Multiomics approach for patient stratification and novel target identification in metastatic clear cell renal carcinoma (MeetUro 31): Preliminary analysis of radiomics features—A Meet-URO and AIRC study (NCT05782400).

Abstract

425 Background: Artificial Intelligence can integrate clinic-pathological features, radiomics, genomic and transcriptomic analysis to define an optimal allocation strategy in first line treatment of metastatic renal cell carcinoma (mRCC). Methods: This is a multicenter Italian prospective translational study including patients (pts) with clear cell mRCC receiving first-line treatment as per investigator’s choice. Tumor tissue was collected at baseline, plasma samples and CT scan were collected at baseline and every 3 months until progression. Due to the short follow up, here we report the preliminary analysis of the radiomic features to identify signatures associated with Objective Response Rate (ORR). A subset of non-analytically correlated radiomic features was extracted from the selected regions of interest. This subset included first-order statistics, three-dimensional shape descriptors, and texture-based features. All features were computed on the original images using PyRadiomics v.3.1.0. The radiomic analysis pipeline consisted of feature variance filtering, multicollinearity reduction, data harmonization and standardization, and feature importance estimation through a Random Forest-based algorithm. Results: 100 pts were enrolled. For the radiomic analysis, 68 patients were included to ensure a more reliable data harmonization process and to improve the robustness of subsequent analyses. 18 (26%) received IO-IO, 38 (56%) received IO-TKI, 12 (18) received TKI monotherapy as first line treatment. According to IMDC score, 16(24%) were good risk, 39(57%) intermediate and 13(19%) poor. The most common site of metastasis were lung (55%, 38), bone (23%,16), nodes (20%, 14/68) and liver (13%, 9). In the overall population, ORR was 48% (33), 44% (18) in the IO-TKI group, 44% (8) in the IO-IO group and 42% (5) in the TKI group. The two most influential features identified by the Random Forest model were original_firstorder_Mean and original_glcm_Contrast (0.59 accuracy, 0.58 precision, 0.58 recall, 0.58 F1 score, 0.49 AUROC). Higher values of these features—reflecting increased tissue density and heterogeneity—were associated with a higher ORR. Conclusions: This preliminary analysis suggests that 2 radiomic signatures are associated with higher ORR and are promising as early biomarkers of response in mRCC. However, they do not appear to provide optimal predictive value when used alone, and should therefore be integrated with clinical, genomic, and transcriptomic data to refine predictive modeling. Acknowledgments: We thank AIRC (Associazione Italiana Ricerca sul Cancro) for the support received to conduct this trial. Clinical trial information: NCT05782400 .