Persistent Discordance in Grade, Stage, and NCCN Risk Stratification in Men Undergoing Targeted Biopsy and Radical Prostatectomy

Alshak, Mark N.; Patel, Neal; Groß, Michael; Margolis, Daniel; Hu, Jim C.

doi:10.1016/j.urology.2019.07.049

Cited by 24 publications

(28 citation statements)

References 29 publications

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“…%fPSA, apical involvement at MRI, biopsy grade group, and clinical T stage at MRI were significantly associated with upgrading in Lasso-LR and SVM models. However, in a comparable study, Alshak et al 15 demonstrated that only the PI-RADS score was a significant predictor of upgrading. Besides, Gandaglia et al 26 reported that preoperative PSA level, GG at MRI-targeted biopsy, and clinically significant PCa at systematic biopsy were independent risk factors of upgrading at RP.…”

Section: Discussionmentioning

confidence: 94%

Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-Support Models

Liu¹,

Tang²,

Peng³

et al. 2020

CMAR

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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.

show abstract

Section: Discussionmentioning

confidence: 94%

Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-Support Models

Liu¹,

Tang²,

Peng³

et al. 2020

CMAR

View full text Add to dashboard Cite

show abstract

“…%fPSA, apical involvement at MRI, biopsy grade group and clinical T stage at MRI were signi cantly associated with upgrading in Lasso-LR and SVM model. However, in a comparable study, Alshak et al [22] demonstrated that only the PI-RADS score was a signi cant predictor of upgrading. Besides, Gandaglia et al [33] reported that preoperative PSA level, GG at MRI-targeted biopsy and clinically signi cant PCa at systematic biopsy were independent risk factors of upgrading at RP.…”

Section: Discussionmentioning

confidence: 93%

Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-support Models

Tang

Peng

Wang

et al. 2020

Preprint

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Objective: To develop a machine learning (ML)-assisted model capable of accurately predicting the probability of biopsy Gleason grade group upgrading before making treatment decisions by integrating multiple clinical characteristics.Materials and Methods: We retrospectively collected data from PCa (prostate cancer) patients who underwent systematic biopsy and radical prostatectomy from January 2015 to December 2019 at Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology. The study cohort was divided into training and testing datasets in a 70:30 ratio for further analysis. Four ML-assisted models were developed from 16 clinical features using logistic regression (LR), logistic regression optimized by 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 were used to investigate the extent of over- or underestimation of predicted probabilities relative to the observed probabilities in models. Results: In total, 530 PCa patients were included, with 371 patients in the training dataset and 159 patients in the testing dataset. 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%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. Conclusions: 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.

show abstract

Section: Discussionmentioning

confidence: 93%

Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-assisted Decision-support Models 

Liu

Tang

Peng

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: 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 who underwent systematic biopsy and radical prostatectomy from January 2015 to December 2019 at Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology. The study cohort was divided into training and testing datasets in a 70:30 ratio for further analysis. 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 were used to investigate the extent of over- or underestimation of predicted probabilities relative to the observed probabilities in models. Results: In total, 530 PCa patients were included, with 371 patients in the training dataset and 159 patients in the testing dataset. 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. 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.

show abstract

Persistent Discordance in Grade, Stage, and NCCN Risk Stratification in Men Undergoing Targeted Biopsy and Radical Prostatectomy

Cited by 24 publications

References 29 publications

Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-Support Models

Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-Support Models

Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-support Models

Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-assisted Decision-support Models

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Persistent Discordance in Grade, Stage, and NCCN Risk Stratification in Men Undergoing Targeted Biopsy and Radical Prostatectomy

Cited by 24 publications

References 29 publications

Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-Support Models

Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-Support Models

Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-support Models

Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-assisted Decision-support Models&nbsp;

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Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-assisted Decision-support Models