Differentiating Immune Checkpoint Inhibitor-Related Pneumonitis From COVID-19 Pneumonia Using a CT-based Radiomics Nomogram

Abstract Objectives This study aimed to develop and validate a radiomics nomogram that effectively distinguishes between immune checkpoint inhibitor-related pneumonitis (CIP) and COVID-19 pneumonia using radiographic imaging features. Methods We included 97 patients in this study, identifying 269 pneumonia lesions—159 from COVID-19 and 110 from CIP. The dataset was randomly divided into a training set (70% of the data) and a validation set (30%). We extracted radiomics features from corticomedullary and nephrographic phase-contrast computed tomography (CT) images, constructed a radiomics signature, and calculated a radiomics score (Rad-score). Using these features, we built models with three classifiers and assessed demographics and CT findings to create a clinical factors model. We then constructed a radiomics nomogram that combines the Rad-score with independent clinical factors and evaluated its performance in terms of calibration, discrimination, and clinical usefulness. Results In constructing the radiomics signature, 33 features were critical for differentiating between CIP and COVID-19 pneumonia. The support vector machine classifier was the most accurate of the three classifiers used. The Rad-score, gender, lesion location, radiological features, and lesion borders were included in the nomogram. The nomogram demonstrated superior predictive performance, significantly outperforming the clinical factors model in the training set (AUC comparison, p = 0.02638). Calibration curves indicated good fit in both training and validation sets, and the nomogram displayed greater net benefit compared to the clinical model. Conclusion The radiomics nomogram emerges as a noninvasive, quantitative tool with significant potential to differentiate between CIP and COVID-19 pneumonia. It enhances diagnostic accuracy and supports radiologists, especially in overburdened medical systems, through the use of machine learning predictions.