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- W3123862014 abstract "Abstract Objective : To develope a generalizable machine learning platform, designated PI-Risk, which incorporates clinicians’ prior identifications with deep transferrable imaging feature representations into predictive models for PCa Gleason grade. Patients and Methods: A retrospective study included 1442 biopsy-naïve patients from two tertiary care medical centers between January 2014 and December 2019. We investigated an interpretable risk assessment model (PI-Risk) to predict risk stratification of PCa using . The performance of PI-Risk model was independently tested on 232 internal test datasets, and on 539 external validation datasets. Model performance was typically evaluated against a “ground truth” with imaging-histopathologic annotations using receiver operating characteristic (ROC). Detection rates such as true positive, true negative, false positive and false negative rate were reported using a confusion matrix analysis. The Cox model’s performance was evaluated based on Harrell’s concordance index (C-index), calibration curves and Kaplan–Meier survival analysis. Results: The PI-Risk integrated with 10 risk factors is formed to accurate risk stratification. In multinomial regression analyzing model, predicted IntraT-Rad G0 and PI-RADS score 2 were two independent predictors of G0 stage. Predicted IntraT-Rad G1 (OR, 2.42; 95% CIs, 2.13–2.86, p < 0.001) and PSA 4-10 ng/ml were two independent predictors of G1 stage. PSA 10-20 ng/ml, Predicted PeriT-DLR-SqueezeNet G1 and Predicted IntraT-Rad G3 were three independent predictors of G2 stage. PI-RADS score 5 were the independent predictor of G3 stage. PI-RADS score 5 and PSA > 100 ng/ml were two independent predictors of G4 stage. Combined use of PSA 4-10 ng/ml, PI-RADS 1-2 and stacked IntraT-Rad G0-1 resulted in excellent NPV (94.1%) for CIS diseases; and combined use of PSA >100 ng/ml and PI-RADS 5 resulted in high PPV (79.8%) for high-risk PCa. In follow-up, patients stratified by PI-Risk showed significantly different biochemical recurrence rate after surgery. Conclusions: We concluded that the PI-Risk can offer a noninvasive alternative tool to stratify PCa aggressiveness. This enables a step towards PCa risk stratification." @default.
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- W3123862014 date "2021-02-11" @default.
- W3123862014 modified "2023-09-30" @default.
- W3123862014 title "Integration of Clinical Identifications With Deep Transferrable Imaging Feature Representations Can Help Predict Prostate Cancer Aggressiveness and Outcome" @default.
- W3123862014 doi "https://doi.org/10.21203/rs.3.rs-180726/v1" @default.
- W3123862014 hasPublicationYear "2021" @default.
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