Hailang Liu1, Kun Tang1, Ejun Peng1, Liang Wang2, Ding Xia1, Zhiqiang Chen1. 1. Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, People's Republic of China. 2. Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, People's Republic of China.
Abstract
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.
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.
Authors: Christian Thomas; Karin Pfirrmann; Frauke Pieles; Alexander Bogumil; Rolf Gillitzer; Christoph Wiesner; Joachim W Thüroff; Sebastian W Melchior Journal: BJU Int Date: 2011-05-18 Impact factor: 5.588
Authors: Freddie C Hamdy; Jenny L Donovan; J Athene Lane; Malcolm Mason; Chris Metcalfe; Peter Holding; Michael Davis; Tim J Peters; Emma L Turner; Richard M Martin; Jon Oxley; Mary Robinson; John Staffurth; Eleanor Walsh; Prasad Bollina; James Catto; Andrew Doble; Alan Doherty; David Gillatt; Roger Kockelbergh; Howard Kynaston; Alan Paul; Philip Powell; Stephen Prescott; Derek J Rosario; Edward Rowe; David E Neal Journal: N Engl J Med Date: 2016-09-14 Impact factor: 91.245
Authors: Giorgio Gandaglia; Guillaume Ploussard; Massimo Valerio; Agostino Mattei; Cristian Fiori; Mathieu Roumiguié; Nicola Fossati; Armando Stabile; Jean-Baptiste Beauval; Bernard Malavaud; Simone Scuderi; Francesco Barletta; Marco Moschini; Stefania Zamboni; Arnas Rakauskas; Zhe Tian; Pierre I Karakiewicz; Francesco De Cobelli; Francesco Porpiglia; Francesco Montorsi; Alberto Briganti Journal: Eur Urol Date: 2019-09-21 Impact factor: 20.096
Authors: Matteo Ferro; Giuseppe Lucarelli; Dario Bruzzese; Giuseppe Di Lorenzo; Sisto Perdonà; Riccardo Autorino; Francesco Cantiello; Roberto La Rocca; Gian Maria Busetto; Amelia Cimmino; Carlo Buonerba; Michele Battaglia; Rocco Damiano; Ottavio De Cobelli; Vincenzo Mirone; Daniela Terracciano Journal: Oncotarget Date: 2017-03-14
Authors: Muammer Altok; Patricia Troncoso; Mary F Achim; Surena F Matin; Graciela N Gonzalez; John W Davis Journal: Asian J Androl Date: 2019 Nov-Dec Impact factor: 3.285