Hematology (Amsterdam, Netherlands) · 2026
Development and internal validation of a machine learning model for predicting second primary malignancy risk in hodgkin lymphoma survivors: a population-based competing risk analysis.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Li Sinian, Xiong Mingyue, Gan Zhonggui, Lu Haishan, Zhao Yiheng, Huang Hong, Yi Tingzhuang
Аннотация
BACKGROUND: Hodgkin lymphoma (HL) survivors face an elevated long-term risk of developing second primary malignancies (SPMs), yet accurate prediction tools remain scarce. This study aimed to develop and internally validate a machine learning-based prediction model for SPM risk in HL survivors using a competing risk framework.
METHODS: We analysed 57,429 patients diagnosed with HL between 2006 and 2020 from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into training (n = 40,201, 70%) and internal validation (n = 17,228, 30%) cohorts. A random survival forest (RSF) model was developed to predict SPM occurrence, with death without SPM treated as a competing event. Model performance was compared against the traditional Fine-Grey subdistribution hazard regression. Discrimination was assessed using time-dependent area under the receiver operating characteristic curve (AUC), and calibration was evaluated through calibration plots. Clinical utility was determined via decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) analysis was employed to interpret the model.
RESULTS: During follow-up, 4,954 patients (8.6%) developed SPMs, while 9,750 (17.0%) died without SPM. The RSF model demonstrated superior discriminative ability compared to Fine-Grey regression across all time points: AUC at 3 years (0.721 vs. 0.549), 5 years (0.761 vs. 0.555), and 10 years (0.804 vs. 0.553). Calibration plots indicated good agreement between predicted and observed risks. DCA confirmed the RSF model provided greater net clinical benefit across a wide range of threshold probabilities. SHAP analysis identified age as the most influential predictor, followed by disease stage, radiotherapy status, race, sex, and chemotherapy.
CONCLUSIONS: The RSF-based machine learning model accurately predicts SPM risk in HL survivors within a competing risk framework, substantially outperforming traditional regression approaches. This interpretable prediction tool may facilitate personalised surveillance strategies and risk-adapted follow-up for HL survivors.
METHODS: We analysed 57,429 patients diagnosed with HL between 2006 and 2020 from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into training (n = 40,201, 70%) and internal validation (n = 17,228, 30%) cohorts. A random survival forest (RSF) model was developed to predict SPM occurrence, with death without SPM treated as a competing event. Model performance was compared against the traditional Fine-Grey subdistribution hazard regression. Discrimination was assessed using time-dependent area under the receiver operating characteristic curve (AUC), and calibration was evaluated through calibration plots. Clinical utility was determined via decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) analysis was employed to interpret the model.
RESULTS: During follow-up, 4,954 patients (8.6%) developed SPMs, while 9,750 (17.0%) died without SPM. The RSF model demonstrated superior discriminative ability compared to Fine-Grey regression across all time points: AUC at 3 years (0.721 vs. 0.549), 5 years (0.761 vs. 0.555), and 10 years (0.804 vs. 0.553). Calibration plots indicated good agreement between predicted and observed risks. DCA confirmed the RSF model provided greater net clinical benefit across a wide range of threshold probabilities. SHAP analysis identified age as the most influential predictor, followed by disease stage, radiotherapy status, race, sex, and chemotherapy.
CONCLUSIONS: The RSF-based machine learning model accurately predicts SPM risk in HL survivors within a competing risk framework, substantially outperforming traditional regression approaches. This interpretable prediction tool may facilitate personalised surveillance strategies and risk-adapted follow-up for HL survivors.