This study investigated the relationship between mediastinal fat and pulmonary nodule status, aiming to develop a deep learning-based radiomics model for diagnosing benign and malignant pulmonary nodules. We proposed a combined model using CT images of both pulmonary nodules and the fat around the chest (mediastinal fat). Patients from three centers were divided into training, validation, internal testing, and external testing sets. Quantitative radiomics and deep learning features from CT images served as predictive factors. A logistic regression model was used to combine data from both pulmonary nodules and mediastinal adipose regions, and personalized nomograms were created to evaluate the predictive performance. The model incorporating mediastinal fat outperformed the nodule-only model, with C-indexes of 0.917 (training), 0.903 (internal testing), 0.942 (external testing set 1), and 0.880 (external testing set 2). The inclusion of mediastinal fat significantly improved predictive performance (NRI = 0.243, p < 0.05). A decision curve analysis indicated that incorporating mediastinal fat features provided greater patient benefits. Mediastinal fat offered complementary information for distinguishing benign from malignant nodules, enhancing the diagnostic capability of this deep learning-based radiomics model. This model demonstrated strong diagnostic ability for benign and malignant pulmonary nodules, providing a more accurate and beneficial approach for patient care.

OBJECTIVE: There is currently no evidence documenting the clinical characteristics and prognosis of non-high-risk patients with incidental stage T1 lung cancer (LC). The aim of this study was to investigate the clinical characteristics and prognosis of non-high-risk patients with incidental stage T1 LC.
METHODS: This prospective cohort study included patients with incidental stage T1 LC who were diagnosed pathologically at the First Affiliated Hospital of Chongqing Medical University between 1st Jan 2019 and 31st Dec 2023. The follow-up time for all participants concluded on 31st Jan 2024, or upon death. All included patients were divided into non-high-risk (observation) and high-risk (control) groups based on the 2021 US preventative services task force recommendations. The primary outcomes were overall survival probability and LC-specific survival probability. The secondary outcomes were clinical characteristics, including demographic variables, histological types and TNM staging.
RESULTS: We studied 1876 patients with incidental stage T1 LC. Of these, 1491 (79.48%) non-high-risk patients were included in the observation group, and the remaining 385 (20.52%) high-risk patients composed the control group. The follow-up interval was between 0 and 248 months for all participants, with a median time of 41.64 ± 23.85 months. The patients in the observation group were younger and had smaller tumors, more adenocarcinomas, and earlier disease stages than those in the control group (p ≤ 0.001). The overall survival probability (HR = 0.23, [95% CI: 0.18, 0.31], p < 0.001) and the LC-specific survival probability (HR = 0.23, [95% CI: 0.17, 0.31], p < 0.001) for the patients in the observation group were also both higher than those in the control group. The results appeared to be consistent across important subgroups.
CONCLUSIONS: In this study, non-high-risk patients with incidental stage T1 LC were younger, had smaller tumors, had more adenocarcinomas, had a lower probability of metastasis, and had longer survival than did high-risk patients.

