Mamidi, Y.K.K.; Mamidi, T.K.K.; Kabir, M.W.U.; Wu, J.; Hoque, M.T.; Hicks, C. PCa-Clf: A Classifier of Prostate Cancer Patients into Patients with Indolent and Aggressive Tumors Using Machine Learning. Mach. Learn. Knowl. Extr.2023, 5, 1302-1319.
Mamidi, Y.K.K.; Mamidi, T.K.K.; Kabir, M.W.U.; Wu, J.; Hoque, M.T.; Hicks, C. PCa-Clf: A Classifier of Prostate Cancer Patients into Patients with Indolent and Aggressive Tumors Using Machine Learning. Mach. Learn. Knowl. Extr. 2023, 5, 1302-1319.
Mamidi, Y.K.K.; Mamidi, T.K.K.; Kabir, M.W.U.; Wu, J.; Hoque, M.T.; Hicks, C. PCa-Clf: A Classifier of Prostate Cancer Patients into Patients with Indolent and Aggressive Tumors Using Machine Learning. Mach. Learn. Knowl. Extr.2023, 5, 1302-1319.
Mamidi, Y.K.K.; Mamidi, T.K.K.; Kabir, M.W.U.; Wu, J.; Hoque, M.T.; Hicks, C. PCa-Clf: A Classifier of Prostate Cancer Patients into Patients with Indolent and Aggressive Tumors Using Machine Learning. Mach. Learn. Knowl. Extr. 2023, 5, 1302-1319.
Abstract
Accurately distinguishing between indolent and aggressive tumors is a crucial unmet need in the clinical management of prostate cancer (PCa). The traditional Gleason grading system has been utilized for this purpose; however, there is often ambiguity in classifying tumors with a Gleason grade of 7. Clinicians commonly resort to using secondary Gleason grades, such as 3+4 or 4+3, to classify these tumors as indolent or aggressive, respectively. Unfortunately, such classifications are prone to misinterpretation, leading to erroneous diagnoses and prognoses. To address this challenge, we investigated the application of Machine Learning (ML) techniques to classify PCa patients based on gene expression data sourced from The Cancer Genome Atlas. By comparing gene expression levels between indolent and aggressive tumors, we sought to identify distinctive features for developing and validating a range of ML algorithms and stacking techniques. The stacking based model achieved an impressive accuracy of 96% for all samples encompassing primary Gleason grades 6 to 10. Notably, when excluding Gleason grade 7 from the analysis, the accuracy further improved to 97%. This study underscores the effectiveness of the stacked ML algorithm for accurately classifying indolent versus aggressive PCa. Leveraging gene expression data and employing a combination of classifiers, this approach offers a powerful solution to address the unmet need in robustly distinguishing between different types of PCa tumors. Future implementation of this methodology may significantly impact clinical decision-making and patient outcomes in the management of prostate cancer.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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