Development and internal validation of machine-learning models to predict adverse pathological features in upper tract urothelial carcinoma.
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Ошибки и ограничения
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Korn Philipp, Pallauf Maximilian, Lokeshwar Soum, Musso Giacomo, Wu Zhenje, Wang Linhui, Kutikov Alex, Lee Randall, Ditonno Francesco, Veccia Alessandro, Bertolo Riccardo, Antonelli Alessandro, Lin Lin, Margulis Vitaly, Ghoreifi Alireza, Ebrahimi Hooman, Djaladat Hooman, Kinoshita Hidefumi, Yoshida Takashi, Tuderti Gabriele, Proietti Flavia, Simone Giuseppe, Mari Andrea, Lambertini Luca, Minervini Andrea, Tozzi Marco, Ferro Matteo, Contieri Roberto, Perdona Sisto, Amparore Daniele, Bignante Gabriele, Porpiglia Francesco, Nativ Omri Falik, Gonzalgo Mark L, Santangelo Alfonso, Silvani Carlo, Stephens Alex, Abdollah Firas, Fu Melinda, Ghodoussipour Saum, Zhu Alec, Mehrazin Reza, Sidhom Daniel A, Sundaram Chandru P, Matsumoto Takashi, Shiota Masaki, Dehghanmanshadi Alireza, Rais-Bahrami Soroush, Daher Karim, Derweesh Ithar H, Autorino Riccardo, Singla Nirmish
Аннотация
MATERIAL AND METHODS: We analyzed a large, multi-institutional registry of patients treated surgically for clinically nonmetastatic UTUC (ROBUUST 3.0). We separately identified patients with ≥pT2 stage and pN+ stage at surgery. Predictors were selected a priori based on clinical relevance, and missing data were imputed using Hyperimpute. Five classifiers (elastic net logistic regression, random forest, LightGBM, support vector machine with radial-basis-function kernel and a stacking ensemble) were trained using an 80/20 stratified train-test split. Discrimination (measured by area under the curve (AUC)), calibration and Brier score were assessed in the independent test cohort.
RESULTS: We identified 3,179 patients with ≥pT2 stage (636 designated as the test cohort) and 1493 patients with pN+ disease (299 designated as the test cohort). In the test cohort, discrimination was moderate and comparable across models. For ≥pT2 prediction, AUC ranged from 0.73 to 0.74 (best model: stacking, AUC 0.74, 95% confidence interval [CI]: 0.70-0.78, and Brier 0.21). For pN+, AUC ranged from 0.73 to 0.75 (best model: stacking, AUC 0.75, 95% CI 0.69-0.81, and Brier 0.18). Established clinical predictors, particularly biopsy grade and clinical staging parameters dominated risk estimation. Limitations include retrospective design and lack of external validation.
CONCLUSIONS AND CLINICAL IMPLICATIONS: ML models using preoperative variables achieved moderate discrimination for predicting adverse pathological features in UTUC. External validation and prospective assessment of clinical utility are necessary before informing preoperative patient selection.