UNASSIGNED: In order to address the low compliance and dissatisfied specificity of low-dose computed tomography (LDCT), efficient and non-invasive approaches are needed to complement its limitations for lung cancer screening and management. The ASCEND-LUNG study is a prospective two-stage case-control study designed to evaluate the performance of a liquid biopsy-based comprehensive lung cancer screening and post-screening pulmonary nodules management system.
UNASSIGNED: We aimed to develop a comprehensive lung cancer system called Peking University Lung Cancer Screening and Management System (PKU-LCSMS) which comprises a lung cancer screening model to identify specific populations requiring LDCT and an artificial intelligence-aided (AI-aided) pulmonary nodules diagnostic model to classify pulmonary nodules following LDCT. A dataset of 465 participants (216 cancer, 47 benign, 202 non-cancer control) were used for the two models\' development phase. For the lung cancer screening model development, cancer participants were randomly split at a ratio of 1:1 into the train and validation cohorts, and then non-cancer controls were age-matched to the cancer cases in a 1:1 ratio. Similarly, for the AI-aided pulmonary nodules model, cancer and benign participants were also randomly divided at a ratio of 2:1 into the train and validation cohorts. Subsequently, during the model validation phase, sensitivity and specificity were validated using an independent validation cohort consisting of 291 participants (140 cancer, 25 benign, 126 non-cancer control). Prospectively collected blood samples were analyzed for multi-omics including cell-free DNA (cfDNA) methylation, mutation, and serum protein. Computerized tomography (CT) images data was also obtained. Paired tissue samples were additionally analyzed for DNA methylation, DNA mutation, and messenger RNA (mRNA) expression to further explore the potential biological mechanisms. This study is registered with ClinicalTrials.gov, NCT04817046.
UNASSIGNED: Baseline blood samples were evaluated for the whole screening and diagnostic process. The cfDNA methylation-based lung cancer screening model exhibited the highest area under the curve (AUC) of 0.910 (95% CI, 0.869-0.950), followed by the protein model (0.891 [95% CI, 0.845-0.938]) and lastly the mutation model (0.577 [95% CI, 0.482-0.672]). Further, the final screening model, which incorporated cfDNA methylation and protein features, achieved an AUC of 0.963 (95% CI, 0.942-0.984). In the independent validation cohort, the multi-omics screening model showed a sensitivity of 99.2% (95% CI, 0.957-1.000) at a specificity of 56.3% (95% CI, 0.472-0.652). For the AI-aided pulmonary nodules diagnostic model, which incorporated cfDNA methylation and CT images features, it yielded a sensitivity of 81.1% (95% CI, 0.732-0.875), a specificity of 76.0% (95% CI, 0.549-0.906) in the independent validation cohort. Furthermore, four differentially methylated regions (DMRs) were shared in the lung cancer screening model and the AI-aided pulmonary nodules diagnostic model.
UNASSIGNED: We developed and validated a liquid biopsy-based comprehensive lung cancer screening and management system called PKU-LCSMS which combined a blood multi-omics based lung cancer screening model incorporating cfDNA methylation and protein features and an AI-aided pulmonary nodules diagnostic model integrating CT images and cfDNA methylation features in sequence to streamline the entire process of lung cancer screening and post-screening pulmonary nodules management. It might provide a promising applicable solution for lung cancer screening and management.
UNASSIGNED: This work was supported by Science, Science, Technology & Innovation Project of Xiongan New Area, Beijing Natural Science Foundation, CAMS Innovation Fund for Medical Sciences (CIFMS), Clinical Medicine Plus X-Young Scholars Project of Peking University, the Fundamental Research Funds for the Central Universities, Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, Peking University People\'s Hospital Research and Development Funds, National Key Research and Development Program of China, and the fundamental research funds for the central universities.

UNASSIGNED: Robotic bronchoscopy (RB) has emerged as a novel technique to address issues with the biopsy of small peripheral lung lesions. The objective of this study was to quantitatively assess the accuracy of a novel multisection robotic bronchoscope compared with current standards of care.
UNASSIGNED: This is a prospective, single-blind, comparative study where the accuracy of a multisection RB was compared against the accuracy of standard electromagnetic navigational bronchoscopy (EM-NB) during lesion localization and targeting. Five blinded subjects of varying bronchoscopy experience were recruited to use both RB and EM-NB in a swine lung model. Accuracy of localization and targeting success was measured as the distance from the center of pulmonary targets at each anatomic location. Subjects used both RB and EM-NB to navigate to 4 pulmonary targets assigned using 1:1 block randomization. Differences in accuracy and time between navigation systems were assessed using Wilcoxon rank-sum test.
UNASSIGNED: Of the 40 total attempts per modality, successful targeting was achieved on 90% and 85% of attempts utilizing RB and EM-NB, respectively. Furthermore, RB demonstrated significantly lower median distance to the real-time EM target (1.1 mm; interquartile range [IQR], 0.6-2.0 mm) compared with EM-NB (2.6 mm; IQR, 1.6-3.8) (P < .001). Median target displacement resulting from lung and bronchus deformation during bronchoscopy was found to be significantly lower using RB (0.8 mm; IQR, 0.5-1.2 mm) compared with EM-NB (2.6 mm; IQR, 1.4-6.4 mm) (P < .001).
UNASSIGNED: The results of this study demonstrate that the multi-section RB prototype allows for improved localization and targeting of small peripheral lung nodules compared with current nonrobot bronchoscopy modalities.

UNASSIGNED: Inflammation plays an important role in the transformation of pulmonary nodules (PNs) from benign to malignant. Prediction of benignancy and malignancy of PNs is still lacking efficacy methods. Although Mayo or Brock model have been widely applied in clinical practices, their application conditions are limited. This study aims to construct a diagnostic model of PNs by machine learning using inflammation-related biological markers (IRBMs).
UNASSIGNED: Inflammatory related genes (IRGs) were first extracted from GSE135304 chip data. Then, differentially expressed genes (DEGs) and infiltrating immune cells were screened between malignant pulmonary nodules (MN) and benign pulmonary nodule (BN). Correlation analysis was performed on DEGs and infiltrating immune cells. Molecular modules of IRGs were identified through Consistency cluster analysis. Subsequently, IRBMs in IRGs modules were filtered through Weighted gene co-expression network analysis (WGCNA). An optimal diagnostic model was established using machine learning methods. Finally, external dataset GSE108375 was used to verify this result.
UNASSIGNED: 4 hub IRGs and 3 immune cells showed significantly difference between MN and BN, C1 and C2 module, namely PRTN3, ELANE, NFKB1 and CTLA4, T cells CD4 naïve, NK cells activated and Monocytes. IRBMs were screened from black module and yellowgreen module through WGCNA analysis. The Support vector machines (SVM) was identified as the optimal model with the Area Under Curve (AUC) was 0.753. A nomogram was established based on 5 hub IRBMs, namely HS.137078, KLC3, C13ORF15, STOM and KCTD13. Finally, external dataset GSE108375 verified this result, with the AUC was 0.718.
UNASSIGNED: SVM model established by 5 hub IRBMs was able to effectively identify MN or BN. Accumulating inflammation and immune dysfunction were important to the transformation from BN to MN.

UNASSIGNED: Pulmonary nodules are small, focal lesions often identified via computed tomography (CT) scans. Although the majority are benign, a small percentage of them may be malignant or potentially become malignant, underscoring the importance of early detection and effective management. This study systematically reviews the epidemiology, risk factors, and management strategies for pulmonary nodules, comparing findings across Chinese and non-Chinese populations to better inform the actuarial calculations for predicting the demand of medical services for patients with pulmonary nodules.
UNASSIGNED: We performed a systematic analysis of the PubMed and China Knowledge Infrastructure (CNKI) databases for studies reporting the detection rate of pulmonary nodules through CT scans. Both cross-sectional studies and the baseline data from longitudinal studies were included. A modified version of the Newcastle-Ottawa Scale was used to assess the risk of bias and random effect models were used to estimate the overall prevalence.
UNASSIGNED: We identified 32 studies and included 24 of them in our meta-analysis. Pooled analysis showed that the overall prevalence of pulmonary nodules was 0.27 (95% confidence interval: 0.25-0.29) after outliers removal. Subgroup analysis showed that there was no significant difference for prevalence between Chinese and non-Chinese populations. Males (0.38) were shown to have slightly higher prevalence compared to females (0.36), but not significant (P=0.88). Age and smoking are the most frequently reported risk factors by studies.
UNASSIGNED: Overall, 27% of participants were positive for pulmonary nodules. Advancing age and smoking were consistently identified as a key risk factor for the incidence of pulmonary nodules. Although the management strategies are different across studies, recent guidelines recommend personalized management strategies, prioritizing nodule size, characteristics, and individual risk factors to optimize outcomes.

