Pneumonitis Prediction Modeling of a Prospective 4DCT-Ventilation Functional Avoidance Clinical Trial

Abstract

<h3>Purpose/Objective(s)</h3> Functional avoidance radiotherapy proposes to use functional imaging to reduce pulmonary toxicity by designing radiotherapy treatment plans that reduce doses to functional regions of the lung. A novel form of lung functional imaging has been proposed that uses 4DCT imaging to calculate 4DCT-based lung ventilation (4DCT-ventilation) maps. A phase II, multi-center, prospective study was completed that showed that 4DCT-ventilation functional avoidance radiotherapy results in decreased ≥ grade 2 pneumonitis when compared to historical controls. Studies are needed that apply machine learning methods to evaluate the large prospective trial dataset to elucidate which factors are most critical in predicting toxicity. The purpose of this work is to use machine learning to predict ≥ grade 2 radiation pneumonitis for patients treated with 4DCT-ventilation functional avoidance radiotherapy. <h3>Materials/Methods</h3> Lung cancer patients receiving curative chemo-radiotherapy (doses of 45-75 Gy) were accrued from 2 institutions. Patient 4DCTs along with image processing techniques were used to generate 4DCT-ventilation images. The 4DCT-ventilation images were used to generate functional avoidance plans that reduced doses to functional portions of the lung while delivering the prescribed tumor dose and respecting tolerances of organs-at-risk. 49 factors were initially used in a machine learning feature selection algorithm, and 36 factors with the highest participation were selected to train 26 classifiers to predict ≥ grade 2 pneumonitis. The assessed factors included patients (age, gender, performance status, etc.), clinical (immunotherapy, surgery, chemotherapy), dose metrics (lung, esophagus, heart, and PTV), and metrics that combine 4DCT-ventilation based function and dose (e.g., V20 to lung portions with ≥50% function). The classifiers were trained using a 5-fold cross-validation scheme. The performance of the classifiers was evaluated using accuracy and receiver operating characteristic area under the curve (AUC). <h3>Results</h3> Of the 67 accrued patients, 10 (14.9%) developed ≥ grade 2 radiation pneumonitis. The best performing classifier was a decision tree classifier with an accuracy of 0.92 and an AUC of 0.75. Dose-function and heart dosimetric indices were the metrics most predictive of ≥ grade 2 pneumonitis. <h3>Conclusion</h3> This is the first study to comprehensively evaluate factors predictive of pneumonitis for patients treated on a prospective 4DCT-ventilation functional avoidance clinical trial. Machine learning methods revealed that lung dose-function metrics and heart doses were significant in predicting toxicity. The results validate the clinical significance in reducing doses to functional portions of the lung and provide seminal clinical guidance for functional avoidance thoracic radiotherapy.