Background: Preoperative prediction of right recurrent laryngeal nerve (RLN) lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) is essential for balancing oncological clearance and nerve preservation. However, conventional computed tomography (CT) imaging based on size criteria exhibits poor sensitivity, frequently <50%, often failing to detect micrometastases or small metastatic nodes. Unlike general lymph node models, this study focuses specifically on the right RLN station to provide a targeted tool for surgical planning in this anatomically complex region. Methods: This retrospective study analyzed 413 consecutive patients who underwent radical esophagectomy. The gold standard for metastasis was established through site-specific histopathological examination of the right RLN lymph nodes, which were meticulously dissected and separately labeled during surgery to ensure direct correlation with preoperative CT imaging. Patients were divided into training (n=289) and test (n=124) sets. Radiomic features (n=945), comprising first-order statistics, shape, and texture features, were extracted from arterial-phase CT images of right RLN lymph nodes. Six machine learning algorithms [logistic regression (LR), K-nearest neighbors (KNN), decision tree (DT), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost)] were trained. Clinical risk factors were identified via univariate/multivariate LR. A combined model fused radiomics scores and clinical factors. Performance was evaluated using ROC curves, Hosmer-Lemeshow calibration, and decision curve analysis (DCA). Results: Eleven radiomic features were selected for modeling. The LR-based radiomics model achieved an area under the curve (AUC) of 0.87 [95% confidence interval (CI): 0.83-0.92] in the training set and 0.82 (95% CI: 0.71-0.90) in the test set. Lymph node short axis (P<0.001) and tumor differentiation (P=0.01) were independent clinical predictors. The combined model demonstrated superior performance, with AUCs of 0.90 (95% CI: 0.85-0.94, training) and 0.86 (95% CI: 0.78-0.94, test). Calibration curves confirmed agreement between predicted and observed outcomes (Hosmer-Lemeshow P>0.05). DCA indicated clinical utility across threshold probabilities. Conclusions: An integrated model combining CT radiomics and clinical factors effectively predicts right RLN LNM in ESCC. This tool may guide surgical strategies and adjuvant therapy decisions, enhancing personalized treatment.

Carinal reconstruction is the primary surgical intervention for tracheal tumors involving the tracheal carina. However, the complexity of airway management and the challenges associated with invasive carina reconstruction significantly increase its operative difficulty. A 48-year-old male patient presented with a 6-month history of persistent cough. Cervical and thoracic computed tomography (CT) imaging, along with bronchoscopic biopsy, confirmed a diagnosis of tracheal adenoid cystic carcinoma (TACC), with the tumor extending to the tracheal carina and left main bronchus. The patient subsequently underwent robot-assisted carinal resection and reconstruction using a three-port approach under extracorporeal membrane oxygenation (ECMO) support. Intraoperative oxygen saturation remained stable, and the postoperative course was uneventful. This case suggests that that the combination of ECMO support and robotic assistance facilitates adequate oxygenation and enables a minimally invasive, safe and efficient approach to carinal resection and reconstruction. Further studies and broader clinical experience across multiple centers are required to validate the safety and practicality of this technique.

Ubiquitin-specific proteases (USPs) function in multiple types of tumor progression by controlling protein turnover. However, the regulatory mechanism of ubiquitin carboxyl-terminal hydrolase 44 (USP44) in esophageal squamous cell carcinoma (ESCC) remains incompletely understood. Here, we show that USP44 expression correlates with tumor size, tumor stage, and TNM stage in ESCC patients. In vitro gain- and loss-of-function experiments validate that USP44 acts as a tumor suppressor in ESCC. Xenograft assays further demonstrate that USP44 overexpression suppresses the growth of ESCC xenografts, as well as lymph node and lung metastasis. Analysis of collected ESCC tumor samples reveals downregulated USP44 expression and upregulated USP44 pan-succinylation, along with two opposing correlations: a negative correlation with Carnitine O-palmitoyltransferase 1 (CPT1A) and a positive correlation with CDK5 regulatory subunit-associated protein 3 (CDK5RAP3). Mechanistic studies identify CPT1A as a lysine succinyltransferase that specifically succinylates USP44 at lysine 543 (K543). Such CPT1A-mediated K543 succinylation triggers ubiquitination-dependent degradation of USP44, thus explaining the downregulation of USP44 observed in clinical ESCC samples. Moreover, ubiquitination proteomics combined with survival analysis identifies CDK5RAP3 as a key deubiquitination substrate of USP44. USP44 directly interacts with CDK5RAP3 and stabilizes CDK5RAP3 by removing its ubiquitin chains at lysine 166 (K166), in a manner dependent on the catalytic cysteine 282 (C282) within its USP domain. In rescue experiments, CDK5RAP3 overexpression reverses the promotion of malignant behavior in ESCC cells and the activation of the Wnt/β-catenin signaling pathway caused by USP44 silencing. Collectively, USP44 may serve as a promising therapeutic target for ESCC.

