The activity and immune dynamics of PD-1 inhibition on high-risk pulmonary ground glass opacity lesions: insights from a single-arm, phase II trial.

Immune checkpoint inhibitors targeting the programmed cell death-1 (PD-1) protein significantly improve survival in patients with advanced non-small-cell lung cancer (NSCLC), but its impact on early-stage ground-glass opacity (GGO) lesions remains unclear. This is a single-arm, phase II trial (NCT04026841) using Simon's optimal two-stage design, of which 4 doses of sintilimab (200 mg per 3 weeks) were administrated in 36 enrolled multiple primary lung cancer (MPLC) patients with persistent high-risk (Lung-RADS category 4 or had progressed within 6 months) GGOs. The primary endpoint was objective response rate (ORR). T/B/NK-cell subpopulations, TCR-seq, cytokines, exosomal RNA, and multiplexed immunohistochemistry (mIHC) were monitored and compared between responders and non-responders. Finally, two intent-to-treat (ITT) lesions (pure-GGO or GGO-predominant) showed responses (ORR: 5.6%, 2/36), and no patients had progressive disease (PD). No grade 3-5 TRAEs occurred. The total response rate considering two ITT lesions and three non-intent-to-treat (NITT) lesions (pure-solid or solid-predominant) was 13.9% (5/36). The proportion of CD8+ T cells, the ratio of CD8+/CD4+, and the TCR clonality value were significantly higher in the peripheral blood of responders before treatment and decreased over time. Correspondingly, the mIHC analysis showed more CD8+ T cells infiltrated in responders. Besides, responders' cytokine concentrations of EGF and CTLA-4 increased during treatment. The exosomal expression of fatty acid metabolism and oxidative phosphorylation gene signatures were down-regulated among responders. Collectively, PD-1 inhibitor showed certain activity on high-risk pulmonary GGO lesions without safety concerns. Such effects were associated with specific T-cell re-distribution, EGF/CTLA-4 cytokine compensation, and regulation of metabolism pathways.

Spatiotemporal distribution of mediastinal neoplasms: A comprehensive multi-center study.

OBJECTIVES: Mediastinal neoplasms are typical but uncommon thoracic diseases with increasing incidence and unfavorable prognoses. A comprehensive understanding of their spatiotemporal distribution is essential for accurate diagnosis and timely treatment. However, previous studies are limited in scale and data coverage. Therefore, this study aims to elucidate the distribution of mediastinal lesions, offering valuable insights into this disease. MATERIALS AND METHODS: This multi-center, hospital-based observational study included 20 nationwide institutions. A retrospective search of electronic medical records from January 1st, 2009, to December 31st, 2020, was conducted, collecting sociodemographic data, computed tomography images, and pathologic diagnoses. Analysis focused on age, sex, time, location, and geographical region. Comparative assessments were made with global data from a multi-center database. RESULTS: Among 7,765 cases, thymomas (30.7%), benign mediastinal cysts (23.4%), and neurogenic tumors (10.0%) were predominant. Distribution varied across mediastinal compartments, with thymomas (39.6%), benign cysts (28.1%), and neurogenic tumors (51.9%) most prevalent in the prevascular, visceral, and paravertebral mediastinum, respectively. Age-specific variations were notable, with germ cell tumors prominent in patients under 18 and aged 18-29, while thymomas were more common in patients over 30. The composition of mediastinal lesions across different regions of China remained relatively consistent, but it differs from that of the global population. CONCLUSION: This study revealed significant heterogeneity in the spatiotemporal distribution of mediastinal neoplasms. These findings provide useful demographic data when considering the differential diagnosis of mediastinal lesions, and would be beneficial for tailoring disease prevention and control strategies.

Robotic-assisted navigation system for preoperative lung nodule localization: a pilot study.

Background: Preoperative percutaneous computed tomography (CT)-guided localization of pulmonary nodules plays a pivotal role in the diagnosis and treatment of early-stage lung cancer. However, conventional manual localization techniques have inherent limitations in achieving a high degree of accuracy. Consequently, a novel robotic-assisted navigation system was developed to attain precise localization of small lung nodules. This study aims to investigate the accuracy and safety of this system in clinical applications. Methods: Patients with peripheral solitary pulmonary nodules measuring less than 20 mm were enrolled. The robotic-assisted navigation system generated a three-dimensional (3D) model based on the patient's CT images, determining the optimal puncture path. The robotic arm then accurately located the nodule and, following percutaneous puncture, indocyanine green (ICG) was injected. The primary outcome measure was the accuracy of pulmonary nodule localization, while secondary outcomes included the complication rate, procedural duration, and total radiation exposure. Results: A total of 33 nodules were successfully localized using the robotic-assisted navigation system and resected through video-assisted thoracoscopic surgery (VATS). The first-pass success rate was 100%, with a median deviation of 6.1 mm [interquartile range (IQR), 2.5-7.2 mm] between the localizer and the nodule. The median localization time was 25.0 minutes, and the single and cumulative exam dose-length products (DLP) were 534.0 and 1491.0 mGy·cm, respectively. Notably, no observable complications were reported during the procedures. Conclusions: The innovative robotic-assisted navigation system demonstrated satisfactory accuracy and holds promise for improving the percutaneous localization of lung nodules. This method represents a safe and viable alternative to traditional CT-guided manual localization techniques.

Robot-assisted navigation for percutaneous localization of peripheral pulmonary nodule: an in vivo swine study.

Background: Robot-assisted surgery (RAS) systems have been developed but rarely applied to lung nodule localization. This study aimed to assess the feasibility and safety of using a robot-assisted navigation system in percutaneous lung nodule localization. Methods: A computed tomography (CT)-guided robot-assisted navigation system was used to localize the simulated peripheral nodule in the swine lung through fluorescent agent injection. After the localization, fluorescent thoracoscopic wedge resection was performed. The deviation between the target point and the needle tip was measured using a professional 3-dimensional (3D) distance measurement software. The primary outcome was the localization accuracy (deviation) of the localization. The secondary outcomes were the localization-related complication rate, the localization duration, and the success rate. Results: A total of 4 pigs were enrolled, and 20 peripheral lung nodules were created and localized successfully. All nodules underwent subsequent wedge resection for verification. The mean deviation by measuring the 3D distance was 3.81 mm [standard deviation (SD): 1.29 mm, 95% confidence interval (CI): 2.936-4.536 mm]. The technical success rate for localization was 100%, and the mean localization time was 14.69 minutes (SD: 4.67 minutes). The complication rate was 5% (1/20), with 1 pneumothorax after localization, and no mortality occurred. Conclusions: This pilot animal study demonstrated the promising potential of the robot-assisted navigation technique in peripheral lung nodule localization, with high accuracy and feasibility. Further clinical trials are needed to validate its safety compared to traditional manual localization.

Relay learning: a physically secure framework for clinical multi-site deep learning.

Big data serves as the cornerstone for constructing real-world deep learning systems across various domains. In medicine and healthcare, a single clinical site lacks sufficient data, thus necessitating the involvement of multiple sites. Unfortunately, concerns regarding data security and privacy hinder the sharing and reuse of data across sites. Existing approaches to multi-site clinical learning heavily depend on the security of the network firewall and system implementation. To address this issue, we propose Relay Learning, a secure deep-learning framework that physically isolates clinical data from external intruders while still leveraging the benefits of multi-site big data. We demonstrate the efficacy of Relay Learning in three medical tasks of different diseases and anatomical structures, including structure segmentation of retina fundus, mediastinum tumors diagnosis, and brain midline localization. We evaluate Relay Learning by comparing its performance to alternative solutions through multi-site validation and external validation. Incorporating a total of 41,038 medical images from 21 medical hosts, including 7 external hosts, with non-uniform distributions, we observe significant performance improvements with Relay Learning across all three tasks. Specifically, it achieves an average performance increase of 44.4%, 24.2%, and 36.7% for retinal fundus segmentation, mediastinum tumor diagnosis, and brain midline localization, respectively. Remarkably, Relay Learning even outperforms central learning on external test sets. In the meanwhile, Relay Learning keeps data sovereignty locally without cross-site network connections. We anticipate that Relay Learning will revolutionize clinical multi-site collaboration and reshape the landscape of healthcare in the future.

The impact of adjuvant EGFR-TKIs and 14-gene molecular assay on stage I non-small cell lung cancer with sensitive EGFR mutations.

Background: Currently, the role of EGFR-TKIs as adjuvant therapy for stage I, especially IA NSCLC, after surgical resection remains unclear. We aimed to compare the effect of adjuvant EGFR-TKIs with observation in such patients by incorporating an established 14-gene molecular assay for risk stratification. Methods: This retrospective cohort study was conducted at the First Affiliated Hospital of Guangzhou Medical University (Study ID: ChNCRCRD-2022-GZ01). From March 2013 to February 2019, completely resected stage I NSCLC (8th TNM staging) patients with sensitive EGFR mutation were included. Patients with eligible samples for molecular risk stratification were subjected to the 14-gene prognostic assay. Inverse probability of treatment weighting (IPTW) was employed to minimize imbalances in baseline characteristics. Findings: A total of 227 stage I NSCLC patients were enrolled, with 55 in EGFR-TKI group and 172 in the observation group. The median duration of follow-up was 78.4 months. After IPTW, the 5-year DFS (HR = 0.30, 95% CI, 0.14-0.67; P = 0.003) and OS (HR = 0.26, 95% CI, 0.07-0.96; P = 0.044) of the EGFR-TKI group were significantly better than the observation group. For subgroup analyses, adjuvant EGFR-TKIs were associated with favorable 5-year DFS rates in both IA (100.0% vs. 84.5%; P = 0.007), and IB group (98.8% vs. 75.3%; P = 0.008). The 14-gene assay was performed in 180 patients. Among intermediate-high-risk patients, EGFR-TKIs were associated with a significant improvement in 5-year DFS rates compared to observation (96.0% vs. 70.5%; P = 0.012), while no difference was found in low-risk patients (100.0% vs. 94.9%; P = 0.360). Interpretation: Our study suggested that adjuvant EGFR-TKI might improve DFS and OS of stage IA and IB EGFR-mutated NSCLC, and the 14-gene molecular assay could help patients that would benefit the most from treatment. Funding: This work was supported by China National Science Foundation (82022048, 82373121).

A surgical Decision-making scoring model for spontaneous ventilation- and mechanical ventilation-video-assisted thoracoscopic surgery in non-small-cell lung cancer patients.

BACKGROUNDS: Spontaneous ventilation-video-assisted thoracoscopic surgery (SV-VATS) has been applied to non-small cell lung cancer (NSCLC) patients in many centers. Since it remains a new and challenging surgical technique, only selected patients can be performed SV-VATS. We aim to conduct a retrospective single-center study to develop a clinical decision-making model to make surgery decision between SV-VATS and MV (mechanical ventilation) -VATS in NSCLC patients more objectively and individually. METHODS: Four thousand three hundred sixty-eight NSCLC patients undergoing SV-VATS or MV-VATS in the department of thoracic surgery between 2011 and 2018 were included. Univariate and multivariate regression analysis were used to identify potential factors influencing the surgical decisions. Factors with statistical significance were selected for constructing the Surgical Decision-making Scoring (SDS) model. The performance of the model was validated by area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA). RESULTS: The Surgical Decision-making Scoring (SDS) model was built guided by the clinical judgment and statistically significant results of univariate and multivariate regression analyses of potential predictors, including smoking status (p = 0.03), BMI (p < 0.001), ACCI (p = 0.04), T stage (p < 0.001), N stage (p < 0.001), ASA grade (p < 0.001) and surgical technique (p < 0.001). The AUC of the training group and the testing group were 0.72 and 0.70, respectively. The calibration curves and the DCA curve revealed that the SDS model has a desired performance in predicting the surgical decision. CONCLUSIONS: This SDS model is the first clinical decision-making model developed for an individual NSCLC patient to make decision between SV-VATS and MV-VATS.

Pan-mediastinal neoplasm diagnosis via nationwide federated learning: a multicentre cohort study.

BACKGROUND: Mediastinal neoplasms are typical thoracic diseases with increasing incidence in the general global population and can lead to poor prognosis. In clinical practice, the mediastinum's complex anatomic structures and intertype confusion among different mediastinal neoplasm pathologies severely hinder accurate diagnosis. To solve these difficulties, we organised a multicentre national collaboration on the basis of privacy-secured federated learning and developed CAIMEN, an efficient chest CT-based artificial intelligence (AI) mediastinal neoplasm diagnosis system. METHODS: In this multicentre cohort study, 7825 mediastinal neoplasm cases and 796 normal controls were collected from 24 centres in China to develop CAIMEN. We further enhanced CAIMEN with several novel algorithms in a multiview, knowledge-transferred, multilevel decision-making pattern. CAIMEN was tested by internal (929 cases at 15 centres), external (1216 cases at five centres and a real-world cohort of 11 162 cases), and human-AI (60 positive cases from four centres and radiologists from 15 institutions) test sets to evaluate its detection, segmentation, and classification performance. FINDINGS: In the external test experiments, the area under the receiver operating characteristic curve for detecting mediastinal neoplasms of CAIMEN was 0·973 (95% CI 0·969-0·977). In the real-world cohort, CAIMEN detected 13 false-negative cases confirmed by radiologists. The dice score for segmenting mediastinal neoplasms of CAIMEN was 0·765 (0·738-0·792). The mediastinal neoplasm classification top-1 and top-3 accuracy of CAIMEN were 0·523 (0·497-0·554) and 0·799 (0·778-0·822), respectively. In the human-AI test experiments, CAIMEN outperformed clinicians with top-1 and top-3 accuracy of 0·500 (0·383-0·633) and 0·800 (0·700-0·900), respectively. Meanwhile, with assistance from the computer aided diagnosis software based on CAIMEN, the 46 clinicians improved their average top-1 accuracy by 19·1% (0·345-0·411) and top-3 accuracy by 13·0% (0·545-0·616). INTERPRETATION: For mediastinal neoplasms, CAIMEN can produce high diagnostic accuracy and assist the diagnosis of human experts, showing its potential for clinical practice. FUNDING: National Key R&D Program of China, National Natural Science Foundation of China, and Beijing Natural Science Foundation.

"Major pathologic response" in lymph nodes: a modified nodal classification for non-small cell lung cancer patients treated with neoadjuvant immunochemotherapy.

We aim to examine the prognostic value of major pathologic response in metastatic lymph nodes (mLN-MPR) after immunochemotherapy in non-small cell lung cancer (NSCLC), and demonstrate the pathological characteristic of regression in mLN. Adult patients consecutively undergone neoadjuvant immunochemotherapy and radical-intent surgery for initial stage cIII NSCLC between 2020 and 2021 were included. Hematoxylin- and eosin-stained slides of paraffinembedded sections of the degree of pathologic response in the primary tumor (PT) and its paired involved LNs were reviewed. Imaging mass cytometry was conducted to quantify the immunological status. With 10% as residual viable tumor (RVT) cutoff, mLN-MPR (HR: 0.34, 95%CI: 0.14-0.78; P = 0.011, ref: mLN-MPR(-)) showed more significant correlation with DFS than ypN0 (HR: 0.40, 95%CI: 0.17-0.94; P = 0.036, ref: ypN1-N2). And mLN-MPR combined with PT-MPR, compared with ypN stage combined with PT-MPR (p-value: 0.030 vs. 0.117), can better distinguished the DFS curves of the 4 subgroups of patients. mLN-MPR(+)/PT-MPR(+) patients had the best prognosis compared with other subgroups. Pathologic responses of RVT in PT and paired regional LNs [MPR inconsistency rate: 21/53 (39.6%)], and across different LNs could be inconsistent, especially in squamous cell carcinoma. RVT% in mLNs after immunochemotherapy appeared to be polarized [16 (30.2%) cases with RVT ≥ 70%; 34 (64.2%) with RVT ≤ 10%]. Partial regression of LN metastasis could present with distinct immune subtypes: immune-inflamed or immune-evacuation subtype, and the former presented with higher CD3, CD8, and PD-1 expression in the invasive margin. mLN-MPR demonstrated a potential prognostic value in predicting DFS in patients treated with neoadjuvant immunochemotherapy, but further research is needed to validate its usefulness for other survival outcomes, including OS.

A machine learning approach for preoperatively assessing pulmonary function with computed tomography in patients with lung cancer.

Background: It is clinically important to accurately assess the pulmonary function of patients with lung cancer, especially before surgery. This knowledge can help clinicians to monitor patients pre- and post-surgery, predict the impact of surgery on pulmonary function, and help to optimize postsurgical recovery. We used a deep learning approach for assessing pulmonary function on computed tomography (CT) scans in patients with lung cancer before they underwent surgery. Methods: A total of 188 patients with lung cancer whose diagnoses had been pathologically confirmed were enrolled in this study. We used a software to automatically delineate regions of interest (ROIs) throughout the airways, lobes, and the whole lungs. We then used AK software to extract radiomics features of the 3 types of ROIs. We randomly separated these cases into a training cohort and a test cohort at a ratio of 7:3. We next constructed a logistic regression model to assess pulmonary function from the radiomics features. The machine learning outcomes were compared with established clinical criteria for pulmonary function. including forced expiratory volume in the first second/forced vital capacity (FEV1/FVC), FVC, and maximum vital capacity (VCmax) to evaluate the accuracy of the machine learning model. Results: In the ROIs of the lobes, our results showed that the machine learning model had good performance in predicting FVC and VCmax, attaining a Spearman correlation r value of 0.714 with P<0.001 for FVC and a r value of 0.687 with P<0.001 for VCmax. Using the airway ROIs, our model achieved a r of 0.603 with P=0.001 for VCmax. Using the whole lung ROIs, our model achieved a r of 0.704 with P<0.001 for FVC and a r of 0.693 with P<0.001 for VCmax. Conclusions: Preoperative CT may provide a means for evaluating pulmonary function in patients with lung cancer. With radiomics features extracted from the airway, lobes, and the whole lung region, and a properly trained machine learning model, it is possible to obtain accurate estimation for metrics used in clinical criteria and to offer clinicians imaging-based indicators for the status of pulmonary functions.

The relationship of airflow limitation with lung squamous cell carcinoma: evidence from mendelian randomization analysis.

BACKGROUND: Observational studies showed associations between smoking, and airflow limitation, with lung squamous cell carcinoma (LUSC). However, the causal association of airflow limitation with LUSC and the modification by smoking status for the association remains unclear. METHODS: Genetic summary data were obtained from large genome-wide association studies (GWAS). One hundred two single nucleotide polymorphisms (SNPs) for airflow limitation (i.e., FEV1/FVC < 0.7) and 153 SNPs for smoking behavior were used as instrumental variables and the main MR analysis methods. The univariable and multivariable Mendelian Randomization (MR) in a two-sample setting were performed to assess the association of airflow limitation, and smoking behavior with LUSC. RESULTS: In the univariable MR analysis, genetic predisposition towards airflow limitation [Inverse Variance-Weighted (IVW) method Odds Ratio (OR) = 4.83, 95% Confidence Interval (CI) 1.55 to 15.06, P = 0.006], age of smoking initiation (IVW method OR = 0.10, 95%CI 0.02 to 0.36, P < 0.001), cigarettes smoked per day (IVW method OR = 3.10, 95%CI 2.07 to 4.63, P < 0.001), ex-smoking (IVW method OR = 0.47, 95%CI 0.31 to 0.69, P < 0.001), current smoking status (IVW method OR = 13.08, 95%CI 2.53 to 67.84, P = 0.002), pack-years of smoking (Weighted median method OR = 11.49, 95%CI 3.71 to 35.63, P < 0.001) were associated with LUSC. In the multivariable MR analysis, the causal effect of airflow limitation was still observed on LUSC (IVW method OR = 2.97, 95% CI 1.09 to 8.04, P = 0.032 adjusted for age of smoking initiation and cigarettes smoked per day; IVW method OR = 3.24, 95% CI 1.09 to 9.58, P = 0.033 adjusted for ex-smoking, current smoking status, and pack years of smoking; IVW method OR = 2.91, 95% CI 1.01 to 8.41, P = 0.049 adjusted for 5 smoking behaviors mentioned above). CONCLUSIONS: Our MR analysis demonstrated that airflow limitation is likely to be an independent predictor of LUSC.

Risk mapping of lung cancer: a comprehensive appraisal of published meta-analyses incorporating Mendelian randomization studies.

INTRODUCTION: A comprehensive appraisal of published meta-analyses incorporating Mendelian randomization studies was performed to map the different risk factors and assess the causality for lung cancer. METHODS: Systematic reviews and meta-analyses of observational and interventional studies were reviewed based on PubMed, Embase, Web of Science, and Cochrane Library. Mendelian randomization analyses were conducted to validate the causal associations of those various exposures with lung cancer using summary statistics from 10 genome-wide association studies (GWAS) consortia and other GWAS databases in MR-Base platform. RESULTS: In the review of meta-analyses, 105 risk factors associated with lung cancer were identified from 93 articles. It was found that 72 risk factors were nominally significant (P < 0.05) associated with lung cancer. Mendelian randomization analyses were performed to analyze 36 exposures based on 551 SNPs and 4,944,052 individuals, finding that 3 exposures had a consistent risk/protective effect on lung cancer with the results of the meta-analysis. In Mendelian randomization anaylses, smoking (OR 1.44, 95% CI 1.18-1.75; P = 0.001) and blood copper (OR 1.14, 95% CI 1.01-1.29; P = 0.039) significantly associated with increased risk of lung cancer, whereas aspirin use (OR 0.67, 95% CI 0.50-0.89; P = 0.006) showed protective effects. CONCLUSION: This study mapped putative associations of risk factors for lung cancer, revealing the causal hazard effect of smoking, blood copper, and the protective effect of aspirin use in the development of lung cancer. CLINICAL TRIAL REGISTRY: This study is registered with PROSPERO (CRD42020159082).

Evaluation of Clinical and Safety Outcomes of Neoadjuvant Immunotherapy Combined With Chemotherapy for Patients With Resectable Esophageal Cancer: A Systematic Review and Meta-analysis.

Importance: A considerable number of clinical trials of neoadjuvant immunotherapy for patients with resectable esophageal cancer are emerging. However, systematic evaluations of these studies are lacking. Objective: To provide state-of-the-art evidence and normative theoretical support for neoadjuvant immunotherapy for locally advanced resectable esophageal cancer. Data Sources: PubMed, Embase, Cochrane Library, and ClinicalTrials.gov databases were searched for relevant original articles and conference proceedings that were published in English through April 1, 2022. Study Selection: Published phase 2 or 3 clinical trials that included patients with resectable stage I to IV esophageal cancer who received immune checkpoint inhibitors (ICIs) before surgery as monotherapy or in combination with other therapies. Data Extraction and Synthesis: The Preferred Reporting Items for Systematic Reviews and Meta-analyses and the Meta-analysis of Observational Studies in Epidemiology guidelines for meta-analysis were followed to extract data. A random-effects model was adopted if the heterogeneity was significant (I2 statistic >50%); otherwise, the common-effects model was used. Data analyses were conducted from April 2 to 8, 2022. Main Outcomes and Measures: Pathological complete response (pCR) rate and major pathological response (MPR) rate were considered to be the primary outcomes calculated for the clinical outcomes of neoadjuvant immunotherapy. Incidence of treatment-related severe adverse events was set as the major measure for the safety outcome. The rate of R0 surgical resection was summarized. Subgroup analyses were conducted according to histologic subtype and ICI types. Results: A total of 27 clinical trials with 815 patients were included. Pooled rates were 31.4% (95% CI, 27.6%-35.3%) for pCR and 48.9% (95% CI, 42.0-55.9%) for MCR in patients with esophageal cancer. In terms of safety, the pooled incidence of treatment-related severe adverse events was 26.9% (95% CI, 16.7%-38.3%). Most patients achieved R0 surgical resection (98.6%; 95% CI, 97.1%-99.6%). Regarding histologic subtypes, the pooled pCR rates were 32.4% (95% CI, 28.2%-36.8%) in esophageal squamous cell carcinoma and 25.2% (95% CI, 16.3%-35.1%) in esophageal adenocarcinoma. The pooled MPR rate was 49.4% (95% CI, 42.1%-56.7%) in esophageal squamous cell carcinoma. Conclusions and Relevance: This study found that neoadjuvant immunotherapy with chemotherapy had promising clinical and safety outcomes for patients with resectable esophageal cancer. Randomized clinical trials with long-term follow-up are warranted to validate the findings and benefits of ICIs.

Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms.

Background: Developing prediction tools for immunotherapy approaches is a clinically important and rapidly emerging field. The routinely used prediction biomarker is inaccurate and may not adequately utilize large amounts of medical data. Machine learning is a promising way to predict the benefit of immunotherapy from individual data by individuating the most important features from genomic data and clinical characteristics. Methods: Machine learning was applied to identify a list of candidate genes that may predict immunotherapy benefits using data from the published cohort of 853 patients with NSCLC. We used XGBoost to capture nonlinear relations among many mutation genes and ICI benefits. The value of the derived machine learning-based mutation signature (ML-signature) on immunotherapy efficacy was evaluated and compared with the tumor mutational burden (TMB) and other clinical characteristics. The predictive power of ML-signature was also evaluated in independent cohorts of patients with NSCLC treated with ICI. Results: We constructed the ML-signature based on 429 (training/validation = 8/2) patients who received immunotherapy and extracted 88 eligible predictive genes. Additionally, we conducted internal and external validation with the utility of the OAK+POPLAR dataset and independent cohorts, respectively. This ML-signature showed the enrichment in immune-related signaling pathways and compared to TMB, ML-signature was equipped with favorable predictive value and stratification. Conclusion: Previous studies proposed no predictive difference between original TMB and modified TMB, and original TMB contains some genes with no predictive value. To demonstrate that fewer genetic tests are sufficient to predict immunotherapy efficacy, we used machine learning to screen out gene panels, which are used to calculate TMB. Therefore, we obtained the 88-gene panel, which showed the favorable prediction performance and stratification effect compared to the original TMB.

Causal effects of genetically determined metabolites on cancers included lung, breast, ovarian cancer, and glioma: a Mendelian randomization study.

Background: Previous studies have shown that metabolites play important roles in phenotypic regulation, but the causal link between metabolites and tumors has not been examined adequately. Herein, we investigate the causality between metabolites and various cancers through a Mendelian randomization (MR) study. Methods: We carried out a two-sample MR analysis based on genetic instrumental variables as proxies for 486 selected human serum metabolites to evaluate the causal effects of genetically determined metabotypes (GDMs) on cancers. Summary data from various cancer types obtained from large consortia. Inverse variance weighted (IVW), MR-Egger and weighted-median methods were implemented to infer the causal effects, moreover, we particularly explored the presentence of horizontal pleiotropy through MR-Egger regression and MR-PRESSO Global test. Metabolic pathways analysis and subgroup analyses were further explored using available data. Statistical analyses were all performed in R. Results: In MR analysis, 202 significant causative relationship features were identified. 7-alpha-hydroxy-3-oxo-4-cholestenoate (ORIVW =1.45; 95% CI: 1.06-1.97; PIVW =0.018), gamma-glutamylisoleucine (ORIVW =1.40; 95% CI: 1.16-1.69; PIVW =0.0004), 1-oleoylglycerophosphocholine (ORIVW =1.22; 95% CI: 1.1-1.35; PIVW =0.0001), gamma-glutamylleucine (ORIVW =4.74; 95% CI: 1.18-18.93; PIVW =0.027) were the most dangerous metabolites for lung cancer, ovarian cancer, breast cancer, and glioma, respectively; while pseudouridine (ORIVW =0.50; 95% CI: 0.30-0.83; PIVW =0.007), 2-methylbutyroylcarnitine (ORIVW =0.77; 95% CI: 0.68-0.86; PIVW =2.9×10-6), 2-methylbutyroylcarnitine (ORIVW =0.77; 95% CI: 0.70-0.85; PIVW =3.4×10-7), glycylvaline (ORIVW =0.13; 95% CI: 0.02-0.75; PIVW =0.021) were associated with lower risk of lung cancer, ovarian cancer, breast cancer, and glioma, respectively. Interestingly, 2-methylbutyroylcarnitine was also associated with decreased risk of lung cancer (ORIVW =0.59; 0.50-0.70; P IVW =1.98×10-9) expect ovarian cancer and breast cancer. In subgroup analysis, 2-methylbutyroylcarnitine was associated with decreased risk of estrogen receptor (ER) positive breast cancer (ORIVW =0.72; 0.64-0.80; PIVW =3.55×10-9), lung adenocarcinoma (LAC) (ORIVW =0.60; 0.48-0.70; PIVW =1.14×10-5). Metabolic pathways analysis identified 4 significant pathways. Conclusions: Our study integrated metabolomics and genomics to explore the risk factors involved in the development of cancers. It is worth exploring whether metabolites with causality can be used as biomarkers to distinguish patients at high risk of cancer in clinical practice. More detailed studies are needed to clarify the mechanistic pathways.

The strategy of non-intubated spontaneous ventilation anesthesia for upper tracheal surgery: a retrospective case series study.

Background: Upper tracheal surgery is used to treat patients who with tracheal tumors or tracheal stenosis. The non-intubated spontaneous ventilation anesthesia (NSVA) may have advantages over endotracheal intubation and surgical cross-field intubation in upper tracheal surgery. This study aimed to illustrate and assess the feasibility of NSVA strategy for upper tracheal surgery. Methods: This is a retrospective case series study in which 51 patients (from May 2015 to August 2020) who met the criteria in NSVA strategy were analyzed. Anesthesia was performed using total intravenous anesthesia (TIVA) combined with bilateral superficial cervical plexus block (CPB) or thoracic epidural anesthesia (TEA). Patients received spontaneous ventilation through laryngeal mask airway (LMA) during the surgery. Anesthesia conversion technique was applied to patients who met the anesthesia conversion criteria. Results: In total, 51 patients met the NSVA criteria and were included in this study. Forty-six out of 51 patients (90%) had TIVA + bilateral superficial CPB and five patients (10%) had TIVA + TEA + CPB. During the airway-opened period, 46 patients had stable spontaneous ventilation. Five patients need anesthesia conversion, two patients had high-frequency ventilation (HFV), and three patients required cross-field intubation. Postoperative complications occurred in seven (14%) patients, no reintubation was needed after surgery. The median postoperative hospital stay was 6.31±4.30 days. Conclusions: This NSVA strategy includes criteria for patient selection, preoperative assessment, surgical technique, airway management, criteria and technique for anesthesia conversion. The NSVA strategy is a feasible procedure in upper tracheal surgery.

Dynamic monitoring serum tumor markers to predict molecular features of EGFR-mutated lung cancer during targeted therapy.

To reveal the correlation of dynamic serum tumor markers (STMs) and molecular features of epidermal growth factor receptor-mutated (EGFR-mutated) lung cancer during targeted therapy, we retrospectively reviewed 303 lung cancer patients who underwent dynamic STM tests [neuron-specific enolase (NSE), carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125), carbohydrate antigen 153 (CA153), the soluble fragment of cytokeratin 19 (CYFRA21-1), and squamous cell carcinoma antigen (SCC)] and circulating tumor DNA (ctDNA) testing with a panel covering 168 genes. At baseline, patients with EGFR mutation trended to have abnormal CEA, abnormal CA153, and normal SCC levels. Additionally, patients with Thr790Met (T790M) mutation were more likely to have abnormal CEA levels than patients without T790M mutation. Among patients with secondary resistance to EGFR tyrosine kinase inhibitors (TKI), the dynamic STMs showed a descending trend in the responsive stage and a rising trend in the resistant stage. However, the changing slopes differed between T790M subgroup and the non-T790M subgroup in individual STMs. Our study demonstrated that the combination of baseline levels and variations of STMs (including the responsive stage and resistant stage) can be suggestive of secondary EGFR-T790M mutation [area under the curve (AUC) = 0.897] and that changing trends of STMs (within 8 weeks after initiating the TKI therapy) can be potential predictors for the clearance of EGFR ctDNA [AUC = 0.871]. In conclusion, dynamic monitoring STMs can help to predict the molecular features of EGFR-mutated lung cancer during targeted therapy.

