Radiomics of Tumor Heterogeneity in 18 F-FDG-PET-CT for Predicting Response to Immune Checkpoint Inhibition in Therapy-Naïve Patients with Advanced Non-Small-Cell Lung Cancer

• Published in: Cancers 2023-04, Vol.15 (8)
• Main Authors: Ventura, David, Schindler, Philipp, Masthoff, Max, Görlich, Dennis, Dittmann, Matthias, Heindel, Walter, Schäfers, Michael, Lenz, Georg, Wardelmann, Eva, Mohr, Michael, Kies, Peter, Bleckmann, Annalen, Roll, Wolfgang, Evers, Georg
• Format: Article
• Language: English
• ISSN: 2072-6694; 2072-6694

We aimed to evaluate the predictive and prognostic value of baseline F-FDG-PET-CT (PET-CT) radiomic features (RFs) for immune checkpoint-inhibitor (CKI)-based first-line therapy in advanced non-small-cell lung cancer (NSCLC) patients. In this retrospective study 44 patients were included. Patients were treated with either CKI-monotherapy or combined CKI-based immunotherapy-chemotherapy as first-line treatment. Treatment response was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST). After a median follow-up of 6.4 months patients were stratified into "responder" ( = 33) and "non-responder" ( = 11). RFs were extracted from baseline PET and CT data after segmenting PET-positive tumor volume of all lesions. A Radiomics-based model was developed based on a Radiomics signature consisting of reliable RFs that allow classification of response and overall progression using multivariate logistic regression. These RF were additionally tested for their prognostic value in all patients by applying a model-derived threshold. Two independent PET-based RFs differentiated well between responders and non-responders. For predicting response, the area under the curve (AUC) was 0.69 for " " and 0.75 predicting overall progression for " ". In terms of progression-free survival analysis, patients with a lower value of (threshold < 0.2014; hazard ratio (HR) 0.17, 95% CI 0.06-0.46; < 0.001) and higher value of (threshold > 0.5233; HR 0.23, 95% CI 0.11-0.49; < 0.001) had a significantly lower probability of disease progression or death. Our Radiomics-based model might be able to predict response in advanced NSCLC patients treated with CKI-based first-line therapy.