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Purpose: This study aimed to systematically assess the quality and performance of computed tomography (CT) radiomics studies in predicting brain metastasis (BM) among patients with lung cancer.
Methods: The PubMed, Embase and Web of Science were searched for studies predicting BM in patients with lung cancer using CT-based radiomics features. Information regarding patients, imaging, and radiomics analysis was extracted from eligible studies. We assessed the quality of included studies using the Radiomics Quality Scoring (RQS) tool and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A meta-analysis of studies regarding the prediction of BM in patients with lung cancer was performed.
Results: Thirteen studies were identified, with sample sizes ranging from 75 to 602. The mean RQS of the studies was 12 (range 9-16), and the corresponding percentage of the score was 33.55 % (range 25.00-44.44 %). Four studies (30.8 %) were considered as low risk of bias, while the remaining nine studies (69.2 %) were considered to have unclear risks. The meta-analysis included twelve studies. The pooled sensitivity, specificity and Area Under the Curve (AUC) value with 95 % confidence intervals were 0.75 [0.69, 0.80], 0.76 [0.68, 0.82], and 0.81 [0.77-0.84], respectively.
Conclusion: CT radiomics-based models show promising results as a non-invasive method to predict BM in lung cancer patients. However, multicenter and prospective studies are warranted to enhance the stability and acceptance of radiomics.
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http://dx.doi.org/10.1016/j.clinimag.2024.110275 | DOI Listing |
JAMA Netw Open
September 2025
Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea.
Importance: Patients with advanced cancer frequently receive broad-spectrum antibiotics, but changing use patterns across the end-of-life trajectory remain poorly understood.
Objective: To describe the patterns of broad-spectrum antibiotic use across defined end-of-life intervals in patients with advanced cancer.
Design, Setting, And Participants: This nationwide, population-based, retrospective cohort study used data from the South Korean National Health Insurance Service database to examine broad-spectrum antibiotic use among patients with advanced cancer who died between July 1, 2002, and December 31, 2021.
Minerva Surg
September 2025
Unit of Geriatric Medicine, Department of Emergency, Foresea Life Insurance Guangzhou General Hospital, Guangzhou, China -
J Neurooncol
September 2025
Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA.
Purpose: Frailty measures are critical for predicting outcomes in metastatic spine disease (MSD) patients. This study aimed to evaluate frailty measures throughout the disease process.
Methods: This retrospective analysis measured frailty in MSD patients at multiple time points using a modified Metastatic Spinal Tumor Frailty Index (MSTFI).
J Robot Surg
September 2025
Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, UT Health San Antonio, 7703 Floyd Curl Drive, 7836, San Antonio, TX, 78229-3900, USA.
To evaluate intraoperative ventilatory mechanics during robotic-assisted hysterectomy in obese women with endometrial cancer and introduce the concept of a physiologic "ceiling effect" in respiratory strain. We conducted a retrospective cohort study of 89 women with biopsy-confirmed endometrial cancer who underwent robotic-assisted total hysterectomy between 2011 and 2015. Intraoperative ventilatory parameters, including plateau airway pressure and static lung compliance, were recorded at five-minute intervals.
View Article and Find Full Text PDFJ Cancer Res Clin Oncol
September 2025
Institute for Community Medicine, Section Epidemiology of Health Care and Community Health, University Medicine Greifswald, Greifswald, Germany.
Purpose: The German sector-based healthcare system poses a major challenge to continuous patient monitoring and long-term follow-up, both essential for generating high-quality, longitudinal real-world data. The national Network for Genomic Medicine (nNGM) bridges the inpatient and outpatient care sectors to provide comprehensive molecular diagnostics and personalized treatment for non-small cell lung cancer (NSCLC) patients in Germany. Building on the established nNGM infrastructure, the DigiNet study aims to evaluate the impact of digitally integrated, personalized care on overall survival (OS) and the optimization of treatment pathways, compared to routine care.
View Article and Find Full Text PDF