UNASSIGNED: Preoperative computed tomography (CT)-guided localization of small pulmonary nodules (SPNs) is the major approach for accurate intraoperative visualization in video-assisted thoracoscopic surgery (VATS). However, this interventional procedure has certain risks and may challenge to less experienced junior doctors. This study aims to evaluate the feasibility and efficacy of robotic-assisted CT-guided preoperative pulmonary nodules localization with the modified hook-wire needles before VATS.
UNASSIGNED: A total of 599 patients with 654 SPNs who preoperatively accepted robotic-assisted CT-guided percutaneous pulmonary localization were respectively enrolled and compared to 90 patients with 94 SPNs who underwent the conventional CT-guided manual localization. The clinical and imaging data including patients\' basic information, pulmonary nodule features, location procedure findings, and operation time were analyzed.
UNASSIGNED: The localization success rate was 96.64% (632/654). The mean time required for marking was 22.85±10.27 min. Anchor of dislodgement occurred in 2 cases (0.31%). Localization-related complications included pneumothorax in 163 cases (27.21%), parenchymal hemorrhage in 222 cases (33.94%), pleural reaction in 3 cases (0.50%), and intercostal vascular hemorrhage in 5 cases (0.83%). Localization and VATS were performed within 24 hours. All devices were successfully retrieved in VATS. Histopathological examination revealed 166 (25.38%) benign nodules and 488 (74.62%) malignant nodules. For patients who received localizations, VATS spent a significantly shorter time, especially the segmentectomy group (93.61±35.72 vs. 167.50±40.70 min, P<0.001). The proportion of pneumothorax in the robotic-assisted group significantly decreased compared with the conventional manual group (27.21% vs. 43.33%, P=0.002).
UNASSIGNED: Robotic-assisted CT-guided percutaneous pulmonary nodules hook-wire localization could be effectively helpful for junior less experienced interventional physicians to master the procedure and potentially increase precision.

UNASSIGNED: Through preoperative localization, surgeons can easily locate ground glass nodules (GGNs) and effectively control the extent of resection. Therefore, it is necessary to choose an appropriate puncture positioning method. The purpose of this study was to evaluate the effectiveness and safety of medical glue and positioning hooks in the preoperative positioning of GGNs and to provide a reference for clinical selection.
UNASSIGNED: From March 30, 2020 to June 13, 2022, a total of 859 patients with a CT diagnosis of GGNs requiring surgical resection were included in our study at the hospital. Among them, 21 patients who either opted out or could not undergo preoperative localization for various reasons were excluded. Additionally, 475 patients who underwent preoperative localization using medical glue and 363 patients who underwent preoperative localization through positioning hooks were also excluded. We conducted statistical analyses on the baseline data, success rates, complications, and pathological results of the remaining patients. The success rates, complication rates, and pathological results were compared between the two groups-those who received medical glue localization and those who received positioning hook localization.
UNASSIGNED: There was no statistically significant difference between the two groups of patients in terms of age, body mass index, smoking history, location of the nodule, distance of the nodule from the pleura, or postoperative pathological results (P > 0.05). The success rate of medical glue and positioning hooks was 100%. The complication rates of medical glue and positioning hooks during single nodule positioning were 39.18% and 23.18%, respectively, which were significantly different (p < 0.001); the complication rates during multiple nodule positioning were 49.15% and 49.18%, respectively, with no statistically significant differences (p > 0.05). In addition, the method of positioning and the clinical characteristics of the patients were not found to be independent risk factors for the occurrence of complications. The detection rate of pulmonary nodules also showed some positive correlation with the spread of COVID-19 during the 2020-2022 period when COVID-19 was prevalent.
UNASSIGNED: When positioning a single node, the safety of positioning hooks is greater than when positioning multiple nodes, the safety of medical glue and positioning hooks is comparable, and the appropriate positioning method should be chosen according to the individual situation of the patient.

