Improved Immunotherapy Response Prediction in NSCLC With Deep-Learning Radiomic Biomarker

Objective:

To evaluate the effectiveness of a deep learning radiomic biomarker (Serial CTRS) in predicting overall survival in advanced NSCLC patients receiving immune checkpoint inhibitors, specifically focusing on its comparative performance against traditional methods.

Key Findings:

• Serial CTRS was significantly associated with overall survival, with hazard ratios of 0.74 (95% CI: 0.70–0.79) in the test cohort and 0.45 (95% CI: 0.32–0.65) in the GARNET cohort, outperforming RECIST and tumor volume change.
• Hazard ratios indicated strong predictive capability for overall survival in both test (HR = 6.19, 95% CI = 4.12–9.28) and GARNET (HR = 18.00, 95% CI = 5.40–59.97) cohorts.
• The biomarker maintained predictive value across PD-L1 and RECIST subgroups.

Interpretation:

Serial CTRS is a validated, automated biomarker that enhances survival predictions in NSCLC patients treated with immune checkpoint inhibitors, suggesting significant potential for improved clinical decision-making and patient outcomes.

Limitations:

• Retrospective nature of the study may introduce biases, including selection and information biases.
• Further validation needed across diverse therapeutic modalities to ensure generalizability.

Conclusion:

Serial CTRS could facilitate more accurate early treatment assessments in clinical practice and trials, enhancing patient management in advanced NSCLC.