Incidence rate of occult lymph node metastasis in clinical T1-2N0M0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study : Original research.

BACKGROUND: This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data. METHODS: By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV). RESULTS: The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients. CONCLUSIONS: The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.

Three-Dimensional Computed Tomography Bronchography and Angiography-Guided Thoracoscopic Segmentectomy for Pulmonary Nodules.

BACKGROUND: Three-dimensional computed tomography bronchography and angiography (3D-CTBA) provides detailed imaging information for pulmonary segmentectomy. This study was performed to verify the feasibility of 3D-CTBA-guided thoracoscopic segmentectomy for the treatment of pulmonary nodules. METHODS: A retrospective analysis was performed on all patients who underwent 3D-CTBA-guided uniport thoracoscopic segmentectomies or subsegmentectomies for pulmonary nodules in the period from May 2019 to May 2020. All of the information related to perioperative management and surgical operations was retrieved from the medical records and operating notes for detailed analysis. RESULTS: A total of 104 eligible operations involving the resection of 110 nodules with diameters in the range of 5-20 mm were included. Under 3D-CTBA guidance, the pulmonary nodules were located with an accuracy of 100% (110/110) and the median resection margin was 24.3 mm (17-33 mm). Additionally, the segmental (subsegmental) bronchi, arteries, and veins were identified with accuracy rates of 100% (104/104), 96.2% (100/104), and 94.2% (98/104), respectively. The postoperative complications consisted of 3 cases of pulmonary infection (2.9%), 6 cases of arrhythmia (5.8%), 2 cases of hemoptysis (1.9%), 4 cases of air leak (3.8%), and 2 cases of subcutaneous emphysema (1.9%). No perioperative death occurred. CONCLUSION: 3D-CTBA-guided thoracoscopic segmentectomy is an effective surgical approach for the management of pulmonary nodules.

Development and validation of a GRGPI model for predicting the prognostic and treatment outcomes in head and neck squamous cell carcinoma.

Background: Head and neck squamous cell carcinoma (HNSCC) is among the most lethal and most prevalent malignant tumors. Glycolysis affects tumor growth, invasion, chemotherapy resistance, and the tumor microenvironment. Therefore, we aimed at identifying a glycolysis-related prognostic model for HNSCC and to analyze its relationship with tumor immune cell infiltrations. Methods: The mRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA), while glycolysis-related genes were obtained from the Molecular Signature Database (MSigDB). Bioinformatics analysis included Univariate cox and least absolute shrinkage and selection operator (LASSO) analyses to select optimal prognosis-related genes for constructing glycolysis-related gene prognostic index(GRGPI), as well as a nomogram for overall survival (OS) evaluation. GRGPI was validated using the Gene Expression Omnibus (GEO) database. A predictive nomogram was established based on the stepwise multivariate regression model. The immune status of GRGPI-defined subgroups was analyzed, and high and low immune groups were characterized. Prognostic effects of immune checkpoint inhibitor (ICI) treatment and chemotherapy were investigated by Tumor Immune Dysfunction and Exclusion (TIDE) scores and half inhibitory concentration (IC50) value. Reverse transcription-quantitative PCR (RT-qPCR) was utilized to validate the model by analyzing the mRNA expression levels of the prognostic glycolysis-related genes in HNSCC tissues and adjacent non-tumorous tissues. Results: Five glycolysis-related genes were used to construct GRGPI. The GRGPI and the nomogram model exhibited robust validity in prognostic prediction. Clinical correlation analysis revealed positive correlations between the risk score used to construct the GRGPI model and the clinical stage. Immune checkpoint analysis revealed that the risk model was associated with immune checkpoint-related biomarkers. Immune microenvironment and immune status analysis exhibited a strong correlation between risk score and infiltrating immune cells. Gene set enrichment analysis (GSEA) pathway enrichment analysis showed typical immune pathways. Furthermore, the GRGPIdel showed excellent predictive performance in ICI treatment and drug sensitivity analysis. RT-qPCR showed that compared with adjacent non-tumorous tissues, the expressions of five genes were significantly up-regulated in HNSCC tissues. Conclusion: The model we constructed can not only be used as an important indicator for predicting the prognosis of patients but also had an important guiding role for clinical treatment.

Th17 cell frequency and IL-17A production in peripheral blood of patients with non-small-cell lung cancer.

OBJECTIVE: This study investigated the frequency of T-helper (Th)17 lymphocytes and production of cytokine interleukin (IL)-17 in peripheral blood of patients with non-small-cell lung cancer (NSCLC) and their use as a marker of clinical value. METHODS: Sixty patients with NSCLC and 60 healthy volunteers were enrolled in the study. Flow cytometry was used to detect the frequency of Th17 lymphocytes in peripheral blood, and enzyme-linked immunosorbent assay (ELISA) was used to detect serum levels of IL-17. We analyzed the association of Th17 lymphocytes and IL-17 levels in the peripheral blood of patients with their clinicopathological features. RESULTS: Frequency of Th17 lymphocytes and production of IL-17 were significantly higher in the NSCLC group than in the control group and were higher in patients with a smoking history compared with non-smokers. Moreover, Th17 lymphocyte and IL-17 expression levels were higher in patients with squamous cell carcinoma than in patients with adenocarcinoma, and significantly higher in patients with stage III and IV cancers than in patients at stage I or II. CONCLUSION: Th17 lymphocytes and IL-17 play an important role in the development of NSCLC in patients and may have clinical value as markers for treatment of NSCLC.