Artificial intelligence (AI) has increasingly been serving the field of radiology over the last 50 years. As modern medicine is evolving towards precision medicine, offering personalized patient care and treatment, the requirement for robust imaging biomarkers has gradually increased. Radiomics, a specific method generating high-throughput extraction of a tremendous amount of quantitative imaging data using data-characterization algorithms, has shown great potential in individuating imaging biomarkers. Radiomic analysis can be implemented through the following two methods: hand-crafted radiomic features extraction or deep learning algorithm. Its application in lung diseases can be used in clinical decision support systems, regarding its ability to develop descriptive and predictive models in many respiratory pathologies. The aim of this article is to review the recent literature on the topic, and briefly summarize the interest of radiomics in chest Computed Tomography (CT) and its pertinence in the field of pulmonary diseases, from a clinician’s perspective.

The aim of our study was to determine the potential role of CT-based radiomics in predicting treatment response and survival in patients with advanced NSCLC treated with immune checkpoint inhibitors. We retrospectively included 188 patients with NSCLC treated with PD-1/PD-L1 inhibitors from two independent centers. Radiomics analysis was performed on pre-treatment contrast-enhanced CT. A delta-radiomics analysis was also conducted on a subset of 160 patients who underwent a follow-up contrast-enhanced CT after 2 to 4 treatment cycles. Linear and random forest (RF) models were tested to predict response at 6 months and overall survival. Models based on clinical parameters only and combined clinical and radiomics models were also tested and compared to the radiomics and delta-radiomics models. The RF delta-radiomics model showed the best performance for response prediction with an AUC of 0.8 (95% CI: 0.65−0.95) on the external test dataset. The Cox regression delta-radiomics model was the most accurate at predicting survival with a concordance index of 0.68 (95% CI: 0.56−0.80) (p = 0.02). The baseline CT radiomics signatures did not show any significant results for treatment response prediction or survival. In conclusion, our results demonstrated the ability of a CT-based delta-radiomics signature to identify early on patients with NSCLC who were more likely to benefit from immunotherapy.

Objective: Checkpoint inhibitor pneumonitis (CIP) is a potentially life-threatening immune-related adverse event. Efficient strategies to select patients at risk are still required. The aim of our study was to assess the utility of a machine learning model, integrating pre-treatment CT lung radiomics features with clinical data, to predict patients at risk of developing CIP. Methods: In this retrospective study, 116 patients with varied malignancies treated with immune checkpoint inhibitors (ICIs) were included. In this cohort, 35 patients presented with CIP and 81 patients did not. Each lung and its lobes were segmented on pre-treatment CT scans to perform a handcrafted radiomic analysis. Radiomic features were associated with clinical parameters to build generalized linear (GLM) and random forest (RF) models, to predict occurrence of CIP. The models were fine-tuned, validated and tested using a nested 5-fold cross-validation method. Results: The RF models combining radiomic and clinical features showed the best performances with an area under the ROC curve (AUC) of 0.75 (95%CI:0.62-0.88) on the test set. The most accurate clinical model was a RF model and achieved an AUC of 0.72 (95%CI:0.51-0.92). The best radiomic model was a GLM model and achieved an AUC of 0.71 (95%CI:0.58-0.84). Conclusions: Our CT-based lung radiomic models showed moderate to good performance at predicting CIP. We demonstrated the potential role of machine learning models associating clinical parameters and lung CT radiomic features to better identify patients treated with ICIs at risk of developing CIP. Advances in knowledge: Radiomics analysis of the lung parenchyma could be used as a non-invasive tool to select patients at risk of developing immune-checkpoint pneumonitis.