AUTHOR=Qiu Qingtao , Xing Ligang , Wang Yu , Feng Alei , Wen Qiang 

TITLE=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

JOURNAL=Frontiers in Immunology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.870842

DOI=10.3389/fimmu.2022.870842

ISSN=1664-3224

ABSTRACT=Backgrounds
The combination of immunotherapy and chemoradiotherapy has become the standard therapeutic strategy for patients with unresected locally advanced stage non-small cell lung cancer (NSCLC) and induced treatment-related adverse effects, particularly immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP). The aim of this study is to differentiate between CIP and RP by the pretreatment CT radiomics and clinical or radiological parameters.

Methods
A total of 126 advanced stage NSCLC patients with pneumonitis were enrolled in this retrospective study and divided into training dataset (n =88) and validation dataset (n = 38). A total of 837 radiomics features were extracted from regions of interest (ROIs) based on the lung parenchyma window of CT images. A radiomics signature was constructed on the basis of the predictive features by the least absolute shrinkage and selection operator (LASSO). A logistic regression was applied to develop radiomics nomogram. Receiver operating characteristics (ROC) curve and area under the curve (AUC) were applied to evaluate the performance of pneumonitis etiology identification.

Results
There was no significant difference between the training and validation datasets for any clinicopathological parameters in this study. The radiomics signature, named Rad-score, consisting of 11 selected radiomics features, has potential ability to differentiate between CIP and RP with the empirical and α-binormal-based AUCs of 0.891 and 0.896. These results were verified in the validation dataset with AUC = 0.901 and 0.874, respectively. The clinical and radiological parameters of bilateral changes (p < 0.001) and sharp border (p = 0.001) were associated with identification of CIP and RP. The nomogram model showed good performance on discrimination in the training dataset (AUC = 0.953 and 0.950) and validation dataset (AUC = 0.947 and 0.936).

Conclusions
CT-based radiomics features have potential values for differentiating between patients with CIP and RP. Addition of bilateral changes and sharp border produced superior model performance on classifying, which could be a useful method to improve related clinical decision-making.