This study aims to develop and evaluate preoperative CT-based peritumoral and tumoral radiomic features to predict tumor spread through air space (STAS) status in clinical stage I lung adenocarcinoma (LUAD). From June 2018 to December 2019, a retrospective diagnostic investigation was done. Patients with pathologically confirmed STAS status (N = 256) were eventually enrolled. The development cohort consisted of 191 patients (74.6%) chosen randomly in a 7:3 ratio, whereas the validation group consisted of 65 patients (25.4%). The performance of models was assessed using receiver operating characteristic analysis, accuracy, sensitivity, specificity, negative predictive values, and positive predictive values. The STAS positive status was found in 85 (33.2%) of the 256 patients (female: 53.2%; median [IQR] age: 62.0, [53.0-79.0] years), while the STAS negative status was found in 171 patients (66.8%) (female:50.6%; median [IQR] age: 62.0, [53.0-87.0] years). The combined TRS and PRS-15 mm model had an AUC of 0.854 (95% CI, 0.799-0.909) in the development cohort and 0.870 (95% CI, 0.781-0.958) in the validation cohort, indicating that the tumor radiomic signature (TRS) model and different peritumoral radiomic signature (PRS) models were used to build the optimal gross radiomic signature (GRS) model. The radiomic nomogram achieves superior discriminatory performance than GRS and clinical and radiological signatures (CRS), with an AUC of 0.871 (95% CI, 0.820-0.922) in the development cohort and AUC of 0.869 (95% CI, 0.776-0.961) in the validation cohort. Based on the Akaike information criterion (AIC) and decision curve analysis (DCA), the radiomic nomogram provided greater clinical predictive capacity than clinical or any radiomic signatures alone. In conclusion, we discovered that peritumoral characteristics were substantially related to STAS status. This study revealed the unit of radiomic signature and clinical signatures may have a better performance in STAS status.

Objectives: To develop and validate a preoperative CT-based nomogram combined with radiomic and clinical–radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions. Methods: This was a retrospective, diagnostic study conducted from August 1, 2018, to May 1, 2020, at three centers. Patients with a solitary pulmonary nodule were enrolled in the GDPH center and were divided into two groups (7:3) randomly: development (n = 149) and internal validation (n = 54). The SYSMH center and the ZSLC Center formed an external validation cohort of 170 patients. The least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression analysis were used to feature signatures and transform them into models. Results: The study comprised 373 individuals from three independent centers (female: 225/373, 60.3%; median [IQR] age, 57.0 [48.0–65.0] years). The AUCs for the combined radiomic signature selected from the nodular area and the perinodular area were 0.93, 0.91, and 0.90 in the three cohorts. The nomogram combining the clinical and combined radiomic signatures could accurately predict interstitial invasion in patients with a solitary pulmonary nodule (AUC, 0.94, 0.90, 0.92) in the three cohorts, respectively. The radiomic nomogram outperformed any clinical or radiomic signature in terms of clinical predictive abilities, according to a decision curve analysis and the Akaike information criteria. Conclusions: This study demonstrated that a nomogram constructed by identified clinical–radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness. Key Points: • The radiomic signature from the perinodular area has the potential to predict pathology invasiveness of the solitary pulmonary nodule. • The new radiomic nomogram was useful in clinical decision-making associated with personalized surgical intervention and therapeutic regimen selection in patients with early-stage non-small-cell lung cancer.

