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The following is a summary of “Quantification of preexisting lung ground glass opacities on CT for predicting checkpoint inhibitor pneumonitis in advanced non-small cell lung cancer patients,” published in the February 2024 issue of Oncology by Wang et al.

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment landscape for non-small cell lung cancer (NSCLC), yet they come with the risk of potentially life-threatening pneumonitis, especially in patients with pre-existing interstitial lung abnormalities (ILAs). However, the subjective nature of ILA assessment and the lack of standardized methods hinder its clinical applicability as a predictive factor for checkpoint inhibitor pneumonitis (CIP). In this cohort study involving 206 patients in the training set and 111 in the validation set, comprising locally advanced or metastatic NSCLC patients undergoing ICI therapy, the researchers aimed to identify individuals at high risk of CIP using quantitative imaging techniques. Employing a deep learning algorithm, the study group labeled interstitial lesions and computed their volume, subsequently developing two predictive models to estimate the probability of grade???2 CIP or severe CIP (grade???3). The models incorporated various predictors such as age, histology, and the percentage of pre-existing ground glass opacity (GGO) of the whole lung or specific lung regions. Validation of these models demonstrated promising accuracy. 

Additionally, the exploratory analysis revealed that the presence of GGOs involving multiple lobes on pre-treatment CT scans was associated with a higher risk of progression-free survival. These findings underscore the potential of quantifying GGO volume and distribution on pre-treatment CT scans to facilitate the monitoring and management of CIP risk in NSCLC patients undergoing ICI therapy.


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