Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients

Ting Wei Wang, Heng Sheng Chao, Hwa Yen Chiu, Yi Hui Lin, Hung Chun Chen, Chia Feng Lu, Chien Yi Liao, Yen Lee, Tsu Hui Shiao, Yuh Min Chen, Jing Wen Huang*, Yu Te Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal.

Original languageEnglish
Article number5125
JournalCancers
Volume15
Issue number21
DOIs
StatePublished - Nov 2023

Keywords

  • computer tomography (CT) scans
  • delta radiomics signatures
  • epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKI)
  • lung adenocarcinoma
  • personalized treatment strategies
  • progression-free survival (PFS)
  • time-variable radiomics

Fingerprint

Dive into the research topics of 'Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients'. Together they form a unique fingerprint.

Cite this