## 3 Discussion
### Radomics and Artificial Intelligence  
Radiomics allows the development of predictive signatures in medical imaging [30]. In particular, radiomics can produce quantitative information from radiological images that could predict tumor biology and immune cell infiltrates. Radiomics has several advantages compared to biopsy: it obtains spatial information of the whole tumor and microenvironment, thereby considering intratumor heterogeneity and interactions with the microenvironment; multiple lesions can be evaluated in parallel; lesions can be evaluated longitudinally, allowing the study of their variations over time; and the information is obtained through a non-invasive approach [31]. Artificial intelligence, including machine learning and deep learning, is a new breakthrough technology that has been proposed as a solution to enable automatic, rapid, and precise analysis of tissues [32; 33]. [PERSON] et al. [31], using signature texture analysis, predicted tumor response in mRCC patients treated with nivolumab [31]. Twenty-seven patients were included in the study; 89% had received prior nephrectomy and 89% had received prior systemic therapy. Furthermore, 30% of the patients were in the good risk group, 60% in the intermediate risk group, and 10% in the poor risk group, according to the IMDC scoring system. Machine learning was applied with the support-vector machine and radial basis function. To predict the pretreatment approach, a classifier was trained to obtain characteristics of the post-treatment plot to distinguish between responders (CR/PR/durable SD) and non-responders. The analysis was performed by contouring 104 lesions: 62 lymph nodes, 24 lung metastasis, and 18 renal or adrenal metastases. Nineteen patients were responders, and eight patients were non-responders. Lesions contoured for analysis included lymph node metastases (60%), lung metastases (23%), and renal/adrenal metastases (17%). The median progression-free survival and overall survival for responders were 14 months and 28.8 months, respectively, while for non-responders they were 3.7 months and 16.3 months, respectively (median progression-free survival: \(p<0.0001\); overall survival: \(p=0.13\)). Texture analysis demonstrated poor ability to predict nivolumab responders from non-responders, with 69% overall accuracy for baseline CT scans (AUC = 0.46, \(p=0.30\)) and with 66% overall accuracy on the first post-treatment CT scans (AUC = 0.51, \(p=0.40\)) [31]. [PERSON] et al. [34], through CT texture analysis, evaluated the prediction of progression-free survival and overall survival in mRCC patients treated with nivolumab [34]. Forty-eight patients were included in the study, 45% of whom were in the intermediate IMDC group. At initial diagnosis, 55% of patients had metastases and 79% of patients had undergone nephrectomy prior to receiving nivolumab treatment. Metastases were predominantly located in the lymph nodes, lungs, bones, liver, and adrenal glands (58%, 42%, 37%, 31%, and 25%, respectively). The progression-free survival was 5.7 months, while overall survival was 13.8 months. Nine patients (18.7%) showed PR, twenty patients (41.7%) showed SD, and nineteen patients (39.6%) showed PD. The Lasso-penalized Cox regression analysis revealed three texture parameters as potential predictors of progression-free survival (skewness, S.2.2. Correlat and vS.1.1. Sum Varnc). Skewness was confirmed as an independent predictor of progression-free survival by multivariate Cox regression analysis (HR (95% CI) 1.49 [1.21-1.85], \(p<0.001\)). The Lasso penalized Cox regression analysis also detected three texture parameters as potential predictors of overall survival (S20 SumVarnc, S22 Contrast, and S22 Entropy). S22 Entropy was confirmed as an independent predictor of overall survival by multivariate Cox regression analysis (HR (95% CI) 1.68 (1.31-2.14), \(p<0.001\)). This study showed promising results in predicting oncological outcomes by radiomic analysis in mRCC patients treated with nivolumab [34]. [PERSON] et al.