The aim of this study was to evaluate the risk of thrombo-embolic events (TEE) in patients with germ-cell tumours (GCT) who receive cisplatin-based chemotherapy, to compare this risk to that of a matched control group of non-GCT cancer patients, and to identify risk factors of TEE. The rate of TEE during the 6 months following the initiation of chemotherapy was assessed in 100 consecutive patients with GCT and in 100 controls with various neoplasms who were matched on sex and age, and who received first-line cisplatin-based chemotherapy during the same period of time at Institut Gustave Roussy, Villejuif, France. Data were subsequently tested on a validation group of 77 GCT patients treated in Lyon, France. A total of 19 patients (19%) (95% confidence interval (CI): 13 -28) and six patients (6%) (95% CI: 3 -13) had a TEE in the GCT group and the non-GCT control group, respectively (relative risk (RR): 3.4; Po0.01). Three patients from the GCT group died of pulmonary embolism. In multivariate analysis, two factors had independent predictive value for TEE: a high body surface area (41.9 m 2 ) (RR: 5 (1.8 -13.9)) and an elevated serum lactate dehydrogenase (LDH) (RR: 6.4 (2.3 -18.2)). Patients with no risk factor (n ¼ 26) and those with at least one risk factor (n ¼ 71) had a probability of having a TEE of 4% (95% CI: 1 -19) and 26% (95% CI: 17 -37), respectively. In the GCT validation set, 10 (13%) patients had a TEE; patients with no risk factor and those with at least one risk factor had a probability of having a TEE of 0 and 17% (95% CI: 10 -29), respectively. Patients with GCT are at a higher risk for TEE than patients with non-GCT cancer while on cisplatin-based chemotherapy. This risk can be accurately predicted by serum LDH and body surface area. This predictive index may help to study prospectively the impact of thromboprophylaxis in GCT patients.
Background: Immune checkpoint inhibitors (ICI) represent a major change in nonsmall cell lung cancer (NSCLC) treatment, however robust biomarkers are needed. Emerging data suggest that features discovered by deep learning (DL) models from CT scan images using artificial intelligence (AI) algorithms can accurately predict outcomes. In this study, our objective was to explore the potential of AI-based DL radiomics models in patients with advanced NSCLC treated with ICI.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.