Table S1-S10 from Integrating ctDNA Analysis and Radiomics for Dynamic Risk Assessment in Localized Lung Cancer

Everett J. Moding; Mohammad Shahrokh Esfahani; Cheng Jin; Angela B. Hui; Barzin Y. Nabet; Yufei Liu; Jacob J. Chabon; Michael S. Binkley; David M. Kurtz; Emily G. Hamilton; Aadel A. Chaudhuri; Chih Long Liu; Zhe Li; Rene F. Bonilla; Alice L. Jiang; Brianna Lau; Pablo Lopez; Jianzhong He; Yawei Qiao; Ting Xu; Luyang Yao; Saumil Gandhi; Zhongxing Liao; Millie Das; Kavitha Ramchandran; Sukhmani K. Padda; Joel W. Neal; Heather A. Wakelee; Michael F. Gensheimer; Billy W. Loo; Ruijiang Li; Steven H. Lin; Ash A. Alizadeh; Maximilian Diehn

doi:10.1158/2159-8290.29819569

Key Points

MAIN FINDING: Integration of ctDNA analysis and radiomics improves risk assessment in localized lung cancer.
KEY EVIDENCE: CIRI-LCRT predicted progression-free survival using metrics from cell-free DNA and radiomic analysis.
APPROACH: The study utilized anonymized clinical data and various biological and molecular metrics for analysis.
SIGNIFICANCE: This approach may lead to more tailored treatment strategies and better outcomes in lung cancer patients.

Implication

Analysis reveals improved risk assessment in localized lung cancer, suggesting ctDNA and radiomics enhance predictive models.

Abstract

Anonymized clinical and demographic data, cell-free DNA metrics, somatic mutation data, radiomic, biological, and molecular metrics, and CIRI-LCRT predicted progression-free survival.

Discussion

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Cite This Study

Moding et al. (2025) studied this question.

synapsesocial.com/papers/689a0f86e6551bb0af8d097c https://doi.org/10.1158/2159-8290.29819569

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Table S1-S10 from Integrating ctDNA Analysis and Radiomics for Dynamic Risk Assessment in Localized Lung Cancer

Key Points

Implication

Abstract

Discussion

Cite This Study

Table S1-S10 from Integrating ctDNA Analysis and Radiomics for Dynamic Risk Assessment in Localized Lung Cancer

Key Points

Implication

Abstract

Discussion

Cite This Study

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