Longitudinal Changes of CT-radiomic and Systemic Inflammatory Features Predict Survival in Advanced Non-Small Cell Lung Cancer Patients Treated With Immune Checkpoint Inhibitors.

PURPOSE: This study aims to determine whether longitudinal changes in CT radiomic features (RFs) and systemic inflammatory indices outperform single-time-point assessment in predicting survival in advanced non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs). MATERIALS AND METHODS: We retrospectively acquired pretreatment (T0) and first disease assessment (T1) RFs and systemic inflammatory indices from a single-center cohort of stage IV NSCLC patients and computed their delta (Δ) variation as [(T1-T0)/T0]. RFs from the primary tumor were selected for building baseline-radiomic (RAD) and Δ-RAD scores using the linear combination of standardized predictors detected by LASSO Cox regression models. Cox models were generated using clinical features alone or combined with baseline and Δ blood parameters and integrated with baseline-RAD and Δ-RAD. All models were 3-fold cross-validated. A prognostic index (PI) of each model was tested to stratify overall survival (OS) through Kaplan-Meier analysis. RESULTS: We included 90 ICI-treated NSCLC patients (median age 70 y [IQR=42 to 85], 63 males). Δ-RAD outperformed baseline-RAD for predicting OS [c-index: 0.632 (95%CI: 0.628 to 0.636) vs. 0.605 (95%CI: 0.601 to 0.608) in the test splits]. Integrating longitudinal changes of systemic inflammatory indices and Δ-RAD with clinical data led to the best model performance [Integrated-Δ model, c-index: 0.750 (95% CI: 0.749 to 0.751) in training and 0.718 (95% CI: 0.715 to 0.721) in testing splits]. PI enabled significant OS stratification within all the models (P-value <0.01), reaching the greatest discriminative ability in Δ models (high-risk group HR up to 7.37, 95% CI: 3.9 to 13.94, P<0.01). CONCLUSION: Δ-RAD improved OS prediction compared with single-time-point radiomic in advanced ICI-treated NSCLC. Integrating Δ-RAD with a longitudinal assessment of clinical and laboratory data further improved the prognostic performance.

A Bayesian multi-proxy contribution to the socioeconomic, political, and cultural history of late medieval Capitanata (southern Italy).

Medieval southern Italy is typically viewed as a region where political, religious, and cultural systems coexisted and clashed. Written sources often focus on elites and give an image of a hierarchical feudal society supported by a farming economy. We undertook an interdisciplinary study combining historical and archaeological evidence with Bayesian modelling of multi-isotope data from human (n = 134) and faunal (n = 21) skeletal remains to inform on the socioeconomic organisation, cultural practices, and demographics of medieval communities in Capitanata (southern Italy). Isotopic results show significant dietary differences within local populations supportive of marked socioeconomic hierarchies. Bayesian dietary modelling suggested that cereal production, followed by animal management practices, was the economic basis of the region. However, minor consumption of marine fish, potentially associated with Christian practices, revealed intra-regional trade. At the site of Tertiveri, isotope-based clustering and Bayesian spatial modelling identified migrant individuals likely from the Alpine region plus one Muslim individual from the Mediterranean coastline. Our results align with the prevailing image of Medieval southern Italy but they also showcase how Bayesian methods and multi-isotope data can be used to directly inform on the history of local communities and of the legacy that these left.