Introduction:The role of sarcopenia in cancer treatment remains to be fully documented. This umbrella meta-analysis aimed to investigate the impact of sarcopenia on therapeutic outcomes across common malignancies with high incidence or mortality. Methods:We conducted an umbrella review of existing meta-analyses based on cohort studies. PubMed, Web of Science, and Embase were systematically searched from inception to October 2025. The primary endpoints were survival outcomes after surgical or first-line treatments. Supplementary meta-analyses were performed for outcomes with insufficient existing evidence. The systematic review protocol was registered in PROSPERO (CRD42022383726). Results:A total of 59 meta-analyses were included, comprising 49 from the literature search and 10 newly conducted in this study, covering 12 cancer types. In patients undergoing surgery, sarcopenia was significantly associated with shorter overall survival (HR = 1.59, 95% CI: 1.37-1.88) and disease-free survival (HR = 1.64, 95% CI: 1.39-1.93), as well as increased risks of any postoperative complications (OR = 1.48, 95% CI: 1.21-1.81) and major complications (OR = 1.51, 95% CI: 1.30-1.76). In patients receiving first-line non-surgical therapy, sarcopenia remained an independent risk factor for poorer overall survival (HR = 1.49, 95% CI: 1.39-1.61) and progression-free survival (HR = 1.39, 95% CI: 1.17-1.66). The methodological quality of included meta-analyses was generally high, while the GRADE certainty of evidence for most outcomes was rated as low or very low. Sarcopenia was also associated with inferior survival after cancer immunotherapy. Discussion:Sarcopenia is consistently associated with adverse therapeutic outcomes across multiple cancers and treatment modalities. It warrants attention in clinical management and represents a promising target for improving cancer therapeutic outcomes. Systematic review registration:https://www.crd.york.ac.uk/prospero/, identifier CRD42022383726.

Abstract Background: Previous MCED studies have shown differences in detection performance across different aggressiveness cancer subtypes. However, the methylation profiles and ctDNA shedding patterns across different aggressiveness subtypes have not been analyzed. We systematically compared cell-free DNA (cfDNA) tumor fraction and methylation profiles across aggressiveness subtypes in three cancers to identify epigenetic biomarkers that differentiate tumor aggressiveness. Methods: Blood samples from a case-control study (NCT06217900) including 757 cancer cases were analyzed using a targeted methylation-based MCED test. We compared cfDNA tumor fraction and methylation profiles between aggressiveness subtypes, stage-matched small cell lung cancer (SCLC) (n=137) versus (vs) non-small cell lung cancer (NSCLC) (n=137), stage I invasive lung adenocarcinoma (IAC) (n=81) vs minimally invasive adenocarcinoma (MIA) (n=81), triple-negative breast cancer (TNBC) (n=151) vs non-TNBC (n=151), and intermediate/high-grade prostate cancer (Gleason Grade Group[GG] 2-5)(n=14) vs low-grade prostate cancer(GG1)(n=6). Tumor fraction was estimated using zero-inflated Negative Binomial model based on the distribution of methylation signals, and methylation profiles were assessed by calculating methylated/unmethylated (M/U) ratios between different aggressiveness subtypes. Marker comparisons were conducted using the Mann-Whitney U test with multiple testing corrections. Results: The sensitivity is higher in more aggressive subtypes (lung cancer 93.73% vs SCLC 99.27%; breast cancer 70.76% vs TNBC 81.46%; prostate cancer 40% vs intermediate and high-grad prostate cancer 42.86%). In lung cancer, SCLC exhibited a significantly higher cfDNA tumor fraction than NSCLC (Wilcoxon p = 2.24×10-24), with 41,136 markers (79.18% hypomethylated) showing elevated M/U ratios. In stage I lung adenocarcinoma, IAC demonstrated a higher cfDNA tumor fraction (Wilcoxon p = 2.55×10-6) and systematic methylation differences compared to MIA. In breast cancer, TNBC had a significantly higher cfDNA tumor fraction than non-TNBC (Wilcoxon p = 2.55×10-6), with 25,717 markers (80.32% hypomethylated) displaying increased M/U ratios. Among prostate cancers of the same stage, GG2-5 cases exhibited higher cfDNA tumor fraction (Wilcoxon p = 0.038) and systematic methylation alterations compared to GG1. Conclusion: Aggressive tumor subtypes consistently display elevated cfDNA tumor fraction and characteristic methylation profiles marked by increased M/U ratios. These findings suggest cfDNA methylation patterns are promising epigenetic biomarkers for distinguishing tumor aggressiveness, potentially improving cancer screening precision and reducing overdiagnosis. Citation Format: Kezhong Chen, Jian Huang, Dahong Zhang, Shu Wang, Hongxu Liu, Wenzhao Zhong, Xiangnan Li, Qiang Zhang, Zhigao Li, Jiaqi Liu, Ziqing Tian, Fei Zhou, Gongsheng Jin, Xudong Xiang, Zhigang Li, Hui Xie, Ya Wei, Guochun Zhang, Guolin Ye, Ming Cai, Junfeng Wang, Yan Zhang, Chao Cheng, Hefei Li, Desong Yang, Jianhong Lian, Sheng Huang, Tao Xu, Zengjun Wang, Xi Guo, Zhuowei Liu, Minfeng Chen, Yang Wang, Yue An, Yanzhan Yang, Min Li, Jing Liu, Baoliang Zhu, Yonghui Li, Xiaohui Wu, Fan Yang, Jun Wang. Epigenetic profiling identifies markers of aggressive cancer subtype using a targeted methylation-based multi-cancer early detection (MCED) blood test [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1100.

