The sympathetic nervous system controls and regulates the activities of the heart and other organs. Sympathetic nervous system dysfunction leads to disease. Therefore, intraoperative real-time imaging of thoracic sympathetic nerves (ITSN) would be of great clinical significance for diagnosis and therapy. The aim of this experimental study was to evaluate the feasibility and validity of intraoperative ITSN using indocyanine green (ICG).Methods: ITSN using ICG was performed on 10 rabbits to determine its feasibility. Animals were allocated to two groups. The rabbits in one group received the same dose of ICG, but were observed at different times. The rabbits in the other group were administered different doses of ICG, but were observed at the same time. Signal to background ratio (SBR) was measured in regions of interest in all rabbits. Furthermore, fifteen consecutive patients with pulmonary nodules were intravenously injected with ICG 24 h preoperatively and underwent near-infrared (NIR) fluorescence imaging (FI) thoracoscopic surgeries between July 2015 and June 2016. A novel self-developed NIR and white-light dual-channel thoracoscope system was used. SBRs of thoracic sympathetic nerves were calculated in all patients.Results: In the preclinical study, we were able to precisely recognize each rabbit's second (T2) to fifth (T5) thoracic ganglia on both sides of the spine using ITSN with ICG. In addition, we explored the relationship between SBR and the injection time of ICG and that between SBR and the dose of ICG. Using the novel dual-channel thoracoscope system, we were able to locate the ganglia from the stellate ganglion (SG) to the sixth thoracic ganglion (T6), as well as the chains between these ganglia in all patients with a high SBR value of 3.26 (standard deviation: 0.57). The pathological results confirmed our findings.Conclusion: We were able to use ICG FI to distinguish thoracic sympathetic nerves during NIR thoracoscopic surgery. The technique may replace the rib-oriented method as standard practice for mapping the thoracic sympathetic nerves.

Lung cancer is one of the most common cancers and the predominant cause of cancer‐related death in the world. The low accuracy of early detection techniques and high risk of relapse greatly contribute to poor prognosis. An accurate clinical tool that can assist in diagnosis and surveillance is urgently needed. Circulating tumor DNA (ctDNA) is free DNA shed from tumor cells and isolated from peripheral blood. The genomic profiles of ctDNA have been shown to closely match those of the corresponding tumors. With the development of approaches with high sensitivity and specificity, ctDNA plays a vital role in the management of lung cancer as a result of its reproducible, non‐invasive, and easy‐to‐obtain characteristics. However, most previous studies have focused on advanced lung cancer. Few studies have investigated ctDNA in the early stages of the disease. In this review, we focus on ctDNA obtained from patients in the early stage of lung cancer, provide a summary of the related literature to date, and describe the main approaches to ctDNA and the clinical applications.

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Purpose: Nodule evaluation is challenging and critical to diagnose multiple pulmonary nodules (MPNs). We aimed to develop and validate a machine learning–based model to estimate the malignant probability of MPNs to guide decision-making. Experimental Design: A boosted ensemble algorithm (XGBoost) was used to predict malignancy using the clinicoradiologic variables of 1,739 nodules from 520 patients with MPNs at a Chinese center. The model (PKU-M model) was trained using 10-fold cross-validation in which hyperparameters were selected and fine-tuned. The model was validated and compared with solitary pulmonary nodule (SPN) models, clinicians, and a computer-aided diagnosis (CADx) system in an independent transnational cohort and a prospective multicentric cohort. Results: The PKU-M model showed excellent discrimination [area under the curve; AUC (95% confidence interval (95% CI)), 0.909 (0.854–0.946)] and calibration (Brier score, 0.122) in the development cohort. External validation (583 nodules) revealed that the AUC of the PKU-M model was 0.890 (0.859–0.916), higher than those of the Brock model [0.806 (0.771–0.838)], PKU model [0.780 (0.743–0.817)], Mayo model [0.739 (0.697–0.776)], and VA model [0.682 (0.640–0.722)]. Prospective comparison (200 nodules) showed that the AUC of the PKU-M model [0.871 (0.815–0.915)] was higher than that of surgeons [0.790 (0.711–0.852), 0.741 (0.662–0.804), and 0.727 (0.650–0.788)], radiologist [0.748 (0.671–0.814)], and the CADx system [0.757 (0.682–0.818)]. Furthermore, the model outperformed the clinicians with an increase of 14.3% in sensitivity and 7.8% in specificity. Conclusions: After its development using machine learning algorithms, validation using transnational multicentric cohorts, and prospective comparison with clinicians and the CADx system, this novel prediction model for MPNs presented solid performance as a convenient reference to help decision-making.

VATS was a safe and effective procedure for treatment of thymomas with satisfactory prognosis. MG with thymoma treated by VATS had comparable neurological outcome to that associated with the trans-sternal approach.