Progress in Serial Imaging for Prognostic Stratification of Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis.

Autor:	Chiu, Hwa-Yen, Wang, Ting-Wei, Hsu, Ming-Sheng, Chao, Heng-Shen, Liao, Chien-Yi, Lu, Chia-Feng, Wu, Yu-Te, Chen, Yuh-Ming
Předmět:	ONLINE information services MEDICAL databases META-analysis MEDICAL information storage & retrieval systems CONFIDENCE intervals IMMUNE checkpoint inhibitors SYSTEMATIC reviews LUNG tumors DIAGNOSTIC imaging RADIOMICS TREATMENT effectiveness DESCRIPTIVE statistics RESEARCH funding MEDLINE PREDICTION models PROGRESSION-free survival COMPUTED tomography IMMUNOTHERAPY OVERALL survival
Zdroj:	Cancers; Feb2024, Vol. 16 Issue 3, p615, 15p
Abstrakt:	Simple Summary: Immunotherapy with checkpoint inhibitors is a promising treatment for lung cancer patients. However, not all patients respond well to immunotherapy, and researchers are seeking new predictive biomarkers for immunotherapy. Radiomics and its derivative, delta radiomics, are potential candidates for use as predictive biomarkers for use in immunotherapy. In this meta-analysis, we performed qualitative and quantitative assessments and confirmed the effectiveness of delta radiomics in predicting the treatment responses and clinical outcomes of immunotherapy. Further studies are warranted to compare the performance of traditional radiomics and deep-learning radiomics. Immunotherapy, particularly with checkpoint inhibitors, has revolutionized non-small cell lung cancer treatment. Enhancing the selection of potential responders is crucial, and researchers are exploring predictive biomarkers. Delta radiomics, a derivative of radiomics, holds promise in this regard. For this study, a meta-analysis was conducted that adhered to PRISMA guidelines, searching PubMed, Embase, Web of Science, and the Cochrane Library for studies on the use of delta radiomics in stratifying lung cancer patients receiving immunotherapy. Out of 223 initially collected studies, 10 were included for qualitative synthesis. Stratifying patients using radiomic models, the pooled analysis reveals a predictive power with an area under the curve of 0.81 (95% CI 0.76–0.86, p < 0.001) for 6-month response, a pooled hazard ratio of 4.77 (95% CI 2.70–8.43, p < 0.001) for progression-free survival, and 2.15 (95% CI 1.73–2.66, p < 0.001) for overall survival at 6 months. Radiomics emerges as a potential prognostic predictor for lung cancer, but further research is needed to compare traditional radiomics and deep-learning radiomics. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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