Progress in Serial Imaging for Prognostic Stratification of Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis
by
, , , and
Hwa-Yen Chiu
1,2,3,4,
Ting-Wei Wang
1,2,
Ming-Sheng Hsu
1,
Heng-Shen Chao
1,4,5,
Chien-Yi Liao
6,
Chia-Feng Lu
6,
Yu-Te Wu
2,* and
Yuh-Ming Chen
4,* 1
School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
2
Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
3
Department of Internal Medicine, Taipei Veterans General Hospital, Hsinchu Branch, Chutong 310, Taiwan
4
Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
5
Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
6
Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
*
Authors to whom correspondence should be addressed.
Cancers 2024, 16(3), 615; https://doi.org/10.3390/cancers16030615
Submission received: 30 November 2023
/
Revised: 23 January 2024
/
Accepted: 29 January 2024
/
Published: 31 January 2024
(This article belongs to the Special Issue Immunotherapy with Checkpoint Inhibitors for Non-small Cell Lung Cancer)
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.
Abstract
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.