BACKGROUND AND AIMS:Circumferential endoscopic submucosal dissection (C-ESD) is increasingly used for circumferential superficial esophageal squamous cell carcinoma (C-ESCC), although outcome data remain sparse. This study aimed to evaluate the short- and long-term outcomes of C-ESD in patients with C-ESCC. METHODS:Clinical outcomes of patients with C-ESCC undergoing C-ESD at 2 Chinese centers were analyzed. Short-term outcomes included en bloc resection rate, curative resection rate, operation time, and adverse events. Long-term outcomes comprised overall survival (OS), disease-specific survival (DSS), cumulative recurrence rate (CRR), and the efficacy of stricture prevention and management. RESULTS:A final assessment was conducted in 100 patients (100 lesions). The median lesion length was 5.2 cm (IQR, 4.5-6.0). Technical success rates were as follows: en bloc resection, 92.0% (95% CI, 85.0-96.4); R0 resection, 84.0% (95% CI, 75.6-90.5); and curative resection, 81.0% (95% CI, 72.3-88.3). The median procedure duration was 97.5 minutes (IQR, 70.5-138.3). Adverse events included delayed bleeding (3.0%), perforation (1.0%), and strictures (88.0%). With a median follow-up of 47.4 months (IQR, 34.3-65.2), 3- and 5-year OS and DSS were identical at 95.6% (95% CI, 91.4-99.9). Cumulative recurrence increased from 16.2% (3 years) to 19.8% (5 years). Prophylactic measures reduced stricture incidence (80% vs 100%; P = .007) and median number of endoscopic dilation sessions, 3 (IQR, 1-6) vs 4 (IQR, 3-9.8); P = .028. Stricture resolution was achieved in 95.5% of cases. CONCLUSIONS:C-ESD is an effective treatment for C-ESCCs. Although it achieves reliable oncological control with a favorable prognosis, its value is tempered by high stricture rates and a nonnegligible recurrence risk (∼20%). To optimize patient outcomes, advancing stricture prevention and ensuring rigorous long-term management are crucial future directions.

Background: Lung squamous cell carcinoma (LUSC) remains a highly lethal malignancy due to its propensity for metastasis and the limited availability of reliable prognostic biomarkers. Anoikis, a detachment-induced cell death mechanism, plays dual roles in tumor suppression and metastasis, yet its prognostic implications in LUSC are poorly characterized. This study aimed to develop an anoikis-related gene (ARG) signature for risk stratification and therapeutic exploration in LUSC. Methods: Transcriptomic data from The Cancer Genome Atlas (TCGA)-LUSC (n=496) and Gene Expression Omnibus (GEO) cohorts (GSE3141/GSE73403; n=121) were analyzed. Differentially expressed ARGs were identified using limma package. Prognostic genes were screened via univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct a risk model. Immune infiltration was evaluated using CIBERSORT, single-sample Gene Set Enrichment Analysis (ssGSEA), and ESTIMATE. Experimental validation included CSNK2A1 knockdown in LUSC cell lines (SK-1, H226) to assess migration, invasion, and proliferation. Drug sensitivity and molecular docking were performed using oncoPredict and AutoDock Vina. Results: A total of 307 differentially expressed ARGs were identified, with 37 showing prognostic significance. Unsupervised clustering divided LUSC into two subgroups with distinct survival (P<0.001) and immune microenvironments. Cluster B exhibited higher immune and stromal scores but poorer outcomes, enriched in exhausted CD8(+) T cells and M2 macrophages. A six-gene risk signature (AKT2, CSNK2A1, CD151, TDGF1, BAG4, SNAI1) stratified patients into high-/low-risk groups, validated across cohorts (5-year AUC: 0.642-0.728). High-risk patients showed sensitivity to Wee1 inhibitors (P<0.001). CSNK2A1 knockdown suppressed migration, invasion, and proliferation in vitro. A nomogram integrating risk score and T-stage improved prognostic accuracy [5-year area under the curve (AUC): 0.728].

