Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-Support Models
Authors Liu H, Tang K, Peng E, Wang L, Xia D, Chen Z
Received 13 October 2020
Accepted for publication 25 November 2020
Published 22 December 2020 Volume 2020:12 Pages 13099—13110
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Eileen O'Reilly
Hailang Liu,1 Kun Tang,1 Ejun Peng,1 Liang Wang,2 Ding Xia,1 Zhiqiang Chen1
1Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, People’s Republic of China; 2Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, People’s Republic of China
Correspondence: Ding Xia; Zhiqiang Chen
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan 430030, Hubei, People’s Republic of China
Email email@example.com; firstname.lastname@example.org
Objective: This study aimed to develop a machine learning (ML)-assisted model capable of accurately predicting the probability of biopsy Gleason grade group upgrading before making treatment decisions.
Methods: We retrospectively collected data from prostate cancer (PCa) patients. Four ML-assisted models were developed from 16 clinical features using logistic regression (LR), logistic regression optimized by least absolute shrinkage and selection operator (Lasso) regularization (Lasso-LR), random forest (RF), and support vector machine (SVM). The area under the curve (AUC) was applied to determine the model with the highest discrimination. Calibration plots and decision curve analysis (DCA) were performed to evaluate the calibration and clinical usefulness of each model.
Results: A total of 530 PCa patients were included in this study. The Lasso-LR model showed good discrimination with an AUC, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 0.776, 0.712, 0.679, 0.745, 0.730, and 0.695, respectively, followed by SVM (AUC=0.740, 95% confidence interval [CI]=0.690– 0.790), LR (AUC=0.725, 95% CI=0.674– 0.776) and RF (AUC=0.666, 95% CI=0.618– 0.714). Validation of the model showed that the Lasso-LR model had the best discriminative power (AUC=0.735, 95% CI=0.656– 0.813), followed by SVM (AUC=0.723, 95% CI=0.644– 0.802), LR (AUC=0.697, 95% CI=0.615– 0.778) and RF (AUC=0.607, 95% CI=0.531– 0.684) in the testing dataset. Both the Lasso-LR and SVM models were well-calibrated. DCA plots demonstrated that the predictive models except RF were clinically useful.
Conclusion: The Lasso-LR model had good discrimination in the prediction of patients at high risk of harboring incorrect Gleason grade group assignment, and the use of this model may be greatly beneficial to urologists in treatment planning, patient selection, and the decision-making process for PCa patients.
Keywords: prostate cancer, biopsy cores, Gleason grade group, upgrading, machine learning
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