Spatial transcriptomic analyses provide spatially resolved gene expression data that can provide insights into complex biological processes. However, current spatial transcriptomics approaches remain financially prohibitive and restricted in resolution, scalability, and gene coverage, limiting broader adoption for large-scale studies. Here, we developed CarHE (contrastive alignment of gene expression for hematoxylin and eosin images), a multimodal pretraining framework that infers high-dimensional spatial transcriptomic profiles from routine H&E-stained slides. By using contrastive learning to align cell type-specific transcriptomic information with histological features, CarHE achieved high prediction accuracy across evaluated datasets and spatial transcriptomics platforms. CarHE approximated spatially organized pathological microenvironment features consistent with tertiary lymphoid structure (TLS)-associated regions in breast cancer, lung cancer, melanoma, and clear cell renal cell carcinoma. Additionally, CarHE inferred approximated 3D spatial transcriptomic context from 2D images, providing more informative neighborhood context than 2D visualization. In a cohort of 880 lung cancer patients, CarHE-derived features were associated with disease-free survival and outperformed current approaches. Overall, CarHE provides a cost-effective and scalable framework for H&E-based spatial inference, supporting further validation toward translational research applications.

Objectives:To evaluate the long-term prognosis and related factors in patients who manifest part-solid invasive lung adenocarcinoma with difficult-to-measure solid portions. Methods:Patients with resected clinical stage I part-solid invasive lung adenocarcinoma in a single institution were retrospectively enrolled. They were divided into measurable and difficult-to-measure groups according to whether the radiologically solid portion was difficult to measure. The Kaplan-Meier method was used to evaluate the outcomes. Cox analysis was used to explore the prognostic factors. Tumor nodules were segmented using 3D Slicer software to extract quantitative features. Results:Of the 1240 patients, 225 (18.2%) were classified into the difficult-to-measure group. The intraclass correlation coefficient revealed that two readers had an excellent agreement on solid size in the measurable group (0.88), but not in the difficult-to-measure group (0.65). Compared with the measurable group, these tumors were larger and exhibited comparable overall survival (10 y 95.0% versus 94.2%; p = 0.341). The prognosis of the difficult-to-measure group was similar to that of cT1a or cT1b in the measurable group. consolidation-to-tumor ratio was an independent risk factor for patients with easy-to-measure solid components but not for patients whose solid parts were difficult to measure. Multivariate analyses revealed that tumor mass (>1.62 g) was an independent factor for worse recurrence-free survival in the measurable or difficult-to-measure group. Conclusions:Patients with invasive adenocarcinoma manifesting as part-solid nodules whose solid components were difficult to measure revealed favorable outcomes. Tumor mass was a better prognostic indicator than consolidation-to-tumor ratio for this population.

Precise localization of small pulmonary nodules is crucial for surgery. Augmented reality (AR) navigation provides real-time guidance, potentially improving localization efficiency. This open-label randomized controlled trial compares AR-guided localization with conventional computed tomography (CT)-guided localization in 168 participants with peripheral pulmonary nodules ≤2 cm. The primary outcome is localization accuracy; secondary outcomes are success rate, procedural time, radiation dose, insertion attempts, and complications. In the intention-to-treat population, the AR group shows significantly lower localization error than the CT group (4.50 ± 3.07 vs. 6.50 ± 3.54 mm), with shorter procedural time, lower radiation exposure, and fewer needle adjustments. Both groups show high success rates and similar complication rates, with no severe adverse events. AR-guided localization provides noninferior accuracy while reducing radiation exposure and procedural time, supporting an efficient alternative to CT guidance. The trial is registered at www.clinicaltrials.gov (NCT06335563).

Background: Small Cell Lung Cancer (SCLC) is a deadly cancer with few reliable prognostic biomarkers. Although systemic inflammation contributes to SCLC progression, no studies have combined cellular senescence biomarkers with inflammatory markers. We proposed that measuring both serum inflammatory cytokines and cell-free telomere length together could improve prognostic accuracy. Methods: We enrolled 187 patients with extensive-stage SCLC before they started first-line chemo-immunotherapy. At baseline, we measured serum levels of IL-6, IL8, TNF-a, CRP, and cell-free telomere length (cfTL). We used Cox regression to create an Inflamm-Ageing Index (IAI). The main outcome was overall survival (OS). Results: High IL-6 (HR 2.14, 95% CI 1.45-3.16, p<0.001) and short cfTL (HR 2.87, 95% CI 1.92-4.29, p<0.001) were each linked to worse overall survival. The IAI grouped patients into low-, intermediate-, and high-risk groups, with median OS of 14.2, 9.1, and 5.3 months, respectively (p<0.001). The IAI stayed an independent predictor after adjusting for other factors (HR 3.42, 95% CI 2.18-5.37, p<0.001) and outperformed individual biomarkers (C-index 0.78). Conclusion: After a median follow-up of 16.8 months and 148 events (79.1%), the Inflamm-Aging Index, which combines inflammatory cytokines and cellular senescence biomarkers, showed promise for risk stratification in extensive-stage SCLC. However, since the biomarker thresholds were set using this cohort, external validation with pre-set cutoffs is needed before clinical use.

The intricate molecular landscape within tissues holds crucial information about cellular behavior and disease progression, yet capturing this complexity at a spatial level remains challenging. While Spatial transcriptomics (ST) offers valuable insights into gene expression patterns within their native tissue context, its widespread adoption is hindered by high costs and limited gene detection capabilities. Here we introduce CarHE (Contrastive Alignment of gene expRession for hematoxylin and eosin image), a method that overcomes these limitations by accurately predicting high-dimensional ST data (over 10,000 genes) solely from readily available H&E (Hematoxylin and Eosin) stained images. This novel pre-trained architecture employs contrastive learning through two mechanisms: cell-type-based transcriptomics information transfer and image-based histology information transfer. These mechanisms precisely align image features with spatial single-cell gene expressions, achieving prediction accuracies exceeding 0.7 (up to 1.7 folds compared to second best) across diverse tissue types and species. CarHE’s superior performance extends to identifying subtle pathological features such as tertiary lymphoid structures in various cancers, including breast cancer, lung cancer, melanoma and ccRCC (clear cell renal cell carcinoma), and reconstructing 3D spatial transcriptomics from images alone, offering a cost-effective and robust alternative for large-scale spatial transcriptomics. We further validated CarHE’s effectiveness by predicting DFS (Disease-Free Survival) from >1,600 lung cancer patients HE images, achieving a significantly higher AUC (Area Under the Receiver Operating Characteristic Curve) of 0.73 compared to state-of-the-art alternatives (0.58-0.64). ### Competing Interest Statement The authors have declared no competing interest. National Key R&D Program of China, 2022YFA1004800, 2025YFF1207900 Natural Science Foundation of China, T2341007, T2350003, 12131020, 42450084, 42450135, 12326614, and 12426310 Zhejiang Province Vanguard Goose-Leading Initiative, 23JS1401300 Zhejiang Province Vanguard Goose-Leading Initiative, 2025C01114 JST Moonshot R&D, JPMJMS2021 Hangzhou Institute for advanced study of UCAS, 2024HIAS-P004

[This retracts the article DOI: 10.3892/etm.2016.3247.].

Objective: To evaluate the feasibility and safety of bronchial sleeve lobectomy (BSL) with pulmonary artery reconstruction (PAR) following neoadjuvant chemoimmunotherapy. Methods: Patients undergoing sleeve lobectomy or pneumonectomy after neoadjuvant chemoimmunotherapy between 2019 and 2023 were enrolled in the analysis retrospectively. Perioperative outcomes, overall survival, and event-free survival were compared among the BSL, BSL with PAR, and pneumonectomy (PN) groups before and after propensity score matching weights (MW). Results: In the overall cohort, 143 patients received simple BSL, 49 received BSL with PAR, and 48 underwent PN. There were marked differences in the distribution of age, clinical N stage, and tumor location among the 3 groups. After MW, BSL with PAR was associated with a higher thoracotomy rate (P = .011), longer operative time (P = .007), and more intraoperative blood loss (P < .001) compared with BSL and PN. The incidences of surgery-related complications (33.1% vs 49.3%) and severe complications (28.1% vs 45.8%) was lower and the hospital stay was shorter (median, 6 days vs 9 days) in the BSL with PAR group compared to the PN group. There was no significant discrepancy in postoperative complication rates between BSL with PAR and BSL after MW. Multivariable logistic regression analysis revealed that compared to PN, BSL with PAR served as a protective factor for both postoperative complications (P = .042) and severe complications (P = .014). Kaplan-Meier analysis further suggested that BSL with PAR was associated with favorable overall survival compared to PN after MW (P = .027). Conclusions: BSL with PAR can be recognized as a safe and feasible surgical procedure even after neoadjuvant chemoimmunotherapy.

Existing prognostic models are useful for estimating the prognosis of lung adenocarcinoma patients, but there remains room for improvement. In the current study, we developed a deep learning model based on histopathological images to predict the recurrence risk of lung adenocarcinoma patients. The efficiency of the model was then evaluated in independent multicenter cohorts. The model defined high- and low-risk groups successfully stratified prognosis of the entire cohort. Moreover, multivariable Cox analysis identified the model defined risk groups as an independent predictor for disease-free survival. Importantly, combining TNM stage with the established model helped to distinguish subgroups of patients with high-risk stage II and stage III disease who are highly likely to benefit from adjuvant chemotherapy. Overall, our study highlights the significant value of the constructed model to serve as a complementary biomarker for survival stratification and adjuvant therapy selection for lung adenocarcinoma patients after resection.

Objective: The potential survival benefits of adjuvant immunotherapy for resectable NSCLC after neoadjuvant chemoimmunotherapy, and the optimal number of adjuvant immunotherapy cycles, remain uncertain. This study aims to evaluate the prognostic impact of adjuvant immunotherapy and determine the optimal number of cycles. Methods: A total of 438 patients who received neoadjuvant chemoimmunotherapy between August 2019 and June 2022 across four hospitals were enrolled in this study, with a median follow-up time of 31.3 months. Recurrence-free survival (RFS) and overall survival (OS) were estimated using Kaplan-Meier methods and tested by log-rank test. Unstratified Cox proportional hazards models were fitted to the subgroups. Results: In this multi-center cohort, 29.7% of patients (n = 130) achieved a pathologic complete response. Patients who received adjuvant immunotherapy experienced significant survival benefits compared with those who did not (RFS: hazard ratio [HR] = 0.63, 95% confidence interval: 0.41- 0.98, p = 0.037; OS: hazard ratio = 0.27, 95% confidence interval: 0.13-0.57, p < 0.001). Subgroup analyses found that patients with a squamous histologic type, positive PD-L1 expression, and those with a major pathologic response particularly benefited from adjuvant immunotherapy. In addition, we found that six cycles of adjuvant immunotherapy served as a threshold for better prognostic differentiation, suggesting that six or more cycles may be more beneficial. Conclusions: Our study found that the addition of adjuvant immunotherapy to neoadjuvant chemoimmunotherapy is significantly associated with improved RFS and OS in patients with resectable NSCLC. We also identified that six cycles of adjuvant immunotherapy may be the optimal regimen for these patients.

Diagnosing lung cancer from indeterminate pulmonary nodules (IPLs) remains challenging. In this multi-institutional study involving 2032 participants with IPLs, we integrate the clinical, radiomic with circulating cell-free DNA fragmentomic features in 5-methylcytosine (5mC)-enriched regions to establish a multiomics model (clinic-RadmC) for predicting the malignancy risk of IPLs. The clinic-RadmC yields an area-under-the-curve (AUC) of 0.923 on the external test set, outperforming the single-omics models, and models that only combine clinical features with radiomic, or fragmentomic features in 5mC-enriched regions (p < 0.050 for all). The superiority of the clinic-RadmC maintains well even after adjusting for clinic-radiological variables. Furthermore, the clinic-RadmC-guided strategy could reduce the unnecessary invasive procedures for benign IPLs by 10.9% ~ 35%, and avoid the delayed treatment for lung cancer by 3.1% ~ 38.8%. In summary, our study indicates that the clinic-RadmC provides a more effective and noninvasive tool for optimizing lung cancer diagnoses, thus facilitating the precision interventions. Diagnosis of lung cancer from indeterminate pulmonary nodules remains challenging. Here, the authors develop a multi-omics signature to identify oncogenic nodules, and prevent unnecessary procedures.