Abstract Background: Methylation-based analysis of cell-free DNA (cfDNA) has emerged as a key technology for MCED. However, existing approaches rely on traditional machine learning algorithms, which inherently limit detection performance. With the rapid advancement of artificial intelligence (AI), we have developed Genie-ADLA, a deep learning algorithm designed specifically for MCED. By integrating state-of-the-art deep neural network architectures with the intrinsic patterns inherent in methylation data, Genie-ADLA significantly enhanced MCED performance. Methods: Genie-ADLA was trained and evaluated on a dataset of 4,781 participants aged 40-75 years, including 2,702 pathologically confirmed cancer cases across 16 cancer types and 2,079 non-cancer controls (NCT06217900). The training set comprised 3,217 samples (1,756 cancer cases and 1,461 non-cancer controls), and the model’s performance was evaluated on an independent test set of 1,564 samples (618 non-cancer controls and 946 cancer cases). To address challenges inherent to methylation data—high dimensionality, sparsity, and noise—we applied feature dimensionality reduction and embedding strategies, reducing computational burden, mitigating overfitting, and improving learning efficiency. An ensemble learning approach further strengthened robustness and generalization. Results: Across all stages of 16 cancer types, Genie-ADLA achieved an overall sensitivity of 63.43% (600/946, 95% CI: [60.26%, 66.50%]) at 99.3% (612/618, 95% CI: [97.90%, 99.64%]) specificity in the test cohort. Compared with the XGBoost model trained on the same dataset, Genie-ADLA demonstrated improved overall sensitivity in 11 of the 16 cancer types, with an average increase of 4.86%.For stage I-III cancer patients, the sensitivities at 99.3% specificity showed notable gains over XGBoost: colorectal cancer achieved 76.98% (97/126, 95% CI: [68.65%, 84.01%]), an improvement of 9.52% from 67.46%; esophageal cancer reached 80.95% (51/63, 95% CI: [69.09%, 89.75%]), up 6.35% from 74.60%; breast cancer reached 37.14% (26/70, 95% CI: [25.89%, 49.52%]), improving by 5.71% from 31.43%. Lung cancer was subdivided into adenocarcinoma and non-adenocarcinoma, with stage I-III sensitivities of 40.90% (27/66, 95% CI: [28.95%, 53.71%]) in adenocarcinoma, an increase of 10.6%, and 84.44% (38/45, 95% CI: [70.54%, 93.51%]) in non-adenocarcinoma, improving by 2.22%. Conclusions: Genie-ADLA, leveraging advanced deep neural network architectures and data processing strategies, substantially elevates the performance ceiling of methylation-based early cancer detection, offering a new paradigm for AI-driven cancer screening. Citation Format: Kezhong Chen, Ziyu Li, Xiaojian Wu, Jian Huang, Guoyue Lv, Weiping Wen, Dahong Zhang, Xiangyu Zhao, Danbo Wang, Zhihua Liu, Lixin Sun, Shu Wang, Xiangnan Li, Zhigang Li, Jiandong Tai, Jiayin Yang, Zhentong Wei, Ming Cai, Qiang Zhang, Songbing He, Shuhua Yi, Shenhong Qu, Wenhui Zhao, Xianjun Yu, Ruixia Guo, Jianhong Lian, Desong Yang, Huaiwu Lu, Xi Guo, Yan Zhang, Zhuowei Liu, Yingjiang Ye, Chang Lin, Jie Gao, Xuanhui Liu, Yushu Guo, Suying Ding, Guoqiang Zhao, Yanzhan Yang, Jiangyu Li, Shiqing Chen, Hui Yu, Fang Liu, Yang Wang, Min Li, Baoliang Zhu, Yonghui Li, Xiaohui Wu, Fan Yang, Jun Wang. Genie-ADLA: A deep learning algorithm for methylation-based multiple cancer early detection (MCED) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5471.

3047 Background: Given that distinct pathological and molecular subtypes of most cancers correspond to different treatment strategies, accurate cancer subtyping is clinically critical. Currently, pathological and molecular subtyping relies primarily on tissue biopsy—a procedure that is not always clinically feasible. Additionally, tumor heterogeneity may compromise the accuracy of subtyping via this approach. Prior studies have shown that cfDNA methylation-based detection is tissue-independent and outperforms mutation-based assays in cancer subtyping. Here, we employed a previously developed targeted methylation MCED assay to further classify the subtypes of lung cancer, breast cancer, and NHL. Methods: Pretreatment blood samples from lung cancer, breast cancer, and NHL patients enrolled in a MCED study were analyzed via the targeted methylation assay. Only samples with detected circulating tumor DNA (ctDNA) were included. The training cohort comprised lung cancer (n=529: 227 adenocarcinomas, 191 squamous cell carcinomas, 111 small cell carcinomas), breast cancer (n=359: 92 triple-negative breast cancers [TNBC], 267 non-TNBC cases), and NHL (n=157: 145 B-cell lymphomas, 12 T-/NK-cell lymphomas). The validation cohort included lung cancer (n=194: 87 adenocarcinomas, 82 squamous cell carcinomas, 25 small cell carcinomas), breast cancer (n=87: 26 TNBC, 61 non-TNBC cases), and NHL (n=60: 48 B-cell lymphomas, 12 T-/NK-cell lymphomas). cfDNA methylation profiles were analyzed to further classify the subtypes of these three malignancies. Results: In the validation cohort, the lung cancer classification model exhibited high overall accuracy 91.8% (178/194), with subtype-specific accuracies of 94.3% (82/87) for adenocarcinoma, 89.0% (73/82) for squamous cell carcinoma, and 92.0% (23/25) for small cell carcinoma. The breast cancer model achieved an overall accuracy of 80.5% (70/87), including 65.4% (17/26) for TNBC and 86.9% (53/61) for non-TNBC. For NHL, the model yielded an overall accuracy of 96.7% (58/60), with 97.9% (47/48) for B-cell lymphoma and 91.7% (11/12) for T-/NK-cell lymphoma. Conclusions: The cfDNA methylation-based MCED assay facilitates accurate subtype prediction for common malignancies without the need for invasive tissue biopsy procedures. This assay achieves high accuracy in lung cancer and NHL, whereas relatively lower accuracy is observed for breast cancer, particularly TNBC, owing to its reliance on molecular subtyping. Future studies will validate this assay in larger cohorts and extend its utility to a broader spectrum of cancer types.

