Radiomics predicts the prognosis of patients with clear cell renal cell carcinoma by reflecting the tumor heterogeneity and microenvironment

• Published in: Cancer imaging 2024-09, Vol.24 (1), p.124-15
• Main Authors: Wu, Ji, Li, