Quantitative lung tissue functional analysis for pulmonary adverse event risk assessment prior to thoracic radiotherapy

Background and purpose Pulmonary adverse events are a limiting factor in thoracic radiotherapy and more common in patients with pulmonary comorbidities. Parametric response mapping (PRM) is an effective method of providing 3-dimensional underlying lung comorbidity data. We hypothesized that a commercial PRM software could identify patients at increased risk of pulmonary adverse events during radiotherapy. Materials and methods Paired inspiratory/expiratory computed tomography (CT) scans obtained during simulation were processed using Lung Density Analysis (LDA) to classify lung tissue as Normal, Functional Low Density (FLD), Persistent Low Density (PLD), and Inspiration High Density (IHD). Patients were classified as elevated FLD, PLD, or IHD using predetermined thresholds. Multivariable models investigated associations between grade ≥ 2 pulmonary adverse events and (i) LDA measures, and (ii) clinical lung disease history. Model discrimination was assessed using area under the receiver operating characteristic curve (AUC). Results Among 98 patients receiving definitive-intent thoracic radiotherapy, 22 experienced grade ≥ 2 pulmonary adverse events. Elevated IHD (Odds Ratio [OR] = 2.95; 95% Confidence Interval [CI]:1.12–7.97; p = 0.03) and PLD (OR = 2.32; 95%CI:0.80–6.52; p = 0.11) were associated with increased odds of adverse events. The LDA-based multivariable model demonstrated higher, but not significantly improved, discrimination than a model with lung disease history (apparent AUC 0.768 vs. 0.735; p = 0.47; optimism-corrected AUC 0.705 vs. 0.686). Conclusion Elevated IHD was a significant predictor of pulmonary adverse events, with PLD demonstrating a similar but nonsignificant trend. LDA measures demonstrated predictive performance comparable to clinical lung disease history, suggesting potential utility as a risk-screening tool pending validation in larger cohorts.