OBJECTIVE:Pathologic complete response (pCR) after neoadjuvant therapy predicts favorable outcomes in esophageal squamous cell carcinoma (ESCC). In this era of neoadjuvant immunochemotherapy (nICT), the prognosis of patients achieving nICT-induced pCR remains unclear. This study aimed to characterize recurrence patterns and identify prognostic factors in this population. METHODS:A multicenter retrospective cohort study was conducted across 26 Chinese centers from 2019 to 2023. Patients with ESCC who underwent surgery after nICT and achieved pCR were included. Prognostic factors for recurrence-free survival (RFS) and overall survival (OS) were evaluated using Cox regression analysis. RESULTS:Among 2135 patients receiving nICT, 474 (22.2%) achieved pCR. After a median follow-up of 32.8 months, 60 patients (12.7%) experienced recurrence, with a median interval of 17.8 months (interquartile range, 8.7-26.7) after surgery. Most recurrences (75%) occurred within 2 years, predominantly as distant metastases (63.3%), with the lung being the most common site. The 2-year RFS and OS were 89.6% and 92.1%, respectively. Advanced clinical nodal stage (cN2-3) was identified as an independent prognostic factor for inferior RFS (adjusted hazard ratio, 1.83; 95% CI, 1.10-3.05; P = .02) but not OS, whereas adjuvant treatment was not associated with improved survival (adjusted hazard ratio, 1.29; 95% CI, 0.69-2.42; P = .42). CONCLUSIONS:Patients with ESCC achieving pCR after nICT exhibited excellent short-term survival but a persistent risk of distant recurrence. Advanced clinical nodal stage is associated with higher recurrence risk, which warrants further validation. Risk-adapted postoperative management may be preferable to routine adjuvant treatment.

Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality. N4-acetylcytidine (ac4C) modification regulates mRNA stability and translation, but the role of its associated co-factor, THUMP domain-containing protein 1 (THUMPD1), in cancer is unknown. Clinical LUAD samples and Gene Expression Omnibus (GEO) datasets were analyzed for THUMPD1 expression and prognosis. In vitro and in vivo functional assays were performed to assess the impact of THUMPD1 on LUAD. Multi-omics approaches and mechanistic studies were employed to identify downstream targets and signaling pathways. THUMPD1 was significantly downregulated in advanced-stage LUAD. THUMPD1 acted as a tumor suppressor, inhibiting LUAD cell proliferation, metastasis, and tumor growth in mouse models. Mechanistically, THUMPD1 directly bound to and upregulated the translation of insulin-like growth factor 2 receptor (IGF2R) mRNA in an ac4C-independent manner by facilitating its cytoplasmic localization. The THUMPD1-IGF2R axis inhibited AKT signaling, which in turn led to the activation of AMPK. This resulted in intracellular Cu+ accumulation, triggering cuproptosis and excessive mitophagy, ultimately suppressing tumor growth. Therapeutically, the copper ionophore elesclomol potently inhibited tumor growth in a Thumpd1-knockout mouse model of LUAD.