Predicting EGFR mutation status in lung adenocarcinoma presenting as ground-glass opacity: utilizing radiomics model in clinical translation.

OBJECTIVES: This study aimed to establish a non-invasive radiomics model based on computed tomography (CT), with favorable sensitivity and specificity to predict EGFR mutation status in GGO-featured lung adenocarcinoma subsequently guiding the administration of targeted therapy. METHODS: Clinical-pathological information and preoperative CT images of 636 lung adenocarcinoma patients (464, 100, and 72 in the training, internal, and external validation sets, respectively) that underwent GGO lesions resection were included. A total of 1476 radiomics features were extracted with gradient boosting decision tree (GBDT). RESULTS: The established radiomics model containing 102 selected features showed an encouraging discrimination performance of EGFR mutation status (mutant or wild type), and the predictive ability was superior to that of the clinical model (AUC: 0.838 vs. 0.674, 0.822 vs. 0.730, and 0.803 vs. 0.746 for the training, internal validation, and external validation sets, respectively). The combined radiomics plus clinical model showed no additional benefit over the radiomics model in predicting EGFR status (AUC: 0.846 vs. 0.838, 0.816 vs. 0.822, and 0.811 vs. 0.803, respectively, in three cohorts). Uniquely, this model was validated in a cohort of lung adenocarcinoma patients who have undertaken adjuvant EGFR-TKI treatment and harbored unresected GGOs during the medication, leading to a significantly improved potency of EGFR-TKIs (response rate: 25.9% vs. 53.8%, p = 0.006; before and after prediction, respectively). CONCLUSION: This presented radiomics model can be served as a non-invasive and time-saving approach for predicting the EGFR mutation status in lung adenocarcinoma presenting as GGO. KEY POINTS: • We developed a GGO-specific radiomics model containing 102 radiomics features for EGFR mutation status differentiation. • An AUC of 0.822 and 0.803 in the internal and external validation cohorts, respectively, were achieved. • The radiomics model was utilized in clinical translation in an adjuvant EGFR-TKI treatment cohort with unresected GGOs. A significant improvement in the potency of EGFR-TKIs was achieved (response rate: 25.9% vs. 53.8%, p = 0.006; before and after prediction).

Spontaneous Ventilation Video-Assisted Thoracoscopic Surgery for Non-small-cell Lung Cancer Patients With Poor Lung Function: Short- and Long-Term Outcomes.

Objective: The goal of this study was to explore the feasibility and safety of spontaneous ventilation video-assisted thoracoscopic surgery (SV-VATS) for non-small-cell lung cancer (NSCLC) patients with poor lung function. Methods: NSCLC patients with poor lung function who underwent SV-VATS or mechanical ventilation VATS (MV-VATS) from 2011 to 2018 were analyzed. 1:2 Propensity score matching (PSM) was applied, and the short- and long-term outcomes between the SV-VATS group and the MV-VATS group were compared. Results: Anesthesia time (226.18 ± 64.89 min vs. 248.27 ± 76.07 min; P = 0.03), operative time (140.85 ± 76.07 min vs. 163.12 ± 69.37 min; P = 0.01), days of postoperative hospitalization (7.29 ± 3.35 days vs. 8.40 ± 7.89 days; P = 0.04), and days of chest tube use (4.15 ± 2.89 days vs. 5.15 ± 3.54 days; P = 0.01), the number of N1 station lymph node dissection (2.94 ± 3.24 vs. 4.34 ± 4.15; P = 0.005) and systemic immune-inflammation index (3855.43 ± 3618.61 vs. 2908.11 ± 2933.89; P = 0.04) were lower in SV-VATS group. Overall survival and disease-free survival were not significantly different between the two groups (OS: HR 0.66, 95% CI: 0.41-1.07, P = 0.09; DFS: HR 0.78, 95% CI: 0.42-1.45, P = 0.43). Conclusions: Comparable short-term and long-term outcomes indicated that SV-VATS is a feasible and safe method and might be an alternative to MV-VATS when managing NSCLC patients with poor lung function.