KEY TAKEAWAYS
- The study aimed to identify high-risk NSCLC patients for CIP using quantitative imaging analysis.
- The study concluded that evaluating GGO volume and distribution on pre-treatment CT scans aids in managing CIP risk.
Immune checkpoint inhibitors (ICIs) may induce life-threatening pneumonitis, and pre-existing interstitial lung abnormalities (ILAs) elevate the risk of checkpoint inhibitor pneumonitis (CIP) in non-small cell lung cancer. The subjective assessment of ILA and the lack of standardized methods impede its clinical utility as a predictive factor.
Xinyue Wang and the team conducted a study that aimed to identify NSCLC patients at high risk of CIP through quantitative imaging methods.
The cohort study involved 206 cases in the training group and 111 cases in the validation set, involving locally advanced or metastatic NSCLC patients treated with ICI therapy. Utilizing a deep learning algorithm, interstitial lesions were labeled, and their volume was computed.
Two predictive models were constructed to estimate the likelihood of grade ≥ 2 CIP or severe CIP (grade ≥ 3). Cox proportional hazard models were utilized to evaluate factors associated with progression-free survival (PFS).
The results demonstrated that among 206 patients in the training cohort, 21.4% experienced CIP. Two predictive models were constructed to estimate the likelihood of CIP based on distinct predictors.
Model 1 integrated age, histology, and the preexisting percentage of ground glass opacity (GGO) across the entire lung to forecast grade ≥ 2 CIP, whereas Model 2 employed histology and GGO % in the right lower lung to predict grade ≥ 3 CIP. These models underwent validation and were assessed for accuracy. Furthermore, in an additional exploratory analysis, the presence of GGOs spanning multiple lobes on pretreatment CT scans was recognized as a risk factor for PFS.
The study concluded that evaluating the volume and distribution of GGOs on pre-treatment CT scans could aid in monitoring and mitigating the risk of CIP in NSCLC patients undergoing ICI therapy.
Utilizing quantitative imaging and computational analysis, this study provided a valuable approach to identifying high-risk NSCLC patients for CIP, facilitating enhanced risk management and potentially leading to improved outcomes among those receiving ICI treatment.
The study received funding from the National Natural Science Foundation of China and the Tianjin Key Medical Discipline (Specialty) Construction Project.
Source: https://pubmed.ncbi.nlm.nih.gov/38408928/
Wang, X., Zhao, J., Mei, T. et al. “Quantification of preexisting lung ground glass opacities on CT for predicting checkpoint inhibitor pneumonitis in advanced non-small cell lung cancer patients.” BMC Cancer 24, 269 (2024). https://doi.org/10.1186/s12885-024-12008-z