BACKGROUND: This study aimed to explore the efficacy of hookwire for computed tomography (CT)-guided pulmonary nodule (PN) localization before video-assisted thoracoscopic surgery (VATS) resection and determine the risk factors for localization-related complications.
METHODS: We enrolled 193 patients who underwent preoperative CT-guided PN hookwire localization. The patients were categorized into groups A (103 patients had no complications) and B (90 patients had complications) according to CT and VATS. Uni- and multivariate logistic regression analyses were used to identify risk factors for localization-related complications. A numerical rating scale was used to evaluate hookwire localization-induced pain.
RESULTS: We successfully performed localization in 173 (89.6%) patients. Pneumothorax was the main complication in 82 patients (42.5%). Patient gender, age, body mass index, tumor diameter, consolidation tumor ratio, pathologic diagnosis, position adjustment during location, lesion location, waiting time for surgery, and pleural adhesions were not significantly different between the two groups. The number of nodules, number of punctures, scapular rest position, and depth of insertion within the lung parenchyma were significant factors for successful localization. Multivariate regression analysis further validated the number of nodules, scapular rest position, and depth of insertion within the lung parenchyma as risk factors for hookwire-localization-related complications. Hookwire localization-induced pain is mainly mild or moderate pre- and postoperatively, and some patients still experience pain 7 days postoperatively.
CONCLUSIONS: Hookwire preoperative PN localization has a high success rate, but some complications remain. Thus, clinicians should be vigilant and look forward to further improvement.

UNASSIGNED: Associations between metabolic risk factors and lung cancer remain elusive, and evidence on the linkage between non-alcoholic fatty liver disease (NAFLD) and pulmonary nodules is limited. This study sought to examine the independent association between NAFLD and the risk of pulmonary nodules.
UNASSIGNED: Cross-sectional analyses of 1,119 patients with intestinal polyps hospitalized at the Department of Gastroenterology, Minhang District Central Hospital of Shanghai, China, were conducted. NAFLD was diagnosed based on hepatic ultrasonography or computed tomography (CT) findings of hepatic steatosis, with exclusion criteria ensuring patients had no history of significant alcohol consumption, viral infections, or hepatic autoimmune diseases. The currently accepted definition of a pulmonary nodule is a solid or sub-solid shadow ≤3 cm in diameter that appears as a solid or semi-solid pattern on a chest CT scan (our specific treatment is pulmonary nodule size: 5 mm to 3 cm). Adjusted 95% confidence intervals (CIs) and odds ratios (ORs) for NAFLD and the clinical features connected with pulmonary nodule risk were determined using a multivariable logistic regression analysis.
UNASSIGNED: Among the 979 intestinal polyp patients, the prevalence rates of NAFLD and pulmonary nodules were 25.9% and 32.8%, respectively. Patients with pulmonary nodules exhibited higher rates of NAFLD (31.5% vs. 23.3%, P=0.006) and obesity (41.4% vs. 32.5%, P=0.006) compared to those without pulmonary nodules. After removing all the possible confounding variables, the adjusted ORs for NAFLD, an older age, smoking, and obesity were 1.370 (95% CI: 1.006-1.867, P=0.04), 1.022 (95% CI: 1.010-1.033), 1.599 (95% CI: 1.033-2.475), and 1.410 (95% CI: 1.057-1.880), respectively (all P values <0.05). NAFLD showed a significant association with an increased risk of pulmonary nodules.
UNASSIGNED: NAFLD was independently linked to an increased incidence of pulmonary nodules in intestinal polyp patients, which emphasizes the importance of screening and managing these conditions in lung cancer prevention.