Development and validation of multiparametric models incorporating F-FDG PET/CT dissemination characteristic for predicting outcomes of small cell lung cancer.

[OBJECTIVE] To evaluate the prognostic value of the tumor dissemination characteristic, metabolic parameters from baseline F-FDG PET/CT, clinical indicators, and pathological indicators in small cell lung cancer (SCLC), and to construct prognostic models.

[MATERIALS & METHODS] SCLC patients who underwent baseline F-FDG PET/CT were retrospectively analyzed and randomly divided into training and validation cohorts (7:3). The tumor dissemination characteristic, metabolic characteristics, morphological features, and clinical and pathological indicators were collected. Cox regression analysis was employed to identify independent prognostic factors. Prognostic models and corresponding nomograms were developed and evaluated using receiver operating characteristic (ROC) curves.

[RESULTS] 303 patients with SCLC were enrolled (including 204 males and 99 females; median age: 62 years, interquartile range: 56-67 years). Multivariate Cox regression analysis identified age, stage, neuron-specific enolase (NSE), and the standardized distance between the two farthest lesions (SD) as independent prognostic factors for progression-free survival (PFS). Area under curve (AUC) values for predicting 6-month, 12-month, and 24-month PFS were 0.790, 0.778, and 0.750 in the training cohort, and 0.778, 0.771, and 0.744 in the validation cohort. For overall survival (OS), age, stage, NSE, whole-body metabolic tumor volume (MTV), and SD were independent prognostic factors. AUC values for predicting 1-year, 2-year, and 3-year OS were 0.861, 0.830, and 0.799 in the training cohort, and 0.834, 0.801, and 0.787 in the validation cohort.

[CONCLUSION] The tumor dissemination characteristic from baseline F-FDG PET/CT is a novel independent prognostic factor in SCLC. Additionally, the models incorporating the dissemination characteristic, metabolic parameter, and clinical indicators demonstrate excellent predictive capabilities in SCLC.