Differentiation between immune checkpoint inhibitor‐related and radiation pneumonitis in lung cancer by CT radiomics and machine learning

Abstract

Purpose Consolidation immunotherapy after completion of chemoradiotherapy has become the standard of care for unresectable locally advanced non‐small cell lung cancer and can induce potentially severe and life‐threatening adverse events, including both immune checkpoint inhibitor‐related pneumonitis (CIP) and radiation pneumonitis (RP), which are very challenging for radiologists to diagnose. Differentiating between CIP and RP has significant implications for clinical management such as the treatments for pneumonitis and the decision to continue or restart immunotherapy. The purpose of this study is to differentiate between CIP and RP by a CT radiomics approach. Methods We retrospectively collected the CT images and clinical information of patients with pneumonitis who received immune checkpoint inhibitor (ICI) only (n = 28), radiotherapy (RT) only (n = 31), and ICI+RT (n = 14). Three kinds of radiomic features (intensity histogram, gray‐level co‐occurrence matrix [GLCM] based, and bag‐of‐words [BoW] features) were extracted from CT images, which characterize tissue texture at different scales. Classification models, including logistic regression, random forest, and linear SVM, were first developed and tested in patients who received ICI or RT only with 10‐fold cross‐validation and further tested in patients who received ICI+RT using clinicians’ diagnosis as a reference. Results Using 10‐fold cross‐validation, the classification models built on the intensity histogram features, GLCM‐based features, and BoW features achieved an area under curve (AUC) of 0.765, 0.848, and 0.937, respectively. The best model was then applied to the patients receiving combination treatment, achieving an AUC of 0.896. Conclusions This study demonstrates the promising potential of radiomic analysis of CT images for differentiating between CIP and RP in lung cancer, which could be a useful tool to attribute the cause of pneumonitis in patients who receive both ICI and RT.

Development and Validation of a Radiomics Nomogram Using Computed Tomography for Differentiating Immune Checkpoint Inhibitor-Related Pneumonitis From Radiation Pneumonitis for Patients With Non-Small Cell Lung Cancer

“…The results were in line with ours, but the predictive efficacy of this model was much lower. Cheng et al collected the CT images of 73 NSCLC patients with ICI or RT and extracted only 3 types of radiomics features ( 31 ). They figured out that the kind of bag-of-word features achieved an AUC of 0.937.…”

Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images

“…Among the remaining patients, 30 of them developed ICIP, resulting in an incidence of 8.50% which is comparable to the data reported in previous studies ( Sears et al, 2019 ; Atchley et al, 2021 ). We used the same criteria in a previous study to define ICIP ( Cheng et al, 2022a ). After excluding six patients who did not have CT scans before the start of immunotherapy, we finally got 24 patients for the ICIP dataset.…”

Immunotherapy in unresectable stage III non-small-cell lung cancer: state of the art and novel therapeutic approaches

“…Radiomics holds great promises to aid in making the correct diagnosis. 110,111 Importantly, although translational immunophenotyping studies unravel the underlying mechanisms of immunotherapy-induced pneumonitis, rational immunomodulatory treatment strategies that do not impact tumor surveillance should be pursued. [112][113][114] Finally, a severe limitation of current ongoing RCTs is that they are not biomarker driven and all unresectable NSCLCs are mixed together in clinical trials.…”