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Progress In Serial Imaging For Prognostic Stratification Of Lung Cancer Patients Receiving Immunotherapy: A Systematic Review And Meta-analysis.

Home
Research Domains
Biomedical And Clinical Sciences
Oncology And Carcinogenesis
Predictive And Prognostic Markers
Progress In Serial Imaging For Prognostic Stratification Of Lung Cancer Patients Receiving Immunotherapy: A Systematic Review And Meta-analysis.

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

Hwa-Yen Chiu^1,2,3,4, Ting-Wei Wang^1,2, Ming-Sheng Hsu¹

¹School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.

|February 10, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Delta radiomics shows promise in predicting treatment response for lung cancer patients receiving immunotherapy. This meta-analysis found it effective in stratifying patients for better survival outcomes.

Keywords:

computed tomography immune checkpoint inhibitor immunotherapy non-small cell lung cancer

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Area of Science:

Medical Imaging
Oncology
Radiology

Background:

Immunotherapy, especially checkpoint inhibitors, has transformed non-small cell lung cancer (NSCLC) treatment.
Identifying patients likely to respond to immunotherapy is critical for treatment optimization.
Predictive biomarkers are actively being researched to improve patient selection.

Purpose of the Study:

To evaluate the predictive performance of delta radiomics in stratifying lung cancer patients undergoing immunotherapy.
To synthesize existing evidence on delta radiomics for predicting treatment response and survival outcomes.

Main Methods:

A meta-analysis was performed following PRISMA guidelines.
Studies were systematically searched in PubMed, Embase, Web of Science, and the Cochrane Library.

treatment outcome

Ten studies were included for qualitative synthesis and pooled analysis.

Main Results:

Radiomic models demonstrated significant predictive power for 6-month response (AUC 0.81).
Delta radiomics predicted improved progression-free survival (HR 4.77) and overall survival (HR 2.15) at 6 months.
The pooled analysis confirmed the prognostic value of radiomics in immunotherapy for lung cancer.

Conclusions:

Delta radiomics is a promising tool for predicting outcomes in lung cancer patients treated with immunotherapy.
Further research is warranted to compare delta radiomics with traditional radiomics and deep-learning approaches.
Radiomics can aid in personalizing lung cancer treatment strategies.