Background The predictive efficacy of current biomarker of immune checkpoint inhibitors (ICIs) is not sufficient. This study investigated the causality between radiomic biomarkers and immunotherapy response status in patients with stage IB–IV non-small cell lung cancer (NSCLC), including its biological context for ICIs treatment response prediction. Methods CT images from 319 patients with pretreatment NSCLC receiving immunotherapy between January 2015 and November 2021 were retrospectively collected and composed a discovery (n=214), independent validation (n=54), and external test cohort (n=51). A set of 851 features was extracted from tumorous and peritumoral volumes of interest (VOIs). The reference standard is the durable clinical benefit (DCB, sustained disease control for more than 6 months assessed via radiological evaluation). The predictive value of combined radiomic signature (CRS) for pathological response was subsequently assessed in another cohort of 98 patients with resectable NSCLC receiving ICIs preoperatively. The association between radiomic features and tumor immune landscape on the online data set (n=60) was also examined. A model combining clinical predictor and radiomic signatures was constructed to improve performance further. Results CRS discriminated DCB and non-DCB patients well in the training and validation cohorts with an area under the curve (AUC) of 0.82, 95% CI: 0.75 to 0.88, and 0.75, 95% CI: 0.64 to 0.87, respectively. In this study, the predictive value of CRS was better than programmed cell death ligand-1 (PD-L1) expression (AUC of PD-L1 subset: 0.59, 95% CI: 0.50 to 0.69) or clinical model (AUC: 0.66, 95% CI: 0.51 to 0.81). After combining the clinical signature with CRS, the predictive performance improved further with an AUC of 0.837, 0.790 and 0.781 in training, validation and D2 cohorts, respectively. When predicting pathological response, CRS divided patients into a major pathological response (MPR) and non-MPR group (AUC: 0.76, 95% CI: 0.67 to 0.81). Moreover, CRS showed a promising stratification ability on overall survival (HR: 0.49, 95% CI: 0.27 to 0.89; p=0.020) and progression-free survival (HR: 0.43, 95% CI: 0.26 to 0.74; p=0.002). Conclusion By analyzing both tumorous and peritumoral regions of CT images in a radiomic strategy, we developed a non-invasive biomarker for distinguishing responders of ICIs therapy and stratifying their survival outcome efficiently, which may support the clinical decisions on the use of ICIs in advanced as well as patients with resectable NSCLC.

Glucagon‐like peptide‐1 (GLP‐1) regulates glycemic excursions by augmenting insulin production and inhibiting glucagon secretion. Liraglutide, a long‐acting GLP‐1 analog, can improve glycemic control for treating type 2 diabetes and prevent neutrophil extravasation in inflammation. Here, we explored the role of liraglutide in the development and therapy of murine lung and liver cancers. In this study, liraglutide substantially decreased circulating neutrophil extracellular trap (NET) markers myeloperoxidase, elastase, and dsDNA in Lewis lung cancer (LLC) and Hepa1‐6 tumor‐bearing mice. Furthermore, liraglutide downregulated NETs and reactive oxygen species (ROS) of neutrophils in the tumor microenvironment. Functionally, in vitro experiments showed that liraglutide reduced NET formation by inhibiting ROS. In addition, we showed that liraglutide enhanced the anti‐tumoral efficiency of programmed cell death‐1 (PD‐1) inhibition in LLC and Hepa1‐6 tumor‐bearing C57BL/6 mice. However, the removal of NETs significantly weakened the antitumor efficiency of liraglutide. We further demonstrated that the long‐term antitumor CD8⁺ T cell responses induced by the combination therapy rejected rechallenges by respective tumor cell lines. Taken together, our findings suggest that liraglutide may promote the anti‐tumoral efficiency of PD‐1 inhibition by reducing NETs in lung and liver cancers.

Purpose: The present study sets out to evaluate the feasibility, safety, and effectiveness of conversion surgery following induction immunochemotherapy for patients with initially unresectable locally advanced esophageal squamous cell carcinoma (ESCC) in a real-world scenario.Materials and Methods: In this multi-center, real-world study (NCT04822103), patients who had unresectable ESCC disease were enrolled across eight medical centers in China. All patients received programmed death receptor-1 (PD-1) inhibitor plus chemotherapy every 3 weeks for at least two cycles. Patients with significant relief of cancer-related clinical symptoms and radiological responsive disease were deemed surgical candidates. Feasibility and safety profile of immunochemotherapy plus conversion surgery, radiological and pathological tumor responses, as well as short-term survival outcomes were evaluated. Moreover, data of an independent ESCC cohort receiving induction chemotherapy (iC) were compared.Results: One hundred and fifty-five patients were enrolled in the final analysis. Esophagectomy was offered to 116 patients, yielding a conversion rate of 74.8%. R0 resection rate was 94%. Among the 155 patients, 107 (69.0%) patients experienced at least one treatment-related adverse event (TRAE) and 45 (29.0%) patients reported grade 3 and above TRAEs. Significant differences in responsive disease rate were observed between iC cohort and induction immunochemotherapy (iIC) cohort [objective response rate: iIC: 63.2% vs. iC: 47.7%, p = 0.004; pathological complete response: iIC: 22.4% vs. iC: 6.7%, p = 0.001). Higher anastomosis fistula rate was observed in the iC group (19.2%) compared with the iIC group (4%). Furthermore, Significantly higher event-free survival was observed in those who underwent conversion surgery.Conclusion: Our results supported that conversion surgery following immunochemotherapy is feasible and safe for patients with initially unresectable locally advanced ESCC. Both radiological and pathological response rates were significantly higher in the iIC cohort compared with those in the traditional iC cohort.