BACKGROUND:The effect of KRAS mutant subtypes on the outcome and recurrence pattern of patients with nonadvanced lung cancer remains controversial. This study aimed to broadly elucidate the oncologic characteristics of G12C mutation in resected non-small cell lung cancer (NSCLC). METHODS:A total of 18,509 stage I-III NSCLC patients who received surgical resection and genetic assay were retrospectively enrolled. Paired cases of KRAS mutation and wild type were formed by propensity score matching. The Kaplan-Meier and Fine-Gray methods were used to describe the survival and recurrent differences. Multivariable analyses were used to control for confounders. RESULTS:KRAS mutation was detected in 1139 patients (6.2%), including 362 G12C and 777 non-G12C mutations. The G12C group showed more male, smoker, and high-grade dominated adenocarcinoma cases than KRAS wild type and non-G12C groups. In the matched cohort, multivariable analyses revealed that G12C mutation was a high-risk factor for time-to-relapse (hazard ratio [HR] vs wild type, 1.30, P = .018; HR vs non-G12C, 1.45, P = .002), lung cancer-specific survival (HR vs wild type, 1.49, P = .004; HR vs non-G12C, 1.45, P = .009), and overall survival (HR vs wild type, 1.39, P = .009; HR vs non-G12C, 1.30, P = .048), independent of clinicopathologic characteristics. G12C-mutated tumors were more likely to relapse rapidly, as well as to develop distant and extrathoracic metastatic recurrences. Both G12C and non-G12C mutations resulted in shorter postrecurrence survival. CONCLUSIONS:KRAS G12C mutation is associated with adverse outcomes and aggressive recurrence patterns for resected NSCLC. Effective perioperative therapies and close postoperative monitoring strategies might be implemented in this population.

Purpose To elucidate the potential reasons for the favourable prognosis of positron emission tomography/computed tomography (PET/CT)-defined occult N2 metastasis and its survival effect in the context of the newly proposed ninth edition N descriptors. Methods A total of 3565 patients who underwent preoperative PET/CT and surgical resection for non-small cell lung cancer were retrospectively included. Survival analysis was conducted using the Kaplan-Meier method and Cox proportional hazards model. Results The incidence of single-station involvement was significantly higher (p < .001) in occult N2 metastasis (117/191, 61.3%) compared to evident N2 metastasis (83/198, 41.9%). The survival rates of patients with occult N2a (single-station N2 involvement) and occult N2b (multiple-station N2 involvement) were comparable to those of patients with clinically evident N2a and N2b, respectively (adjusted p >.20 for all). Conversely, single-station involvement was associated with a markedly superior prognosis than multiple-station involvement, whether for patients with occult N2 metastasis (5-year overall survival [OS]: 62.7% vs 50.1%, adjusted p = .04) or patients with clinically evident N2 metastasis (5-year OS: 50.3% vs 36.2%, adjusted p = .03). Cox regression analysis of the pathological N2 population further indicated that multiple-station involvement was a more robust prognostic factor than occult lymph node metastasis. Conclusions The favourable prognosis of PET/CT-defined occult N2 metastasis may be attributed to the discrepancy in prognosis and proportion between occult N2a and clinically evident N2b. This external validation provided substantial evidence supporting the reasonableness and robustness of the newly proposed ninth edition N descriptors.

Diagnosing lung cancer at a curable stage offers the opportunity for a favorable prognosis. The emerging epigenomics analysis on plasma cell-free DNA (cfDNA), including 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) modifications, has acted as a promising approach facilitating the identification of lung cancer. And, integrating 5mC biomarker with chest computed tomography (CT) image features could optimize the diagnosis of lung cancer, exceeding the performance of models built on single feature. However, the clinical applicability of integrated markers might be limited by the potential risk of overfitting due to small sample size. Hence, we prospectively collected peripheral blood sample and the paired chest CT images of 2032 patients with indeterminate pulmonary nodules across 5 centers, and constructed a large-scale, multi-institutional, multiomics database that encompass CT imaging data and plasma cfDNA fragmentomic in 5mC-, 5hmC-enriched regions. To our best knowledge, this dataset is the first radio-epigenomic dataset with the largest sample size, and provides multi-dimensional insights for early diagnosis of lung cancer, facilitating the individuated management for lung cancer.

