wistuba, David Jaffray, Joseph D. Khoury, and Jia Wu. 2022. "Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering" Cancers 14, no. 10: 2398. https://doi.org/10.3390/cancers14102398

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