PCa-Clf:利用机器学习将前列腺癌患者分为惰性和侵袭性肿瘤

IF 4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Machine learning and knowledge extraction Pub Date : 2023-09-27 DOI:10.3390/make5040066
Yashwanth Karthik Kumar Mamidi, Tarun Karthik Kumar Mamidi, Md Wasi Ul Kabir, Jiande Wu, Md Tamjidul Hoque, Chindo Hicks
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引用次数: 0

摘要

在前列腺癌(PCa)临床管理中,一个关键的未满足的医疗需求集中在区分惰性肿瘤和侵袭性肿瘤上。传统上,格里森分级已被用于这一目的。然而,使用Gleason分级7对肿瘤进行分类往往是不明确的,因为这些肿瘤的临床行为遵循不同的临床过程。本研究旨在探讨机器学习技术(ML)在将患者分为惰性和侵袭性前列腺癌中的应用。我们使用来自癌症基因组图谱的基因表达数据,比较了惰性肿瘤和侵袭性肿瘤的基因表达水平,以确定开发和验证一系列ML和堆叠算法的特征。ML算法可以准确区分惰性和侵袭性pca。当使用所有初级Gleason等级为6至10的样本时,堆叠模型的准确率为96%,优于单个ML算法。排除Gleason Grade 7的样本将准确率提高到97%。本研究表明,机器学习算法和叠加模型是准确分类惰性和侵袭性pca的有效方法。该方法学的未来实施可能会显著影响前列腺癌临床管理的临床决策和患者预后。
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PCa-Clf: A Classifier of Prostate Cancer Patients into Patients with Indolent and Aggressive Tumors Using Machine Learning
A critical unmet medical need in prostate cancer (PCa) clinical management centers around distinguishing indolent from aggressive tumors. Traditionally, Gleason grading has been utilized for this purpose. However, tumor classification using Gleason Grade 7 is often ambiguous, as the clinical behavior of these tumors follows a variable clinical course. This study aimed to investigate the application of machine learning techniques (ML) to classify patients into indolent and aggressive PCas. We used gene expression data from The Cancer Genome Atlas and compared gene expression levels between indolent and aggressive tumors to identify features for developing and validating a range of ML and stacking algorithms. ML algorithms accurately distinguished indolent from aggressive PCas. With the accuracy of 96%, the stacking model was superior to individual ML algorithms when all samples with primary Gleason Grades 6 to 10 were used. Excluding samples with Gleason Grade 7 improved accuracy to 97%. This study shows that ML algorithms and stacking models are powerful approaches for the accurate classification of indolent versus aggressive PCas. Future implementation of this methodology may significantly impact clinical decision making and patient outcomes in the clinical management of prostate cancer.
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CiteScore
6.30
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审稿时长
7 weeks
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