Background: Reliable biomarkers that capture tumor–host interactions and predict treatment resistance in extensive-stage small cell lung cancer (SCLC) remain limited. We evaluated the prognostic and predictive value of the pretreatment lactate dehydrogenase-to-albumin ratio (LAR), an integrative biomarker reflecting metabolic activity, systemic inflammation, and host nutritional status. Methods: This multicenter, retrospective cohort study included patients with extensive-stage SCLC treated at five tertiary centers between 2016 and 2024. Pretreatment LAR was calculated using baseline serum lactate dehydrogenase and albumin levels and dichotomized using a Youden index-derived cut-off at the 12-month overall survival (OS) horizon. Time-dependent receiver operating characteristic (ROC) analyses using inverse probability weighting were performed to assess discriminative performance. Survival outcomes were evaluated using Kaplan–Meier estimates and Cox proportional hazards models. Associations with platinum resistance and lack of objective treatment benefit (defined as progressive disease as best response) were examined using logistic regression models. Results: A total of 223 patients were included. Elevated LAR was associated with inferior OS (median, 15.8 vs. 25.2 months; log-rank p < 0.001) and progression-free survival (7.9 vs. 11.5 months; p < 0.001). In multivariable analysis, LAR remained independently associated with OS (HR, 1.43; 95% CI, 1.04–1.95; p = 0.028). LAR demonstrated modest but consistently superior discriminative performance compared with other inflammatory indices for both 12-month OS (area under the curve [AUC], 0.692) and 6-month progression-free survival (PFS) (AUC, 0.646), with statistically significant differences in DeLong comparisons. Higher LAR was independently associated with increased odds of platinum resistance (adjusted odds ratio [aOR], 2.31; 95% CI, 1.41–3.81; p = 0.001) and lack of objective treatment benefit (adjusted OR, 2.04; 95% CI, 1.33–3.14; p = 0.001). Conclusions: Pretreatment LAR is a clinically accessible and biologically integrative biomarker associated with survival and treatment resistance in extensive-stage SCLC. By capturing tumor–host interactions, LAR may support risk stratification and identify patients at increased risk of early treatment failure. Prospective validation is warranted to define its role in biomarker-driven clinical decision-making.

Aims: Organizing pneumonia (OP) frequently presents as a diagnostic challenge because of its propensity to mimic lung malignancy on FDG-PET/CT. This study aimed to characterize the metabolic heterogeneity of OP and evaluate the relationship between systemic inflammatory burden and a hypermetabolic imaging phenotype. Methods: We retrospectively analyzed data of 61 patients with histopathologically confirmed OP with suspected malignancy who underwent preoperative FDG-PET/CT. Patients were stratified by the cohort-specific median primary lesion SUVmax of 5.1. Systemic inflammatory indices, including the pan-immune-inflammation value (PIV), were derived from baseline laboratory parameters. Independent predictors of high FDG uptake (SUVmax ≥5.1) were identified through multivariate logistic regression. Results: High SUVmax lesions were significantly associated with a history of smoking (83.9% vs. 60.0%; p=0.049), spiculated margins (38.7% vs. 10.0%; p=0.016), and metabolically active mediastinal lymph nodes (38.7% vs. 16.7%; p=0.036). Preoperative inflammatory indices, including monocyte-to-lymphocyte ratio (MLR) (p=0.024), platelet-to-lymphocyte ratio (PLR) (p=0.046), PIV (p=0.037), and Systemic Inflammatory Response Index (SIRI) (p=0.030), were significantly elevated in the high SUVmax group. In the multivariate analysis, PIV remained independently associated with a hypermetabolic phenotype (OR: [1.20; 95% CI]: 1.02-1.42; p=0.031). Notably, lesion size and nodal involvement did not emerge as independent predictors of primary lesion FDG avidity. Conclusion: OP encompasses a biologically heterogeneous spectrum in which a distinct subset mimics lung malignancy through intense FDG avidity. This hypermetabolic phenotype is primarily driven by systemic inflammatory intensity rather than lesion morphology alone. Integrating composite biomarkers, such as PIV, may refine the contextual interpretation of PET/CT findings and support preoperative risk stratification in patients with indeterminate pulmonary lesions.

Background: Distinguishing organizing pneumonia from recurrent malignancy in patients undergoing cancer surveillance remains a major clinical challenge. Although inflammatory biomarkers have emerged as potential diagnostic tools, their incremental value beyond imaging-based assessment remains uncertain. Methods: In this retrospective single-center study, we evaluated 170 patients with prior malignancy who underwent surgical assessment for suspicious lung-only pulmonary lesions between 2013 and 2023. Histopathology confirmed organizing pneumonia in 61 patients and malignancy in 109 patients. Preoperative clinical, radiologic, Positron emission tomography/computed tomography (PET/CT), and inflammatory biomarker variables were incorporated into predefined clinical, radiology-PET/CT, biomarker, and integrated models using Least absolute shrinkage and selection operator (LASSO)-penalized logistic regression with internal cross-validation. Results: The radiology/PET model demonstrated excellent discrimination (AUC 0.927), closely approximating the performance of the integrated model (AUC 0.935). The clinical and biomarker-only models showed moderate discrimination (AUC 0.764 and 0.778, respectively). Adding inflammatory biomarkers to imaging-derived features resulted in only a minimal improvement in diagnostic performance (ΔAUC = 0.009, 95% CI −0.019 to 0.037), indicating no statistically significant incremental benefit. Calibration, decision curve, and predictor stability analyses consistently identified radiologic and PET/CT-derived variables as the principal sources of predictive information. Conclusions: In patients with suspicious pulmonary lesions during cancer surveillance, diagnostic discrimination was driven predominantly by imaging-derived features, whereas conventional inflammatory biomarkers provided limited incremental value. These findings support an imaging-centered approach to preoperative risk stratification and multidisciplinary decision-making.