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

SIMPLE SUMMARY: In our research, we analyzed the CT scans of 322 advanced lung cancer patients over time to see how long they might remain disease-free after undergoing a specific treatment called EGFR-TKI. By integrating the patterns from these scans with other medical data, such as gene mutations and treatment strategies, we improved our ability to predict the course of the disease. However, when we included data from multiple centers, the consistency of our findings reduced. Simply put, our technique can offer doctors a glimpse into the future progression of lung cancer, and aid in tailoring treatments. This approach could be groundbreaking in lung adenocarcinoma treatment, but it needs further investigation. 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.