Combination of computed tomography imaging-based radiomics and clinicopathological characteristics for predicting the clinical benefits of immune checkpoint inhibitors in lung cancer

Abstract

Background In this study, we tested whether a combination of radiomic features extracted from baseline pre-immunotherapy computed tomography (CT) images and clinicopathological characteristics could be used as novel noninvasive biomarkers for predicting the clinical benefits of non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs). Methods The data from 92 consecutive patients with lung cancer who had been treated with ICIs were retrospectively analyzed. In total, 88 radiomic features were selected from the pretreatment CT images for the construction of a random forest model. Radiomics model 1 was constructed based on the Rad-score. Using multivariate logistic regression analysis, the Rad-score and significant predictors were integrated into a single predictive model (radiomics nomogram model 1) to predict the durable clinical benefit (DCB) of ICIs. Radiomics model 2 was developed based on the same Rad-score as radiomics model 1.Using multivariate Cox proportional hazards regression analysis, the Rad-score, and independent risk factors, radiomics nomogram model 2 was constructed to predict the progression-free survival (PFS). Results The models successfully predicted the patients who would benefit from ICIs. For radiomics model 1, the area under the receiver operating characteristic curve values for the training and validation cohorts were 0.848 and 0.795, respectively, whereas for radiomics nomogram model 1, the values were 0.902 and 0.877, respectively. For the PFS prediction, the Harrell’s concordance indexes for the training and validation cohorts were 0.717 and 0.760, respectively, using radiomics model 2, whereas they were 0.749 and 0.791, respectively, using radiomics nomogram model 2. Conclusions CT-based radiomic features and clinicopathological factors can be used prior to the initiation of immunotherapy for identifying NSCLC patients who are the most likely to benefit from the therapy. This could guide the individualized treatment strategy for advanced NSCLC.

The Role of Radiomics in the Era of Immune Checkpoint Inhibitors: A New Protagonist in the Jungle of Response Criteria

“…Although radiomics models have demonstrated a predictive and prognostic value in several cancers, the performance of these models alone is still not enough. In order to improve the prediction of clinical benefits of ICI, the combination of radiomic features with clinicopathological variables has been proposed recently by Yang and colleagues [ 47 ]. In a cohort of 92 NSCLC patients, the authors developed two nomogram models, combining radiomic features from baseline CT and clinicopathological variables (i.e., higher Rad-score, younger age, N stage and M stage), identifying with good accuracy (AUC 0.902 in the training cohort) patients with durable response and longer PFS, although without an external validation.…”

CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy

“…Accurate prediction of treatment response is of great significance for the stratification and selection of patients benefiting from immunotherapy. Yang et al (36) selected 88 radiomics features from the CT images of 92 patients with lung cancer before immunotherapy and constructed a random forest model. Combined with clinicopathological information, they successfully predicted the patients who would benefit from ICI treatment (the AUCs of the training and validation groups were 0.848 and 0.795, respectively).…”

Clinical‑radiomic model in advanced kidney cancer predicts response to tyrosine kinase inhibitors

“…The concept of applying radiomics to predict response is not new (26)(27)(28)(29). Zhi Ji et al predicted response to immunotherapy utilizing radiomics in 87 patients with gastrointestinal malignant tumors obtaining a model with an AUC of 0.80 with a sensitivity and specificity of 83.3 and 88.9% respectively.…”

“…Yang B et al predicted response to immunotherapy in 92 patients with lung cancer. The model combined 15 radiomic features and clinicopathologic data obtaining an AUC of 0.90, a sensitivity of 85.7%, and a specificity of 88.4% ( 27 ). We strongly believe that adding clinical data to the model is paramount to obtaining a robust model.…”