Clinical, Dosimetric and Radiomic Features Predictive of Lung Toxicity After (Chemo)Radiotherapy

Abstract

Background: Treatment of locally advanced non small cell lung cancer (LA-NSCLC) is based on (chemo)radiotherapy, which may cause acute lung toxicity: radiation pneumonitis (RP). Its frequency seems to increase by the use of adjuvant durvalumab therapy.

Aims: To identify clinical, dosimetric, and radiomic factors associated with grade (G)≥2 RP and build a prediction model based on selected parameters.

Patients and methods: This is a retrospective multicenter cohort study including patients receiving radiation therapy between 2015 and 2019 for LA-NSCLC. Baseline computed tomography scanners were segmented to extract radiomic features from the "Lung - Tumor" volume. Variables associated with the risk of G≥2 RP in the descriptive analysis were then selected for explanatory analysis, followed by predictive analysis.

Results: 153 patients were included in 4 centers (51 with G≥2 RP). Factors associated with G≥2 RP included a high initial hemoglobin level, dosimetric factors (mean dose to healthy lungs, lung V20Gy and V13Gy), the addition of maintenance durvalumab, and 7 radiomic features (intensity distribution and texture). Other factors were associated with an increased risk of G≥2 RP in our explanatory model, such as older age, low Tiffeneau ratio, and a decreased initial platelet count. The best-performing predictive model was a random forest-based learning model using clinical, dosimetric, and radiomic variables, with an area under the ROC curve of 0.72 (95%CI [0.63; 0.80]) versus 0.64 for models using one type of data.

Conclusion: The addition of radiomic features to clinical and dosimetric ones improves prediction of the occurrence of G≥2 RP in patients receiving radiotherapy for lung cancer.

Keywords: Artificial intelligence; Lung cancer; Radiation pneumonitis; Radiation therapy; Radiomics.