Thoracic Cancer最新文献

Background: With the widespread adoption of low-dose CT screening, the detection of pulmonary ground-glass nodules (GGNs) has risen markedly, presenting diagnostic challenges in distinguishing preinvasive lesions from invasive adenocarcinomas (IAC). This study aimed to develop a machine learning (ML)-based model using artificial intelligence (AI)-extracted CT radiomic features to predict the invasiveness of GGNs.

Methods: A retrospective cohort of 285 patients (148 with preinvasive lesions, 137 with IAC) from the Lingnan Campus was divided into training and internal validation sets (8:2). An independent cohort of 210 patients (118 with preinvasive lesions, 92 with IAC) from the Tianhe Campus served as external validation. Nineteen radiomic features were extracted and filtered using Boruta and LASSO algorithms. Seven ML classifiers were evaluated using AUC-ROC, decision curve analysis (DCA), and SHAP interpretability.

Results: Median CT value, skewness, 3D long-axis diameter, and transverse diameter were ultimately selected for model construction. Among all classifiers, the Gradient Boosting Machine (GBM) model achieved the best performance (AUC = 0.965 training, 0.908 internal validation, and 0.965 external validation). It demonstrated strong accuracy (88.1%), specificity (80.7%), and F1 score (0.87) in the external validation cohort. The GBM model demonstrated superior net clinical benefit. SHAP analysis identified median CT value and skewness as the most influential predictors.

Conclusion: This study presents a simplified ML model using AI-extracted radiomic features, which has strong predictive performance and biological interpretability for preoperative risk stratification of GGNs. By leveraging median CT value, skewness, 3D long-axis diameter, and transverse diameter, the model enables accurate and noninvasive differentiation between IAC and indolent lesions, supporting precise surgical planning.

Introduction: Previous studies have reported inconsistent findings regarding the associationbetween ALK and ROS1 rearrangements in lung cancer and thromboembolic risk. This retrospective study aimed to investigate this association in advanced lung adenocarcinoma patients with ALK, ROS1, RET rearrangements, and EGFR mutations.

Materials and methods: We retrospectively collected information on patients with advanced lung adenocarcinoma in the First Affiliated Hospital of Zhejiang University School of Medicine from January 2013 to March 2021. All patients with confirmed ALK, ROS1, or RET rearrangements, as well as a comparison cohort of those with EGFR mutation, were included. Clinical characteristics were analyzed, and the association between driver genes and TE risks was analyzed using competing risk and logistic regression.

Results: A total of 546 patients were included in the study. Among them, those with ROS1 rearrangements exhibited the highest cumulative incidence of thromboembolic events (TEs), reaching 17.5% ± 0.2% during the peri-diagnostic period (within 6 months following diagnosis). Regardless of the entire follow-up or the peri-diagnostic period, ROS1 rearrangements were significantly associated with an increased risk of TEs. Multivariate analysis revealed ROS1 rearrangements, the number of comorbidities, the size of mediastinal lymph nodes, and elevated C-reactive protein (CRP) levels as TE risk factors during the peri-diagnostic period. Throughout the follow-up period, ROS1 rearrangements and hypertension were independent TE risk factors. In addition, the development of TE significantly affected the overall survival of patients with EGFR mutations.

Conclusion: ROS1 rearrangements were significantly associated with an increased risk of TE.

Retraction: L. Zhao, X. Zhang, Y. Shi, and T. Teng, "LncRNA SNHG14 Contributes to the Progression of NSCLC Through miR-206/G6PD Pathway," Thoracic Cancer 11, no. 5 (2020): 1202-1210, https://doi.org/10.1111/1759-7714.13374. The above article, published online on 09 March 2020 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, Tateaki Naito; and John Wiley & Sons Australia, Ltd. The retraction has been agreed upon following concerns raised by a third party regarding the use of an incorrect primer for the SNHG14 gene. The authors were invited to provide comments and supporting data but did not respond. Given the nature of the concern, the editors consider the results and conclusions of this article invalid. The authors did not respond to our notice of retraction.

