Functional-based multi-omics early prediction of radiation pneumonitis in NSCLC using AI-generated perfusion and ventilation from planning CT

Abstract

Objective. This study aims to develop a functional-based multi-omics model for early prediction of radiation pneumonitis (RP) by extracting radiomic and dosiomic features from functionally defined lung regions, using generated perfusion (Q) and ventilation (V) from pre-radiotherapy planning computed tomography (CT). Approach. We retrospectively analyzed data from 121 patients with locally advanced non-small cell lung cancer treated with curative-intent intensity-modulated radiotherapy between 2015 and 2019, including pre-treatment CT and dose maps. Q and V maps were generated from CT with deep learning-based and supervoxel-based approaches, respectively. Regions of interest (ROIs) combined the planning target volume with each of three functional lung regions—high functional lung (HFL), low functional lung, and whole lung (WL)—defined by thresholds on Q and V maps. Radiomic and dosiomic features were extracted from CT and dose distributions within each ROI. For each ROI, three methods—radiomics (R), dosiomics (D), and dual-omics (RD)—were constructed. 13 machine learning algorithms were trained and evaluated using 10-fold cross-validation, and model performance was assessed by the average area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score. RP was defined as CTCAE grade ⩾2. Main results. Of the 35 selected features, 20 were from HFL. In dual-omics models, using HFL features improved predictive performance for RP (AUC 0.879 ± 0.105) compared to WL (AUC 0.778 ± 0.100). In HFL, the RD method outperformed both R (AUC 0.786 ± 0.076) and D (AUC 0.791 ± 0.107) methods. Decision curve analysis showed the dual-omics model based on HFL provided the highest net benefit across threshold probabilities. Significance. This study is the first to systematically demonstrate that features extracted from CT-derived HFL capture important functional differences and provide strong predictive value for RP. Compared to conventional methods, integrating radiomics, dosiomics, and CT-based functional information further improves predictive performance.