Organ-based tumor distribution for predicting prognosis in small-cell lung cancer using fluorodeoxyglucose positron...

To validate and compare conventional metabolic tumor burden measurements with comprehensive metabolic tumor distribution patterns using [¹⁸F] fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) to predict small-cell lung cancer (SCLC) prognosis. This retrospective study included 520 patients with SCLC (mean age ± standard deviation, 67 ± 5.6 years; 84.8% men) who underwent PET/CT for staging. Of these, 364 scans were used for training (n = 291) and internal (n = 73) tests, while 156 other scans were used for external testing. Clinical data (age, sex, and stage) were reviewed. Volumes of interest were manually drawn using a threshold standard uptake value of 2.5 for total lesion glycolysis (TLG) for all tumor lesions on PET. TLG with distribution (TLGd) and organ-based tumor distribution (metastasis in organs, METAORG) was analyzed from CT-based automatic organ segmentation and overlaid on PET. Four survival prediction models (event and duration) were developed using a Random Forest classifier: (1) clinical factors, (2) tumor TLG, (3) TLGd and METAORG, and (4) combined models. The top 11 features were selected for survival duration prediction included clinical factors (age and stage), TLG, five TLGd radiomics features, and three METAORG features (axial and peripheral skeletal distribution patterns and the liver distribution pattern). In the internal test, C-indices for overall survival were 0.611, 0.592, 0.721, and 0.753 for tumor TLG, clinical, METAORG, and combined model, respectively. External test C-indices were 0.637, 0.326, 0.706, and 0.740, respectively. The combined model, which incorporated tumor distribution information such as TLGd and METAORG, demonstrated the highest predictive power for both test sets. The combined model outperformed the other models in predicting survival. Application of tumor distribution (TLGd and METAORG) to whole-body tumor distribution pattern analysis shows promise for improving prognosis evaluation, with advantages of quantifiable metastasis stratification.