The British journal of radiology · 2026
Prognostic significance of baseline 18F-FDG PET/CT parameters in combination with an artificial intelligence-based pleural effusion segmentation model for malignant pleural effusion.
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Cao Yaqi, Hu Fan, Feng Shuqian, Xia Hui, Tan Xueyun, Pan Feng, Li Minglei, Wang Sufei, Yang Lian, Ma Yanling, Meng Daquan, Huang Ze, Wang Zhengyan, Yi Mengjia, Bao Qingjia, Lan Xiaoli, Jin Yang
Аннотация
OBJECTIVES: We aimed to use an artificial intelligence (AI)-based pleural effusion segmentation model on baseline 18F-FDG PET/CT images to investigate the prognostic value of PET/CT-derived parameters for overall survival (OS) among lung cancer patients with malignant pleural effusion (MPE).
METHODS: A total of 146 patients with MPEs were recruited. An integrated AI segmentation model combining 3D spatially weighted and 2D classical U-Net segmented pleural effusion for 18F-FDG PET/CT parameter extraction. Cox regression analyses revealed independent 12-month survival predictors. The area under the receiver operating characteristic curve (AUC) and DeLong's test were used to evaluate the discriminant power of the predictors and the LENT score. Bootstrap resampling was employed for internal validation.
RESULTS: The patients comprised 81 males (55.5%) and had a mean age of 61.7 (SD = 11.5) years. The key survival predictors included maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The combined PET/CT parameters demonstrated a statistically significant advantage over the LENT score for 12-month survival prediction (AUC: 0.849, 95% CI, 0.795-0.903 vs AUC: 0.732, 95% CI, 0.660-0.796). The internal bootstrap validation had an AUC of 0.840, (95% CI, 0.671-0.922) and demonstrated a well-fitting calibration curve.
CONCLUSIONS: The baseline 18F-FDG-PET/CT parameters extracted using the deep learning model performed excellently in predicting MPE survival and may complement existing MPE survival models and guide clinical stratified treatment.
ADVANCES IN KNOWLEDGE: AI-integrated 18F-FDG-PET/CT radiomics improved prognostic assessment of MPE, facilitating personalized interventions stratified by survival expectations.
METHODS: A total of 146 patients with MPEs were recruited. An integrated AI segmentation model combining 3D spatially weighted and 2D classical U-Net segmented pleural effusion for 18F-FDG PET/CT parameter extraction. Cox regression analyses revealed independent 12-month survival predictors. The area under the receiver operating characteristic curve (AUC) and DeLong's test were used to evaluate the discriminant power of the predictors and the LENT score. Bootstrap resampling was employed for internal validation.
RESULTS: The patients comprised 81 males (55.5%) and had a mean age of 61.7 (SD = 11.5) years. The key survival predictors included maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The combined PET/CT parameters demonstrated a statistically significant advantage over the LENT score for 12-month survival prediction (AUC: 0.849, 95% CI, 0.795-0.903 vs AUC: 0.732, 95% CI, 0.660-0.796). The internal bootstrap validation had an AUC of 0.840, (95% CI, 0.671-0.922) and demonstrated a well-fitting calibration curve.
CONCLUSIONS: The baseline 18F-FDG-PET/CT parameters extracted using the deep learning model performed excellently in predicting MPE survival and may complement existing MPE survival models and guide clinical stratified treatment.
ADVANCES IN KNOWLEDGE: AI-integrated 18F-FDG-PET/CT radiomics improved prognostic assessment of MPE, facilitating personalized interventions stratified by survival expectations.