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Radiation pneumonitis (RP) is a significant and relatively common complication of (chemo) radiotherapy (RT) in the treatment of locally advanced non-small cell lung cancer (LA-NSCLC).
This study investigates clinical, dosimetric, and radiomic features predictive of lung toxicity, specifically grade (G)≥2 RP, in patients undergoing (chemo)RT for LA-NSCLC, with the aim to build a predictive model to estimate its occurrence.
The researchers conducted a retrospective multicenter analysis of 153 patients treated with (chemo)RT, between 2015 and 2019, to identify key risk factors. Baseline CT scans were segmented to extract radiomic features from the lungs and the tumor, and integrate them with clinical and dosimetric features.
The study employed a machine learning (ML) approach using logistic regression and random forest models to develop predictive models for RP occurrence in this patient population.
The clinical and dosimetric risk factors linked to an increased RP risk included high initial hemoglobin levels, older age, low Tiffeneau ratio (FEV1/VC), decreased initial platelet count, dosimetric factors (mean dose to lungs, lung V20Gy and V13Gy), and the use of adjuvant durvalumab.
Seven radiomic features related to intensity distribution and texture were significantly associated with RP risk.
The developed ML-based model (random forest) integrating clinical, dosimetric and radiomic data achieved the best performance with an AUC = 0.72 (95% CI [0.63-0.80]), outperforming models based on combined clinical and dosimetric data (AUC = 0.64), or on radiomic data alone (AUC=0.64).
Integrating radiomic features with clinical and dosimetric ones improves the prediction of RP, providing a more comprehensive tool for risk stratification in lung cancer patients undergoing radiotherapy.
This study showed that identifying high-risk patients for RP could allow for a more personalized treatment planning to reduce their risk, such as adjusting radiation dose constraints, introducing protective measures, or enhancing follow-up care.
Explore this infographic to learn more about this project and the predictive model developed by Evin et al’s.
Evin. C, et al. Clin Lung Cancer. 2024 Nov 20:S1525-7304(24)00248-1. doi: 10.1016/j.cllc.2024.11.003
Principal Investigator: Eleonor Rivin del Campo, MD, PhD
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