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.

BACKGROUND: With the popularization of computed tomography, more and more pulmonary nodules (PNs) are being detected. Risk stratification of PNs is essential for detecting early-stage lung cancer while minimizing the overdiagnosis of benign nodules. This study aimed to develop a circulating tumor DNA (ctDNA) methylation-based, non-invasive model for the risk stratification of PNs.
METHODS: A blood-based assay (\"LUNG-TRAC\") was designed to include novel lung cancer ctDNA methylation markers identified from in-house reduced representative bisulfite sequencing data and known markers from the literature. A stratification model was trained based on 183 ctDNA samples derived from patients with benign or malignant PNs and validated in 62 patients. LUNG-TRAC was further single-blindly tested in a single- and multi-center cohort.
RESULTS: The LUNG-TRAC model achieved an area under the curve (AUC) of 0.810 (sensitivity = 74.4 % and specificity = 73.7 %) in the validation set. Two test sets were used to evaluate the performance of LUNG-TRAC, with an AUC of 0.815 in the single-center test (N = 61; sensitivity = 67.5 % and specificity = 76.2 %) and 0.761 in the multi-center test (N = 95; sensitivity = 50.7 % and specificity = 80.8 %). The clinical utility of LUNG-TRAC was further assessed by comparing it to two established risk stratification models: the Mayo Clinic and Veteran Administration models. It outperformed both in the validation and the single-center test sets.
CONCLUSIONS: The LUNG-TRAC model demonstrated accuracy and consistency in stratifying PNs for the risk of malignancy, suggesting its utility as a non-invasive diagnostic aid for early-stage peripheral lung cancer.
BACKGROUND: www.
RESULTS: gov (NCT03989219).

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.

Radiomics can quantify pulmonary nodule characteristics non-invasively by applying advanced imaging feature algorithms. Radiomic textural features derived from Computed Tomography (CT) imaging are broadly used to predict benign and malignant pulmonary nodules. However, few studies have reported on the radiomics-based identification of nodular Pulmonary Cryptococcosis (PC).
This study aimed to evaluate the diagnostic and differential diagnostic value of radiomic features extracted from CT images for nodular PC.
This retrospective analysis included 44 patients with PC (29 males, 15 females), 58 with Tuberculosis (TB) (39 males, 19 females), and 60 with Lung Cancer (LC) (20 males, 40 females) confirmed pathologically. Models 1 (PC vs. non-PC), 2 (PC vs. TB), and 3 (PC vs. LC) were established using radiomic features. Models 4 (PC vs. TB) and 5 (PC vs. LC) were established based on radiomic and CT features.
Five radiomic features were predictive of PC vs. non-PC model, but accuracy and Area Under the Curve (AUC) were 0.49 and 0.472, respectively. In model 2 (PC vs. TB) involving six radiomic features, the accuracy and AUC were 0.80 and 0.815, respectively. Model 3 (PC vs. LC) with six radiomic features performed well, with AUC=0.806 and an accuracy of 0.76. Between the PC and TB groups, model 4 combining radiomics, distribution, and PI, showed AUC=0.870. In differentiating PC from LC, the combination of radiomics, distribution, PI, and RBNAV achieved AUC=0.926 and an accuracy of 0.90.
The prediction models based on radiomic features from CT images performed well in discriminating PC from TB and LC. The individualized prediction models combining radiomic and CT features achieved the best diagnostic performance.

OBJECTIVE: This study aims to assess the diagnostic utility of circulating tumor cells (CTCs) in conjunction with low-dose computed tomography (LDCT) for differentiating between benign and malignant pulmonary nodules and to substantiate the foundation for their integration into clinical practice.
METHODS: A systematic literature review was performed independently by two researchers utilizing databases including PubMed, Web of Science, The Cochrane Library, Embase, and Medline, to collate studies up to September 15, 2023, that investigated the application of CTCs in diagnosing pulmonary nodules. A meta-analysis was executed employing Stata 15.0 and Revman 5.4 to calculate the pooled sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC). Additionally, trial sequential analysis was conducted using dedicated TSA software.
RESULTS: The selection criteria identified 16 studies, encompassing a total of 3409 patients. The meta-analysis revealed that CTCs achieved a pooled sensitivity of 0.84 (95% CI 0.80 to 0.87), specificity of 0.80 (95% CI 0.73 to 0.86), PLR of 4.23 (95% CI 3.12 to 5.72), NLR of 0.20 (95% CI 0.16 to 0.25), DOR of 20.92 (95% CI 13.52 to 32.36), and AUC of 0.89 (95% CI 0.86 to 0.93).
CONCLUSIONS: Circulating tumor cells demonstrate substantial diagnostic accuracy in distinguishing benign from malignant pulmonary nodules. The incorporation of CTCs into the diagnostic protocol can significantly augment the diagnostic efficacy of LDCT in screening for malignant lung diseases.

BACKGROUND: To investigate the risk factors of pneumothorax of using computed tomography (CT) guidance to inject autologous blood to locate isolated lung nodules.
METHODS: In the First Hospital of Putian City, 92 cases of single small pulmonary nodules were retrospectively analyzed between November 2019 and March 2023. Before each surgery, autologous blood was injected, and the complications of each case, such as pneumothorax and pulmonary hemorrhage, were recorded. Patient sex, age, position at positioning, and nodule type, size, location, and distance from the visceral pleura were considered. Similarly, the thickness of the chest wall, the depth and duration of the needle-lung contact, the length of the positioning procedure, and complications connected to the patient\'s positioning were noted. Logistics single-factor and multi-factor variable analyses were used to identify the risk factors for pneumothorax. The multi-factor logistics analysis was incorporated into the final nomogram prediction model for modeling, and a nomogram was established.
RESULTS: Logistics analysis suggested that the nodule size and the contact depth between the needle and lung tissue were independent risk factors for pneumothorax.
CONCLUSIONS: The factors associated with pneumothorax after localization are smaller nodules and deeper contact between the needle and lung tissue.