Objective: To investigate the current status of decisional conflict in lung cancer patients receiving systemic therapy and to analyze its influencing factors, with the aim of providing a basis for developing decision support strategies.

Methods: From August to September 2024, a convenience sample of 500 patients receiving systemic therapy for lung cancer at the Department of Medical Oncology, Cancer Hospital, Chinese Academy of Medical Sciences, was surveyed. Data were collected using a general information questionnaire, the Decisional Conflict Scale (DCS), Cancer Patient's Involvement in Treatment Decision-Making Scale (CPITDM), Preparation for Decision-Making Scale (PreDM), and Decisional Regret Scale (DRS).

Result: The mean DCS score was 47.28 ± 15.83, with subscale scores ranking from highest to lowest as decision support/effectiveness, decision uncertainty, and information/values. The mean CPITDM, PreDM, and DRS scores were 28.56 ± 3.91, 63.02 ± 11.65, and 9.46 ± 2.62, respectively. DCS was negatively correlated with CPITDM (r = -0.188, p < 0.001) and PreDM (r = -0.303, p < 0.001) but positively correlated with DRS (r = 0.342, p < 0.001). Multiple regression identified occupation, medical payment, treatment line, pathology, medication type, patient involvement, and preparedness as significant influencing factors (p < 0.05), explaining 59.9% of variance.

Conclusion: Lung cancer patients receiving systemic therapy experience a relatively high level of decisional conflict, with many exhibiting delayed decision-making. Healthcare providers should identify high-risk patients early based on key influencing factors and explore practical clinical decision support interventions. Enhancing decision readiness and reducing decision regret may help to improve quality of life and reduce decisional conflict.

Background: Lung cancer is a leading cause of cancer-related deaths. Perioperative therapies, including neoadjuvant chemo-immunotherapy, have improved outcomes, but combining them with antiangiogenic drugs may offer further benefits. This study evaluated the 3-year efficacy and safety of neoadjuvant sintilimab, anlotinib, and chemotherapy in resectable NSCLC patients from the TD-NeoFOUR trial.

Methods: The study included 45 patients who received neoadjuvant treatment with anlotinib, sintilimab, and platinum-based chemotherapy. The primary endpoint was overall survival (OS), and the secondary endpoint was event-free survival (EFS). The Kaplan-Meier method was used to estimate survival curves, and the log-rank test was used to compare survival rates between subgroups.

Results: As of November 11, 2024, all 45 patients had been followed up for a median of 35.7 months. The estimated 3-year EFS rate was 84.3%, and the estimated 3-year OS rate was 86.7%. Subgroup analysis showed that patients achieving pathological complete response (pCR) and major pathological response (MPR) had significantly higher 3-year EFS and OS rates compared to patients with non-pCR and non-MPR. No new treatment-related adverse events (TRAEs) occurred during the 3-year follow-up, indicating the long-term safety of the treatment regimen.

Conclusions: The combination of neoadjuvant chemo-immunotherapy and antiangiogenic drugs significantly improved long-term survival outcomes in patients with resectable NSCLC. This treatment regimen is a promising option for improving prognosis in this patient population.

