ObjectiveTo explore the present state of research, emerging trends, and key topics in the field of liquid biopsy for lung cancer, offering insights for the holistic management of the disease. MethodsData was sourced from the Web of Science Core Collection database, focusing on literature related to liquid biopsy in lung cancer published between 2012 and 2025. Tools such as CiteSpace and Biblioshiny were employed to perform a detailed visual analysis of various aspects, including publication outputs, contributing countries and institutions, international collaborations, leading authors, prominent journals, academic disciplines, keyword distributions, and cited references. ResultsA total of 1 128 articles were analyzed. Findings indicated that research in the area of liquid biopsy for lung cancer experienced rapid growth since 2014, peaking in 2022. The majority of research efforts were centered in China and the United States. The French institution Institut National de la Sante et de la Recherche Medicale (INSERM) leaded in publication output. Malapelle U was the most prolific author. The journal Cancers published the highest number of related articles. Keywords analysis highlighted liquid biopsy and lung cancer as central research themes. Key research topics consistently included circulating tumor DNA, circulating tumor cells, extracellular vesicles, epidermal growth factor receptor mutations, and DNA methylation in the context of liquid biopsy. Meanwhile, immunotherapy and minimal residual disease emerged as frontier areas in this domain. ConclusionThe bibliometric results demonstrate a continuous rise in scholarly output on liquid biopsy in lung cancer. The identified research hotspots and evolving trends offer valuable guidance for future studies, with the ultimate goal of facilitating broader clinical adoption of liquid biopsy technologies and advancing precision medicine in lung cancer treatment.
ObjectiveTo explore the predictive value of the modified frailty index-11 (mFI-11) for postoperative complications in elderly lung cancer patients undergoing robot-assisted lobectomy. MethodsRetrospective collection of clinical data from lung cancer patients aged ≥65 years who underwent robot-assisted lobectomy at the Department of Thoracic Surgery, Gansu Provincial Hospital, from January 2022 to June 2025. Based on the optimal grouping threshold of 0.27 in previous studies for the mFI-11 score, patients were divided into a frail and a non-frail group. Postoperative complications of the two groups were analyzed, and multivariate logistic regression was used to assess the relationship between mFI-11 and postoperative complications. The receiver operating characteristic (ROC) curve was drawn to evaluate the predictive efficiency of mFI-11 for postoperative complications. ResultsA total of 161 patients were included, with 77 males and 84 females, and an average age of (68.48±2.90) years. Among them, 103 (64.0%) patients were in the non-frail group and 58 (36%) in the frail group. Differences between the two groups in terms of independent functional status, hypertension requiring drug control, history of type 2 diabetes, history of chronic obstructive pulmonary disease, American Society of Anesthesiologists classification, and tumor staging were all statistically significant (P<0.05). The length of postoperative hospital stay in the frail group was longer than that in the non-frail group [5.50 (5.00, 8.25) d vs. 5.00 (4.00, 5.00) d, P<0.001]. The incidence rates of general respiratory diseases (25.9% vs. 8.7%), hypoproteinemia (15.5% vs. 4.9%), arrhythmia (12.1% vs. 1.9%), bronchopleural fistula (5.2% vs. 0.0%), transfer to ICU for severe complications (10.3% vs. 1.0%), and readmission within 30 days after discharge (12.1% vs. 1.9%) were all higher in the frail group compared to the non-frail group (P<0.05). Multivariate logistic regression analysis found that mFI-11 had a better predictive efficiency for postoperative complications: general respiratory diseases [area under the curve (AUC)=0.759], hypoproteinemia (AUC=0.723), arrhythmia (AUC=0.795), transfer to ICU for severe complications (AUC=0.713), and readmission within 30 days after discharge (AUC=0.702). ConclusionmFI-11 can effectively predict postoperative complications in elderly lung cancer patients undergoing robot-assisted lobectomy and can serve as an objective indicator for identifying high-risk elderly lung cancer patients.