Background: Preoperative localization for small invisible and impalpable pulmonary nodules is important in single-port video-assisted thoracoscopic surgery (VATS). Localization of multiple pulmonary nodules during VATS resection remains challenging. The aim of our study is to elucidate the efficacy of preoperative CT-guided methylene blue localization of both single and multiple pulmonary nodules.Methods: Consecutive patients undergoing preoperative CT-guided methylene blue dye localization for lung nodules, followed by VATS resection, were retrospectively analyzed between January 2014 and November 2019. Chi-square tests, Fisher's exact test and independent T-test were used to compare variables between the groups. Logistic regression was used to identify risk factors for procedure-related complications.Results: A total of 388 patients, including 337 with single nodule and 51 with multiple nodules, were analyzed. The success rate of preoperative CT-guided methylene blue localization for both single and multiple pulmonary nodules were comparable as 98.8% (333/337) vs. 100% (108/108). The procedure time was longer (23.2 ± 9.4 vs. 7.6 ± 4.8 min, p < 0.001) and risk of pneumothorax was higher (47.1 vs. 25.5%, p = 0.002) in the multiple nodule group. The procedure time (OR 1.079; 95% CI = 1.041–1.118; p < 0.001) was an independent risk factor for pneumothorax. Nodule depth (OR 2.829; 95% CI = 1.259–6.356; p = 0.011) was an independent risk factor for pulmonary hemorrhage.Conclusions: Preoperative CT-guided methylene blue localization for both single and multiple pulmonary nodules is safe, feasible, and effective.

Background: Recent advance in tissue characterization with parametric mapping imaging has the potential to be a novel biomarker for histopathologic correlation with thymic epithelial tumors (TETs). The purpose of our study is to evaluate MRI T1 mapping with the calculation of extracellular volume (ECV) fraction for histologic correlation with thymic epithelial tumor based on lymphocyte abundance. Methods: A retrospective study including 31 consecutive patients (14 men and 17 women, median age, 56 years; interquartile range, 12 years) with TETs was performed. The T1 values and ECV were assessed by using quantitative MRI mapping techniques. Mann-Whitney U test, Kruskal-Wallis H test, and receiver operating characteristic curve analyses were used to assess discrimination between different types of TETs based on lymphocyte abundance. Results: Extracellular volume was significantly higher in TETs with sparse lymphocyte, including type A, type B3, and thymic carcinoma, compared with those with abundant lymphocyte, including type B1, B2, and AB thymomas (42.5% vs 26.9%, respectively; p < 0.001). Extracellular volume was significantly higher in thymic carcinoma compared with low grade and high grade thymomas (48.6% vs 31.1% vs 27.6%, respectively; p = 0.002). Conclusions: T1 mapping with the calculation of extracellular volume (ECV) fraction correlate with the WHO histologic classification of thymic epithelial tumor based on lymphocyte abundance.

Persisting programmed cell death-1 (PD-1) signaling impairs T cell effector function, which is highly associated with T cell exhaustion and immunotherapy failure. However, the mechanism responsible for PD-1 deubiquitination and T cell dysfunction remains unclear. Here, we show that ubiquitin-specific peptidase 24 (USP24) promotes PD-1 protein stability by removing K48-linked polyubiquitin. Increased interleukin-6 level transcriptionally activates the USP24 expression, which leads to PD-1 stabilization. Furthermore, USP24 deficiency reduces PD-1 levels in CD8 ⁺ T cells and attenuates Egfr ^L858R -driven lung tumorigenesis in Usp24 ^C1695A catalytic deficient mice. Targeting PD-1 stability with the USP24-specific inhibitor USP24-i-101 boosts cytotoxic T cell activity, restrains lung tumor growth, and achieves superior therapeutic effects when combined with anti-CTLA4 immunotherapy. Clinically, patients with lung cancer exhibiting high USP24 expression in tumor-infiltrating CD8 ⁺ T cells display exhausted features and show unfavorable responses to immunotherapy. Our findings dissect the mechanism for regulating enhanced PD-1 stability in tumor-infiltrating CD8 ⁺ T cells and reveal USP24 as a potential target of antitumor immunotherapy.

Background: The prognostic significance of cardiac radiation dose in esophageal cancer after definitive concurrent chemoradiotherapy (CCRT) remains largely unknown. We aimed to investigate the association between cardiac dose-volume parameters and overall survival (OS) in esophageal squamous cell carcinoma (ESCC) after definitive CCRT. Methods: One hundred and twenty-one ESCC patients undergoing definitive CCRT with intensity modulated radiotherapy technique between 2008 and 2018 were reviewed. Cardiac dose-volume parameters were calculated. Survival of patients and cumulative incidence of adverse events were estimated by the Kaplan–Meier method and compared between groups by the log-rank test. The prognostic significance of cardiac dose-volume parameters was determined with multivariate Cox proportional hazards regression analysis. Results: Median follow-up was 16.2 months (range, 4.3–109.3). Median OS was 18.4 months. Heart V5, V10, and V20 were independent prognostic factors of OS. Median OS was longer for patients with heart V5 ≤ 94.3% (24.7 vs. 16.3 months, p = 0.0025), heart V10 ≤ 86.4% (24.8 vs. 16.9 months, p = 0.0041), and heart V20 ≤ 76.9% (20.0 vs. 17.2 months, p = 0.047). Lower cumulative incidence of symptomatic cardiac adverse events was observed among patients with heart V5 ≤ 94.3% (p = 0.017), heart V10 ≤ 86.4% (p = 0.02), and heart V20 ≤ 76.9% (p = 0.0057). Patients without symptomatic cardiac adverse events had a higher 3-year OS rate (33.8% vs. 0%, p = 0.03). Conclusions: Cardiac radiation dose inversely correlated with survival in ESCC after definitive CCRT. Radiation dose to the heart should be minimized.

Purpose: To compare the diagnostic performance of radiomic analysis with machine learning (ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs).Methods: A retrospective study was performed in patients with PMTs and undergoing surgical resection or biopsy in National Cheng Kung University Hospital, Tainan, Taiwan, E-Da Hospital, Kaohsiung, Taiwan, and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan between January 2010 and December 2019. Clinical data including age, sex, myasthenia gravis (MG) symptoms and pathologic diagnosis were collected. The datasets were divided into UECT (unenhanced computed tomography) and CECT (enhanced computed tomography) for analysis and modelling. Radiomics model and 3D CNN model were used to differentiate TETs from non-TET PMTs (including cyst, malignant germ cell tumor, lymphoma and teratoma). The macro F1-score and receiver operating characteristic (ROC) analysis were performed to evaluate the prediction models.Result: In the UECT dataset, there were 297 patients with TETs and 79 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 83.95%, ROC-AUC = 0.9117) had better performance than the 3D CNN model (macro F1-score = 75.54%, ROC-AUC = 0.9015). In the CECT dataset, there were 296 patients with TETs and 77 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 85.65%, ROC-AUC = 0.9464) had better performance than the 3D CNN model (macro F1-score = 81.01%, ROC-AUC = 0.9275).Conclusion: Our study revealed that the individualized prediction model integrating clinical information and radiomic features using machine learning demonstrated better predictive performance in the differentiation of TETs from other PMTs at chest CT scan than 3D CNN model.