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Updated: Sep 18, 2026
Patient-derived Orthotopic Xenograft Models for Human Urothelial Cell Carcinoma and Colorectal Cancer Tumor Growth and Spontaneous Metastasis
Published on: May 12, 2019
Development and internal validation of machine-learning models to predict adverse pathological features in upper
Philipp Korn1, Maximilian Pallauf2, Soum Lokeshwar3
1School of Medicine, Johns Hopkins Medicine, Brady Urological Institute, Baltimore, MD; Department of Urology, Faculty of Medicine, University of Augsburg, Augsburg, Germany; Comprehensive Cancer Center Augsburg (CCCA), University Medical Center Augsburg, Augsburg, Germany; Bavarian Cancer Research Center (BZKF), Augsburg, Germany.
Background:
Accurate preoperative staging and identification of adverse pathological features in upper tract urothelial carcinoma (UTUC) remain contemporary challenges. We aimed to develop and internally validate machine-learning (ML) models to predict muscle-invasive (≥pT2) and lymph node positive (pN+) disease using preoperative variables.
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.
