European journal of nuclear medicine and molecular imaging · 2026
An interpretable machine learning model integrating [18F]FDG PET/CT radiomics and clinical features for predicting perforation following chemotherapy in gastrointestinal lymphoma: a multicenter study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Chen Donghe, Li Tiancheng, Cui Jianan, Yang Chunmei, Liu Zhenfeng, Huang Huang, Liu Huafeng, Su Xinhui
Аннотация
BACKGROUND: Perforation following chemotherapy in gastrointestinal lymphoma (PFCGL) is a rare but severe and life-threatening complication. Early pre-chemotherapy prediction is crucial for optimizing treatment and improving outcomes, yet it remains challenging. This study aimed to develop a machine learning (ML) model combining clinical features and [18F]FDG PET/CT radiomics signature to predict the risk of PFCGL.
MATERIALS AND METHODS: A total of 257 patients diagnosed at two hospitals with gastrointestinal lymphoma were included. Clinical data and PET/CT images were collected. PET/CT radiomics signature were extracted from the FDG-avid regions. Five ML algorithms were used to develop predictive models, including clinical, PET/CT radiomics, and clinical-PET/CT models. The SHapley Additive exPlanations (SHAP) method was applied for model interpretability.
RESULTS: The internal training, internal validation and external validation cohorts consisted of 144 patients (mean age: 56.90 ± 14.69 years; 88 males), 63 patients (mean age: 61.76 ± 13.71 years; 36 males) and 50 patients (mean age: 63.28 ± 12.17 years; 27 males), respectively. Among these, 22, 11, and 11 patients were diagnosed with PFCGL in the three cohorts, correspondingly. For clinical features, elevated C-reactive protein levels and pathological type of T-cell non-Hodgkin's lymphoma were risk factors for predicting PFCGL. SHAP analysis revealed that PET/CT radiomics signature significantly contributed to the prediction of PFCGL. The clinical-PET/CT model utilizing the logistic regression (LR) algorithm exhibited superior predictive performance for PFCGL compared to the individual clinical model and the PET/CT radiomics model employing the ExtraTrees algorithm. This was evidenced by the area under the curve (AUC) values of 0.918, 0.872 and 0.857 in the training cohort, 0.858, 0.795 and 0.849 in the internal validation cohort, and 0.852, 0.770 and 0.807 in the external validation cohort, respectively.
CONCLUSION: The combined model that integrates [18F]FDG PET/CT radiomics signature with clinical features through the application of machine learning provides a clinically actionable nomogram for the evaluation and prediction the risk of PFCGL.
MATERIALS AND METHODS: A total of 257 patients diagnosed at two hospitals with gastrointestinal lymphoma were included. Clinical data and PET/CT images were collected. PET/CT radiomics signature were extracted from the FDG-avid regions. Five ML algorithms were used to develop predictive models, including clinical, PET/CT radiomics, and clinical-PET/CT models. The SHapley Additive exPlanations (SHAP) method was applied for model interpretability.
RESULTS: The internal training, internal validation and external validation cohorts consisted of 144 patients (mean age: 56.90 ± 14.69 years; 88 males), 63 patients (mean age: 61.76 ± 13.71 years; 36 males) and 50 patients (mean age: 63.28 ± 12.17 years; 27 males), respectively. Among these, 22, 11, and 11 patients were diagnosed with PFCGL in the three cohorts, correspondingly. For clinical features, elevated C-reactive protein levels and pathological type of T-cell non-Hodgkin's lymphoma were risk factors for predicting PFCGL. SHAP analysis revealed that PET/CT radiomics signature significantly contributed to the prediction of PFCGL. The clinical-PET/CT model utilizing the logistic regression (LR) algorithm exhibited superior predictive performance for PFCGL compared to the individual clinical model and the PET/CT radiomics model employing the ExtraTrees algorithm. This was evidenced by the area under the curve (AUC) values of 0.918, 0.872 and 0.857 in the training cohort, 0.858, 0.795 and 0.849 in the internal validation cohort, and 0.852, 0.770 and 0.807 in the external validation cohort, respectively.
CONCLUSION: The combined model that integrates [18F]FDG PET/CT radiomics signature with clinical features through the application of machine learning provides a clinically actionable nomogram for the evaluation and prediction the risk of PFCGL.