The Journal of clinical endocrinology and metabolism · 2025
Machine Learning-Based Survival Prediction Tool for Adrenocortical Carcinoma.
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Saygili Emre Sedar, Elhassan Yasir S, Prete Alessandro, Lippert Juliane, Altieri Barbara, Ronchi Cristina L
Аннотация
CONTEXT: Adrenocortical carcinoma (ACC) is a rare, aggressive malignancy with difficult to predict clinical outcomes. The S-GRAS score combines clinical and histopathological variables (tumor stage, grade, resection status, age, and symptoms) and showed good prognostic performance for patients with ACC.
OBJECTIVE: To improve ACC prognostic classification by applying robust machine learning (ML) models.
METHOD: We developed ML models to enhance outcome prediction using the published S-GRAS dataset (n = 942) as the training cohort and an independent dataset (n = 152) for validation. Sixteen ML models were constructed based on individual clinical variables. The best-performing models were used to develop a web-based tool for individualized risk prediction.
RESULTS: Quadratic Discriminant Analysis, Light Gradient Boosting Machine, and AdaBoost Classifier models exhibited the highest performance, predicting 5-year overall mortality (OM), and 1-year and 3-year disease progression (DP) with F1 scores of 0.79, 0.63, and 0.83 in the training cohort, and 0.72, 0.60, and 0.83 in the validation cohort. Sensitivity and specificity for 5-year OM were at 77% and 77% in the training cohort, and 65% and 81% in the validation cohort, respectively. A web-based tool (https://acc-survival.streamlit.app) was developed for easily applicable and individualized risk prediction of mortality and disease progression.
CONCLUSION: S-GRAS parameters can efficiently predict outcome in patients with ACC, even using a robust ML model approach. Our web app instantly estimates the mortality and disease progression for patients with ACC, representing an accessible tool to drive personalized management decisions in clinical practice.
OBJECTIVE: To improve ACC prognostic classification by applying robust machine learning (ML) models.
METHOD: We developed ML models to enhance outcome prediction using the published S-GRAS dataset (n = 942) as the training cohort and an independent dataset (n = 152) for validation. Sixteen ML models were constructed based on individual clinical variables. The best-performing models were used to develop a web-based tool for individualized risk prediction.
RESULTS: Quadratic Discriminant Analysis, Light Gradient Boosting Machine, and AdaBoost Classifier models exhibited the highest performance, predicting 5-year overall mortality (OM), and 1-year and 3-year disease progression (DP) with F1 scores of 0.79, 0.63, and 0.83 in the training cohort, and 0.72, 0.60, and 0.83 in the validation cohort. Sensitivity and specificity for 5-year OM were at 77% and 77% in the training cohort, and 65% and 81% in the validation cohort, respectively. A web-based tool (https://acc-survival.streamlit.app) was developed for easily applicable and individualized risk prediction of mortality and disease progression.
CONCLUSION: S-GRAS parameters can efficiently predict outcome in patients with ACC, even using a robust ML model approach. Our web app instantly estimates the mortality and disease progression for patients with ACC, representing an accessible tool to drive personalized management decisions in clinical practice.