OBJECTIVE:We aimed to investigate the prognostic significance of ROS1 fusion in surgically resected lung adenocarcinoma (LUAD). MATERIALS AND METHODS:Consecutive patients who underwent complete resection and ROS1 testing between 2015 and 2020 were included. Propensity score matching (1:2) was applied to balance baseline characteristics. Disease-free survival (DFS), overall survival (OS), and cumulative incidence of recurrence (CIR) were compared overall and by TNM stage. Recurrence patterns and post-recurrence survival (PRS) in ROS1 fusion-positive patients were also analyzed. RESULTS:Overall, 16,779 patients met the inclusion criteria (216 ROS1 fusion-positive and 16,563 ROS1 fusion-negative). After matching, 216 ROS1 fusion-positive and 432 ROS1 fusion-negative patients were included in survival analysis. In the overall cohort, the ROS1 fusion-positive group had similar DFS (5-year: 72.7 % vs. 66.4 %; p = 0.074) and OS (5-year: 87.6 % vs. 80.7 %; p = 0.055) compared with theROS1fusion-negative group. Subgroup analysis showed that ROS1 fusion was associated with better DFS (hazard ratio [HR], 0.609; 95 % confidence interval [CI], 0.376-0.986; p = 0.043) and OS (HR, 0.481; 95 % CI, 0.242-0.958; p = 0.037) in stage I, with similar outcomes in stages II-III. In addition, recurrence patterns and CIR analyses were consistent with these findings. Among patients who experienced recurrence, ROS1 tyrosine kinase inhibitors (TKIs) significantly improved PRS (median PRS: 65 vs. 20 months; p < 0.001). In multivariate Cox regression analysis, ROS1-TKI therapy remained an independent protective factor for PRS (HR = 0.259; 95 % CI, 0.103-0.647; p = 0.004). CONCLUSION:ROS1 fusion was associated with better prognosis in stage I LUAD, and ROS1-TKI therapy conferred a survival advantage after recurrence.

BACKGROUND:This study evaluated whether buttressing the bronchial anastomosis with an autogenous pedicled flap has short- and long-term advantages for patients undergoing sleeve lobectomy. METHODS:Consecutive patients who underwent bronchial sleeve lobectomy for centrally located non-small cell lung cancer were retrospectively identified. Perioperative outcomes, recurrence-free survival, and overall survival were compared between those who received anastomosis coverage and those who did not before and after stable inverse probability of treatment weighting (IPTW). RESULTS:The study included 682 patients. Among them, 211 patients (30.9%) received anastomosis coverage (143 pleura, 39 intercostal muscle, and 29 pericardial or vein flap), and the other 471 (69.1%) did not. Perioperative outcomes were comparable except for more operative time (P = .028) in patients with anastomosis coverage after IPTW. Multivariable logistic regression analysis revealed that buttressing the anastomosis did not lead to fewer postoperative complications (odds ratio, 0.95; 95% CI, 0.60-1.52; P = .842). No significant difference was observed in long-term survival between the 2 groups before and after IPTW. Multivariable Cox regression analysis demonstrated that wrapping the anastomosis was not associated with favorable long-term recurrence-free survival (hazard ratio, 1.04; 95% CI, 0.81-1.35, P = .737) or overall survival (hazard ratio, 0.97; 95% CI, 0.72-1.30; P = .819) for patients undergoing sleeve lobectomy in the IPTW-adjusted cohort. CONCLUSIONS:Our results indicated that buttressing the bronchial anastomosis does not reduce the incidence of postoperative complications or confer survival benefits to patients undergoing sleeve lobectomy.

