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Cancer Survival Analysis

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Integrating ctDNA Analysis and Radiomics for Dynamic Risk Assessment in Localized Lung Cancer.

Everett J Moding^1,2, Mohammad Shahrokh Esfahani^2,3, Cheng Jin¹

¹Department of Radiation Oncology, Stanford University, Stanford, California.

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|April 29, 2025

Summary

This summary is machine-generated.

Combining tumor features, radiomics, and circulating tumor DNA (ctDNA) analysis significantly enhances outcome prediction for non-small cell lung cancer (NSCLC) patients undergoing chemoradiotherapy (CRT). This integrated approach supports personalized, response-adapted therapies to improve patient outcomes and reduce treatment toxicity.

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Area of Science:

Oncology
Radiology
Genomics

Background:

Non-small cell lung cancer (NSCLC) treatment often involves chemoradiotherapy (CRT).
Accurate prediction of treatment outcomes is crucial for optimizing patient management.
Current prediction models may not fully capture the complexity of treatment response.

Purpose of the Study:

To evaluate the efficacy of an integrated model combining tumor features, radiomics, and ctDNA analysis for outcome prediction in NSCLC patients treated with CRT.
To explore the potential for personalized and response-adapted therapies.

Main Methods:

Analysis of tumor features, radiomic data extracted from medical imaging, and circulating tumor DNA (ctDNA) levels.
Development and validation of an integrated predictive model.
Correlation of model predictions with patient outcomes.

Main Results:

The integrated model demonstrated improved accuracy in predicting outcomes compared to models using single data types.
Radiomics and ctDNA analysis provided complementary information for outcome prediction.
The model's predictive power suggests potential for guiding personalized treatment strategies.

Conclusions:

Combining tumor features, radiomics, and ctDNA analysis offers a powerful approach for predicting outcomes in NSCLC patients receiving CRT.
This integrated strategy can facilitate the development of personalized and response-adapted therapies.
Improved outcome prediction may lead to reduced treatment toxicity and enhanced patient survival.