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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\u2019s test were used to evaluate the discriminant power of the predictors and the LENT score. Bootstrap resampling was employed for internal validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The patients comprised 81 males (55.5%) and had a mean age of 61.7 (SD\u2009=\u200911.5) 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Advances in knowledge<\/jats:title>\n                    <jats:p>AI-integrated 18F-FDG-PET\/CT radiomics improved prognostic assessment of MPE, facilitating personalized interventions stratified by survival expectations.<\/jats:p>\n                  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