Objective: Accurately predicting response during neoadjuvant chemoimmunotherapy for resectable non-small cell lung cancer remains clinically challenging. In this study, we investigated the effectiveness of blood-based tumor mutational burden (bTMB) and a deep learning (DL) model in predicting major pathologic response (MPR) and survival from a phase 2 trial. Methods: Blood samples were prospectively collected from 45 patients with stage IIIA (N2) non-small cell lung cancer undergoing neoadjuvant chemoimmunotherapy. An integrated model, combining the computed tomography-based DL score, bTMB, and clinical factors, was developed to predict tumor response to neoadjuvant chemoimmunotherapy. Results: At baseline, bTMB were detected in 77.8% (35 of 45) of patients. Baseline bTMB >= 11 mutations/megabase was associated with significantly greater MPR rates (77.8% vs 38.5%, P = .042), and longer disease-free survival (P = .043), but not overall survival (P = .131), compared with bTMB <11 mutations/megabase in 35 patients with bTMB available. The developed DL model achieved an area under the curve of 0.703 in all patients. Importantly, the predictive performance of the integrated model improved to an area under the curve of 0.820 when combining the DL score with bTMB and clinical factors. Baseline circulating tumor DNA (ctDNA) status was not associated with pathologic response and survival. Compared with ctDNA residual, ctDNA clearance before surgery was associated with significantly greater MPR rates (88.2% vs 11.1%, P < .001) and improved disease-free survival (P = .010). Conclusions: The integrated model shows promise as a predictor of tumor response to neoadjuvant chemoimmunotherapy. Serial ctDNA dynamics provide a reliable tool for monitoring tumor response.

This study aimed to establish a novel quantification system of anoikis and angiogenesis related genes (AAGs) and comprehensively analyze the relationship between AAG signature score (AAGscore) and the prognosis, tumor immune microenvironment, and therapeutic response in LUAD. Univariate Cox regression analysis was used to screen prognosis-related AAGs. A consensus clustering algorithm was applied for AAG subtypes identification on 750 LUAD samples from TCGA and GEO databases. The differences in prognosis, immune infiltration, and therapeutic response were evaluated among the subtypes. The AAG signature scoring system was constructed by a principal component analysis algorithm. Cluster-A demonstrated a high gene expression and stromal-score with a poor prognosis. Our results showed that there were significant differences in survival time, mutation frequency, expression of chemokines and receptors, expression of immune checkpoint related genes, immunotherapy efficacy and antitumor drug sensitivity between high- and low-AAGscore groups. Survival analysis revealed that patients in the high-AAGscore group had better prognosis. We also discovered that the AAGcluster-B and geneCluster-2 subtype showed higher AAGscores. In addition, a number of conventional antitumor drugs were selected to test the sensitivity of high- and low-AAGscore groups to drug therapy. In summary, we constructed molecular subtypes and AAGscores of LUAD based on AAGs. The risk score model can be used to predict the prognosis of LUAD patients and the efficacy and sensitivity of antitumor drugs.

Typhoid and paratyphoid fever are common infectious diseases and remain a heavy burden, especially in some low-income countries. Although the global burden has decreased over the past three decades, an analysis of the burden of typhoid and paratyphoid fever will help inform public health strategies. This study is aimed to comprehensively evaluate the global, regional, and national burden of typhoid and paratyphoid, and the temporal trends while exploring potential associations with socio-demographic development over three decades (1990–2021). Data on typhoid and paratyphoid fever were analyzed using the Global Burden of Disease (GBD) study in 2021. For this analysis, we calculated to demonstrate temporal trends in the incidence, mortality, and disability adjusted life years (DALYs) of typhoid and paratyphoid fever from 1990 to 2021. From 1990 to 2021, both typhoid and paratyphoid fever showed declining trends globally and in different socio-demographic index (SDI) regions, including incidence, mortality, and DALYs. For typhoid fever worldwide, new cases decreased by 62.12

Ulcerative colitis (UC) is a chronic, recurrent inflammatory bowel disease. UC confronts with severe challenges including the unclear pathogenesis and lack of specific diagnostic markers, demanding for identifying predictive biomarkers for UC diagnosis and treatment. We perform immune infiltration and weighted gene co-expression network analysis on gene expression profiles of active UC, inactive UC, and normal controls to identify UC related immune cell and hub genes. Neutrophils, M1 macrophages, activated dendritic cells, and activated mast cells are significantly enriched in active UC. MMP-9, CHI3L1, CXCL9, CXCL10, CXCR2 and S100A9 are identified as hub genes in active UC. Specifically, S100A9 is significantly overexpressed in mice with colitis. The receiver operating characteristic curve demonstrates the excellent performance of S100A9 expression in diagnosing active UC. Inhibition of S100A9 expression reduces DSS-induced colonic inflammation. These identified biomarkers associated with activity in UC patients enlighten the new insights of UC diagnosis and treatment.