Resumen de Design and implementation of predictive models based on radiomics to assess response to immunotherapy in non-small-cell lung cancer

M. Corral Bolaños, B. Farina, A.D. Ramos Guerra, Carmelo Palacios Miras, G. Gallardo Madueño, Arrate Muñoz Barrutia, G. R. Peces Barba, Luis Miguel Seijo Maceiras, J. Corral, I. Gil Bazo, M. Dómine Gómez, M. J. Ledesma Carbayo

• Lung cancer is the leading cause of cancer-related deaths in Europe. Immunotherapy treatments have been proved as the new standard of care for stage III-IV non-small cell lung cancer patients. However, the treatments vary in success, and there is not a reliable biomarker. This retrospective project aimed to develop a predictive model based on radiomics through machine learning or deep learning techniques to assess the response to the treatment, understood as the progression (or not) of the disease. Then, the study was complemented with an analysis of the progression-free survival time and an attempt of association with biological data. We used the basal computed tomography images of the primary tumour lesions from a cohort with 84 patients with IV stage nonsmall- cell lung cancer. The best performance model reached an AUC of 0.80 – 90 % CI [0.62, 0.99]. Our results suggest that the radiomics models may be useful for patient classification