Patients with advanced lung cancer are candidates for systemic therapies. In the context of improved tumor responses and prolonged survival periods, the treatment of tumor/therapy-related complications must be taken into account. Rescue surgery consists of a surgical resection without oncologic purpose but with the aim of controlling an acute and life-threatening complication. We evaluated the postoperative outcomes of patients with advanced stage lung cancer who underwent rescue surgery for tumor or therapy-related life-threatening complications. We conducted a systematic review of literature using PubMed, Scopus, Embase, and Google Scholar using following keywords: ("rescue surgery" or "salvage surgery" or "salvage lung resection") and ("lung cancer" or "non-small cell lung cancer" or "NSCLC" or "SCLC"). The primary outcome was overall survival. Secondary outcomes were the morbidity and mortality. Nine articles were included in our review for a total of 64 patients. The most common indications for rescue surgery were lung abscess, post-obstructive pneumonia, hemoptysis, and empyema. The lung resection consisted of lobectomy (n = 31, 48%), bilobectomy (n = 5, 8%), pneumonectomy (n = 11, 17%), sleeve pneumonectomy (n = 15, 23%), sleeve lobectomy (n = 1, 2%), and segmentectomy (n = 1, 2%). The mean overall survival was 12 months; the postoperative complication rate was 51%. No intraoperative deaths were observed. Rescue surgery is feasible for patients with advanced lung cancer and tumor/therapy-related life-threatening complications. Rescue surgery may allow access to ulterior systemic therapies; but the risk-benefit imbalance should always be taken into account, considering this as a last resort treatment.

Background: While established biomarkers predict immunotherapy response in advanced nonsmall cell lung cancer (NSCLC), additional noninvasive imaging biomarkers may enhance treatment selection. Pretreatment computed tomography (CT) texture analysis may provide tumor characterization to predict survival outcomes.

Methods: We conducted a systematic review and meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Cochrane Library databases were searched. Study quality was assessed using the quality in prognosis studies (QUIPS) tool. Hazard ratios (HRs) with 95% confidence intervals (CIs) were pooled using random-effects models.

Results: Ten retrospective studies involving 2400 patients were included. Patients stratified as low-risk based on CT texture features demonstrated significantly improved survival outcomes compared to high-risk patients. The included studies used diverse radiomic features for risk stratification, including texture features from gray-level co-occurrence matrix (GLCM) such as entropy and dissimilarity, first-order statistical parameters including skewness and kurtosis, gray-level run-length matrix (GLRLM) features, and deep learning-derived features. Meta-analysis of five studies (n = 1102) revealed that patients stratified as low-risk based on these quantitative CT texture signatures had substantially better overall survival (OS) (p < 0.0001) with minimal heterogeneity (I² = 0.0%). Similarly, progression-free survival (PFS) analysis of five studies (n = 1799) showed significant benefit for low-risk patients (p < 0.0001), though with moderate heterogeneity (I² = 71.7%).

Conclusions: Pretreatment quantitative CT texture analysis effectively predicts survival outcomes in advanced NSCLC patients receiving immunotherapy, providing clinically meaningful risk stratification. This noninvasive imaging approach may serve as an additional tool to complement established pathological and molecular biomarkers, including liquid biopsy, for enhanced personalized treatment selection.

Background: To evaluate the target localization accuracy of image registration methods in cone beam computed tomography (CBCT)-based image guidance (IG) for lung stereotactic body radiation therapy (SBRT) and the associations with tumor characteristics such as size, mobility, and location.

Methods: Four methods involving different matching axes and regions were used to register the planning CT and 3D CBCT images of 36 lung tumors treated with SBRT. The registration axes were divided into 3D translational axes and 6D axes (translational and rotational axes), and the regions were divided into wide rectangular (WR) volume-of-interest (VOI) and tumor-focused (TF) VOI, consisting of the internal target volume (ITV) plus a 1 cm margin.

Results: Compared with the WR registrations, the TF registrations yielded higher localization accuracies for all registration pairs, with differences of 6.3%-9.1% in the percentage of inclusion of the registered CBCT gross tumor volume (GTV) into the ITV and 1.3-1.8 mm in the 3D distance between the ITV and registered CBCT GTV centroids. The TF3D and TF6D registrations yielded similar accuracy metrics of 91.9% and 1.4 mm, respectively, whereas the WR6D registration exhibited improved accuracies compared with the WR3D registration. The localization accuracies with the TF registrations decreased with increasing tumor mobility index, expressed as the ITV/GTV50% ratio, with significant differences between the tumor groups at a cutoff of 1.7.

Conclusions: The localization accuracies of our image registration methods may serve as clinically useful references for selecting the most suitable registration in CBCT-based IG for lung SBRT.