To develop and validate an AI-radiomics nomogram that preoperately predicts T1 lung adenocarcinoma invasiveness, providing an objective reference when frozen-section and clinical judgment disagree to optimize surgical extent. Clinical data and thin-section computed tomography (CT) images from six centers were analyzed using the Shukun AI workstation, a total of 108 features were included. Patients were classified into invasive (invasive adenocarcinoma, IAC) and non-invasive groups. Data from five centers were randomly allocated to a training set (n = 1066) and an internal validation set (n = 457), with the ratio of pre-invasive to invasive lesions kept consistent between the two sets. External validation was conducted using data from the remaining center (n = 562). Statistical analyses were performed using R software (version 4.2.1). Least absolute shrinkage and selection operator (LASSO) regression was used to select significant features, which were then incorporated into logistic regression for model construction. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. The regression equation was derived as P = ex/(1 + ex), x = 0.0113×(age) + 0.0059× (median tumor size) − 8.5780× (sphericity) + 1.0377× (GLCM Imc1) + 0.8724× (GLSZM small area emphasis) + 2.0429× (GLDM dependence entropy) − 8.8928. The training set’s ROC AUC was 0.941, sensitivity 0.8602, and specificity 0.8879. The internal validation set showed an AUC of 0.934, and the external set an AUC of 0.905. The model demonstrated high accuracy and clinical applicability. In the present study, artificial intelligence (AI) techniques were integrated with radiomics analysis to develop a predictive model for assessing the invasiveness of pulmonary nodules. This model provides valuable support for surgeons to plan surgical procedures and guide intraoperative decision-making. Its utility is particularly prominent in scenarios where there is a discrepancy between the results of intraoperative frozen section (IFS) analysis and the preoperative expectations of clinicians.

1616 Background: The TNM staging system serves as the cornerstone for most solid tumor treatment strategies. However, preoperative evaluation of early-stage cancers is not always reliable, with a particular propensity to overlook occult lymph node metastasis (OLNM) or even distant metastases. In turn, incorrect staging may lead to suboptimal perioperative decisions, including inappropriate surgical strategies, unjustified neoadjuvant therapy, and inadequate lymph node dissection. This study evaluated whether preoperative circulating tumor DNA (ctDNA) detected by methylation-based assay is associated with pathologic upstaging and the presence of OLNM or distant metastasis across multiple types of solid tumor. Methods: This study analyzed preoperative blood samples from a validation cohort within a multi-cancer early detection (MCED) study, encompassing 16 high-burden cancer types in China. Lymphomas, liver cancer, and nasopharyngeal carcinoma were excluded, due to limited use of TNM staging and/or non-routine radical surgery. Patients who had received neoadjuvant therapy were also excluded. ctDNA was detected using a targeted methylation-based MCED assay. Associations between ctDNA positivity and discrepancies between clinical (cTNM) and pathologic (pTNM) staging-including upstaging due to OLNM or distant metastasis-were examined statistically. Results: A total of 681 cases of clinical stage I–II cancers were analyzed, including lung cancer (n=124), breast cancer (n=113), colorectal cancer (n=88), gastric cancer (n=79), pancreatic cancer (n=55), cervical cancer (n=54), ovarian cancer (n=36), prostate cancer (n=34), esophageal cancer (n=32), endometrial cancer (n=31), bladder cancer (n=17), renal cancer (n=17), and gallbladder cancer (n=1). Preoperative ctDNA was detectable in 46.8% (184/393) of stage I and 71.5% (206/288) of stage II patients. ctDNA-positive patients were more likely to undergo pathologic upstaging than ctDNA-negative patients (16.9% vs. 6.9%; p=0.0002). For patients with clinical N0 (cN0), OLNM (defined as preoperative cN0 with postoperative confirmation of pN1–3) was more common in ctDNA-positive patients（12.6% vs. 4.7%; p=0.0012）. Among clinically M0 patients, three were confirmed as pathologic M1, all ctDNA-positive. Conclusions: Methylation-based preoperative ctDNA testing correlates with pathologic upstaging and OLNM, and may also be associated with occult distant metastasis. This finding supports the potential of ctDNA testing to refine preoperative staging and inform perioperative decision-making. Further validation with an expanded sample size will be conducted in an additional blinded cohort to confirm these findings.

Background: Neoadjuvant chemoimmunotherapy is a standard treatment strategy for resectable stage II-IIIB non-small cell lung cancer (NSCLC), yet the value of postoperative maintenance immunotherapy remains controversial. Methods: This five-center real-world cohort study included patients with clinical stage II-IIIB NSCLC who received exactly 3-4 cycles of neoadjuvant chemoimmunotherapy and underwent complete resection. Patients were classified into maintenance immunotherapy and no-maintenance groups. Event-free survival (EFS), disease-free survival (DFS), and overall survival (OS) were compared using Kaplan-Meier analysis, and prognostic factors were determined by Cox regression. Propensity score matching (PSM) was performed to reduce baseline imbalance. Results: A total of 450 patients were included, with 288 in the no-maintenance group and 162 in the maintenance group. The maintenance group had higher proportions of Males (93.2% vs. 86.1%), smokers (80.2% vs. 68.4%) and open surgery (38.9% vs. 29.9%). After PSM, 151 matched pairs were generated. Maintenance immunotherapy was not associated with improved survivals before or after PSM. Cox regression confirmed that maintenance immunotherapy was not a predictive factor for any survival endpoint (EFS: HR = 1.31, P = 0.142; DFS: HR = 1.37, P = 0.085; OS: HR = 1.15, P = 0.548). Subgroup analyses showed no survival advantage from maintenance immunotherapy across most strata. Conclusions: Postoperative maintenance immunotherapy did not provide additional survival benefit in NSCLC patients who received 3-4 cycles of neoadjuvant chemoimmunotherapy and complete resection. Randomized controlled trials (RCTs) are warranted to validate our results.