UNASSIGNED: Whether nasal administration of esketamine can provide effective analgesia is unclear in patients with acute pain after preoperative CT-guided needle localization.
UNASSIGNED: In this double-blind, randomized, placebo-controlled trial, patients were assigned to receive either nasal administration of esketamine (0.3 mg/kg or 0.5 mg/kg) or saline (identical in appearance to esketamine) when they had visual analog scale (VAS) pain scores >3/10 during deep breathing after preoperative CT-guided needle localization. The primary outcome was the percentage of patients with satisfactory pain relief, which was defined as VAS pain scores ≤3/10 measured 15 min after intranasal of esketamine or saline. Secondary outcomes included VAS measured following esketamine or saline, the incidence and cumulative dose of rescue hydromorphone use, and related adverse events.
UNASSIGNED: A total of 90 patients were included in the final analysis. Following intranasal treatment, the percentage of patients with satisfactory pain relief was 16.7% (5/30) in the saline group, 56.7% (17/30) in the 0.3 mg/kg esketamine group, and 53.3% (16/30) in the 0.5 mg/kg esketamine group (p = 0.002). The median VAS during deep breathing was less after the intranasal administration of esketamine {median (IQR), 3 (3, 5) in 0.3 mg/kg or 0.5 mg/kg esketamine compared to the saline group [5 (4, 6)], p = 0.009}. The incidence of rescue hydromorphone use was detected less in the esketamine group compared to the saline group (43.3% in the 0.3 mg/kg esketamine group, 36.7% in the 0.5 mg/kg esketamine group, and 73.3% in the saline group, p = 0.010). The adverse events were similar among the three groups (p > 0.05).
UNASSIGNED: Intranasal administration of esketamine is easier and more effective in alleviating acute pain in patients after preoperative CT-guided needle localization without significant adverse effects.

The aim of this study is to examine the precision of semi-automatic, conventional and automatic volumetry tools for pulmonary nodules in chest CT with phantom N1 LUNGMAN. The phantom is a life-size anatomical chest model with pulmonary nodules representing solid and subsolid metastases. Gross tumor volumes (GTVis) were contoured using various approaches: manually (0); as a means of semi-automated, conventional contouring with (I) adaptive-brush function; (II) flood-fill function; and (III) image-thresholding function. Furthermore, a deep-learning algorithm for automatic contouring was applied (IV). An intermodality comparison of the above-mentioned strategies for contouring GTVis was performed. For the mean GTVref (standard deviation (SD)), the interquartile range (IQR)) was 0.68 mL (0.33; 0.34-1.1). GTV segmentation was distributed as follows: (I) 0.61 mL (0.27; 0.36-0.92); (II) 0.41 mL (0.28; 0.23-0.63); (III) 0.65 mL (0.35; 0.32-0.90); and (IV) 0.61 mL (0.29; 0.33-0.95). GTVref was found to be significantly correlated with GTVis (I) p < 0.001, r = 0.989 (III) p = 0.001, r = 0.916, and (IV) p < 0.001, r = 0.986, but not with (II) p = 0.091, r = 0.595. The Sørensen-Dice indices for the semi-automatic tools were 0.74 (I), 0.57 (II) and 0.71 (III). For the semi-automatic, conventional segmentation tools evaluated, the adaptive-brush function (I) performed closest to the reference standard (0). The automatic deep learning tool (IV) showed high performance for auto-segmentation and was close to the reference standard. For high precision radiation therapy, visual control, and, where necessary, manual correction, are mandatory for all evaluated tools.

UNASSIGNED: Radiomics models could help assess the benign and malignant invasiveness and prognosis of pulmonary nodules. However, the lack of interpretability limits application of these models. We thus aimed to construct and validate an interpretable and generalized computed tomography (CT) radiomics model to evaluate the pathological invasiveness in patients with a solitary pulmonary nodule in order to improve the management of these patients.
UNASSIGNED: We retrospectively enrolled 248 patients with CT-diagnosed solitary pulmonary nodules. Radiomic features were extracted from nodular region and perinodular regions of 3 and 5 mm. After coarse-to-fine feature selection, the radiomics score (radscore) was calculated using the least absolute shrinkage and selection operator logistic method. Univariate and multivariate logistic regression analyses were performed to determine the invasiveness-related clinicoradiological factors. The clinical-radiomics model was then constructed using the logistic and extreme gradient boosting (XGBoost) algorithms. The Shapley additive explanations (SHAP) method was then used to explain the contributions of the features. After removing batch effects with the ComBat algorithm, we assessed the generalization of the explainable clinical-radiomics model in two independent external validation cohorts (n=147 and n=149).
UNASSIGNED: The clinical-radiomic XGBoost model integrating the radscore, CT value, nodule length, and crescent sign demonstrated better predictive performance than did the clinical-radiomics logistic model in assessing pulmonary nodule invasiveness, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.889 [95% confidence interval (CI), 0.848-0.927] in the training cohort. The SHAP algorithm illustrates the contribution of each feature in the final model. The specific model decision process was visualized using a tree-based decision heatmap. Satisfactory generalization performance was shown with AUCs of 0.889 (95% CI, 0.823-0.942) and 0.915 (95% CI, 0.851-0.963) in the two external validation cohorts.
UNASSIGNED: An interpretable and generalized clinical-radiomics model for predicting pulmonary nodule invasibility was constructed to help clinicians determine the invasiveness of pulmonary nodules and devise assessment strategies in an easily understandable manner.