Neoadjuvant chemoimmunotherapy (NACI) has significantly improved survival in patients with resectable non-small cell lung cancer (NSCLC). However, with the currently available methods (PD-L1, RECIST), it is difficult to predict who will benefit from treatment before therapy and who will achieve pathological complete response (pCR) before surgery. Non-invasive methods to predict treatment response to NACI could be used to further personalize treatment. In the multicenter retrospective study, we enrolled 534 patients with NSCLC who received NACI followed by surgical resection at three Chinese hospitals between January 2019 and December 2024 (193 patients from Center A, 193 patients from Center B, and 148 patients from Center C). We developed and validated Lung Cancer Neo-adjuvant Immuno-Chemotherapy Response Predictor (LC-NICER), a CT-based Artificial Intelligence (AI) system that integrates longitudinal radiomics (tumor texture), deep learning (microenvironmental context), and habitat imaging (tumor and peritumoral subregional dynamics) to predict pCR by analyzing tumor spatiotemporal heterogeneity. The LC-NICER system consists of two complementary predictive models: LC-NICER_α, a pretreatment model that identifies patients likely to benefit from NACI to guide personalized therapy, and LC-NICER_δ, a preoperative model that evaluates tumor regression and resection feasibility to inform surgical planning. This study is registered at ClinicalTrials.gov (NCT06285058). Among the 386 patients from Center A and B, 308 were randomly assigned to the training dataset and 78 to the internal validation dataset, following an 8:2 split ratio. The 148 patients from Center C formed an independent and external test dataset. LC-NICER prediction system demonstrated excellent performance with the area under the curves (AUCs) of 0.950 (0.927–0.970) in the training cohort, 0.889 (0.796–0.961) in the internal validation cohort, and 0.870 (0.803–0.927) in the external test cohort. The LC-NICER_α achieved an accuracy of 0.722 (0.668–0.772) before therapy, while LC-NICER_δ showed significantly improved accuracy of 0.831 (0.800–0.861) before surgery. Notably, LC-NICER outperformed current clinical standards, with absolute accuracy improvements of 10% over PD-L1 testing (0.622 [0.564–0.683], p = 0.002) and 18% over RECIST 1.1 criteria (0.651 [0.610–0.693], p = 0.008). For easy clinical utility and research reproducibility, we developed and openly published a software. As a non-invasive AI system for predicting NACI response in NSCLC, LC-NICER may offer future clinical personalized therapeutic strategies, accelerate adaptive clinical trials, and optimize treatment decisions, potentially reducing reliance on invasive procedures. This work was supported by the Noncommunicable Chronic Diseases- 10.13039/501100018537 National Science and Technology Major Project (2024ZD0531100, 2024ZD0531101), the 10.13039/501100001809 National Natural Science Foundation of China (82472062, 62102103, 82473298, 82202148, 82502479), the 10.13039/501100012166 National Key Research and Development Program of China (2023YFC2508603), the 10.13039/501100003453 Natural Science Foundation of Guangdong Province of China (2024A1515011672), Regional Innovation and Development Joint Fund of 10.13039/501100001809 National Natural Science Foundation of China (U22A20345), Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (2022B1212010011).