Importance Anastomotic complications, including anastomotic leakage, stricture, and reflux, remain major sources of morbidity after esophagectomy, yet existing techniques inadequately address this triad of issues. Objective To describe a novel Y-shaped layered anti-reflux anastomosis (Y-shaped group). and evaluate its preliminary outcomes compared with conventional circular-stapled anastomosis (O-shaped group). Design Retrospective review of a prospectively maintained database from a tertiary referral center, including consecutive patients who underwent thoracolaparoscopic esophagectomy with gastric tube reconstruction between January and December 2025. Interventions Hand-sewn Y-shaped anastomosis versus circular-stapled end-to-side anastomosis. Results Among 573 patients (102 Y-shaped, 471 O-shaped), the Y-shaped group was older (66.1 vs 63.0 years; P<0.001), more cN3 disease (P=0.006) and better tumor differentiation (P = 0.006). Anastomotic leakage (1.0% vs 5.9%; P=0.038), stricture (3.9% vs 12.3%; P=0.046), dysphagia (4.9% vs 16.8%; P = 0.002), and symptomatic reflux (8.8% vs 23.1%; P=0.011) were significantly lower in the Y-shaped group. Postoperative hospital stay was shorter in the Y-shaped group (11.8±2.98 vs 13.2±7.96 days; P = 0.003). At 6 months, mean dysphagia scores (14.33 vs 20.92; P < 0.001) and reflux scores (21.01 vs 31.86; P < 0.001) on the EORTC QLQ-OES18 were significantly lower in the Y-shaped group. Overall complication rates were comparable between groups (44/102 [43.1%] vs 236/471 [50.1%]; P = 0.636). Conclusions and Relevance The Y-shaped anastomosis was technically feasible and associated with lower rates of leakage, stricture, dysphagia, and reflux. These findings warrant prospective validation.

Abstract Objective The aim of this study was to explore the high-resolution computed tomography (HRCT) imaging characteristics of various malignant pure ground-glass nodules (pGGNs) with a diameter ≤ 10 mm and their correlation with the clinical diagnosis of invasive adenocarcinomas (IAs). Data and Methods We retrospectively analyzed patients who underwent thoracoscopic surgery for lung cancer due to malignant pGGNs (diameter ≤ 10 mm) at the First Affiliated Hospital of Zhengzhou University between July 2019 and September 2021. Based on the pathological results, patients were classified into three groups: IAs, microinvasive adenocarcinomas (MIAs) and precursor glandular lesions (PGLs). Univariate and multivariate analyses were performed to identify the independent risk factors, and receiver operating characteristic (ROC) curve analysis was used to calculate the cutoff values. Results A total of 381 patients were enrolled. No significant differences were observed in age, sex, vacuole sign, vascular penetration, tumor-pleura distance, or normal lung tissue density among the three groups (all P > 0.05). Tumor diameter (P = 0.001) and relative density (P = 0.001) were independent risk factors for distinguishing PGLs from MIAs, while relative density (P = 0.001) was the only independent risk factor for distinguishing MIAs from IAs. ROC curve analysis showed that a relative density > 320.5 HU could differentiate IAs from MIAs. Conclusion For malignant pGGNs ≤ 10 mm, MIAs can be distinguished from PGLs when the diameter exceeds 6.015 mm and relative density exceeds 209.5 HU, IAs can be distinguished from MIAs when relative density exceeds 320.5 HU. These findings provide a reference for the pathological diagnosis and surgical strategy selection of small malignant pGGNs.

BACKGROUND:Anaplastic lymphoma kinase (ALK) inhibitors have emerged as promising agents for patients with resectable ALK-positive non-small-cell lung cancer (NSCLC). Whether ensartinib, a second-generation ALK inhibitor, is safe and effective in such patients is unknown. METHODS:In this phase 3, double-blind, randomized trial involving patients with completely resected, ALK-positive stage IB to IIIB NSCLC after adjuvant chemotherapy, we randomly assigned patients in a 1:1 ratio to receive ensartinib at a dose of 225 mg once daily or placebo for 24 months. The primary end point was disease-free survival in patients with stage II to IIIB NSCLC. The key secondary end point was disease-free survival in the overall patient population. RESULTS:A total of 274 patients were randomly assigned to receive ensartinib or placebo (137 patients in each group). At 24 months, the percentage of patients with stage II to IIIB disease who were alive and disease-free was 86.4% in the ensartinib group and 53.5% in the placebo group (hazard ratio for disease recurrence or death, 0.20; 95% confidence interval [CI], 0.11 to 0.38; P<0.001). In the overall patient population, the percentage of patients who were alive and disease-free was 87.3% in the ensartinib group and 57.2% in the placebo group (hazard ratio, 0.20; 95% CI, 0.10 to 0.37; P<0.001). Overall survival data were immature. Adverse events of grade 3 or higher occurred in 35.8% of the patients who received ensartinib (most commonly rash) and in 18.2% of those who received placebo. CONCLUSIONS:Among patients with completely resected stage IB to IIIB ALK-positive NSCLC, the percentage of patients who were alive and disease-free at 24 months was significantly higher with ensartinib than with placebo. (Funded by Betta Pharmaceuticals; ELEVATE ClinicalTrials.gov number, NCT05341583.).

