BMC cancer · 2026
Development and internal validation of an explainable machine-learning model to predict 3-year overall survival rate after radical cystectomy.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wang Yunze, Ailiyaer Aikeshanjiang, Chen Shiming, Wang Wenguang
Аннотация
BACKGROUND: This study aimed to develop and internally validate an explainable machine-learning model using routinely available clinicopathologic and laboratory variables for predicting 3-year overall survival (OS) after radical cystectomy.
METHODS: We retrospectively included 300 patients who underwent radical cystectomy between January 2018 and December 2022. The primary endpoint was prespecified as death within 3 years after surgery, chosen as a clinically relevant fixed-time milestone with relatively complete follow-up at this horizon in our cohort. Predictors were selected in the training set using LASSO logistic regression followed by random-forest recursive feature elimination.
RESULTS: In internal validation, AUCs ranged from 0.834 to 0.950. CatBoost achieved the best overall classification performance (AUC = 0.931, accuracy = 0.862, sensitivity = 0.647, specificity = 0.951, PPV = 0.846, and NPV = 0.867). SHAP analyses identified tumor stage (T, N, and M stage) as the dominant drivers of predicted risk, with additional contributions from age, BMI, albumin, globulin, lymphocyte count, platelet count, and preoperative creatinine.
CONCLUSIONS: We developed an internally validated, SHAP-interpretable CatBoost model for predicting 3-year overall survival (OS) after radical cystectomy. External validation and recalibration in independent cohorts are required before clinical use.
TRIAL REGISTRATION: This study did not involve a prospective clinical trial.
TRIAL REGISTRATION: Not applicable.
METHODS: We retrospectively included 300 patients who underwent radical cystectomy between January 2018 and December 2022. The primary endpoint was prespecified as death within 3 years after surgery, chosen as a clinically relevant fixed-time milestone with relatively complete follow-up at this horizon in our cohort. Predictors were selected in the training set using LASSO logistic regression followed by random-forest recursive feature elimination.
RESULTS: In internal validation, AUCs ranged from 0.834 to 0.950. CatBoost achieved the best overall classification performance (AUC = 0.931, accuracy = 0.862, sensitivity = 0.647, specificity = 0.951, PPV = 0.846, and NPV = 0.867). SHAP analyses identified tumor stage (T, N, and M stage) as the dominant drivers of predicted risk, with additional contributions from age, BMI, albumin, globulin, lymphocyte count, platelet count, and preoperative creatinine.
CONCLUSIONS: We developed an internally validated, SHAP-interpretable CatBoost model for predicting 3-year overall survival (OS) after radical cystectomy. External validation and recalibration in independent cohorts are required before clinical use.
TRIAL REGISTRATION: This study did not involve a prospective clinical trial.
TRIAL REGISTRATION: Not applicable.