ABSTRACT Controversies persist regarding the optimal regimens of neoadjuvant and adjuvant therapies for locally advanced esophageal squamous cell carcinoma (ESCC). This real‐world cohort study aimed to compare the efficacy of neoadjuvant chemotherapy (NCT), chemoradiotherapy (NCRT), and chemoimmunotherapy (NCIT), and to clarify the survival contributions of adjuvant strategies. We retrospectively enrolled 1818 ESCC patients across four high‐volume Chinese medical centers. After propensity score matching, pathological tumor response and tumor downstaging followed the hierarchy NCRT > NCIT > NCT, yet no significant differences were detected in overall survival (OS) or progression‐free survival (PFS). Subgroup analyses revealed superior OS benefits of NCIT in never‑smokers, nondrinkers, and patients with clinical N2–3 disease compared with NCT. While NCRT showed greater PFS benefit than NCIT as clinical T stage advanced, its OS advantage was restricted to obese individuals. Adjuvant immunotherapy extended PFS for patients receiving prior NCIT, whereas adjuvant chemotherapy delivered survival benefits to those with pathological lymph node metastasis. We constructed optimal treatment rule (OTR) decision trees with moderate‐to‐good predictive capacity to guide selection of neoadjuvant and adjuvant therapies based on survival benefit. These results highlight the necessity of precision perioperative therapy for ESCC. The established OTR decision trees can support individualized clinical decision‐making.

First-line therapy for recurrent or metastatic esophageal squamous cell carcinoma (ESCC) currently consists of a programmed cell death protein 1 inhibitor plus platinum-based chemotherapy. Previous studies have indicated selected patients with synchronous oligometastatic ESCC may benefit from intensified curative-intent local therapy; however, prospective trials dedicated to treating patients with this specific disease are lacking. The SOME trial will prospectively explore whether patients with synchronous oligometastatic thoracic ESCC who undergo protocol-defined curative-intent local treatment after systemic therapy may have more favorable overall survival than patients with polymetastatic disease treated with systemic therapy alone. In this phase II trial, systemic anticancer therapy-naive thoracic ESCC patients will be enrolled and assigned by stage and metastatic burden to cohort A (locally advanced resectable), cohort B (synchronous oligometastatic ESCC), or cohort C (polymetastatic ESCC not meeting oligometastatic disease criteria). A total of 148 patients will be enrolled (37 in cohort A, 37 in cohort B, and 74 in cohort C). All patients will receive sintilimab plus platinum-based chemotherapy, and cohorts A and B will additionally receive curative-intent local therapy. The primary endpoint is overall survival. The secondary endpoints are progression-free survival and disease-free survival, among others.Clinical trial registration: Chinese Clinical Trial Registry (http://www.chictr.org.cn), identifier is ChiCTR2500108825.

Neoadjuvant therapy aims to reduce tumor size, increase resectability, eliminate micrometastases, and improve outcomes for patients with advanced non‑small cell lung cancer (NSCLC). Platinum‑based chemotherapy was the standard neoadjuvant treatment strategy in the past. In recent years, the emergence of targeted therapy and immunotherapy has significantly promoted neoadjuvant strategies. Immune checkpoint inhibitors (ICIs) such as programmed cell death 1 (PD-1) inhibitors, programmed cell death ligand 1 (PD-L1) inhibitors, and cytotoxic T lymphocyte associated protein 4 (CTLA-4) inhibitors represent established therapeutic agents in the treatment of NSCLC. In terms of targeted therapy, tyrosine kinase inhibitors (TKI) against driver genes such as epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) have shown promising potential in patients with driver mutations. Meanwhile, numerous clinical studies have demonstrated that neoadjuvant combination therapy can markedly improve pathological complete response (pCR) rates and event‑free survival (EFS). The CheckMate-816 trial confirmed that nivolumab plus chemotherapy outperforms chemotherapy alone. However, there are still several challenges. Drug resistance, the gap between pathological response and long-term survival benefit, and the limited predictive accuracy of current biomarkers remain major challenges in neoadjuvant therapy. In the future, efforts should focus on the search for precise predictive tools to develop personalized treatment strategies. Validation of neoadjuvant combination regimens aimed at enhancing clinical efficacy and quality of life for NSCLC patients is ongoing. This review summarizes the current advances and future directions of neoadjuvant therapy for NSCLC.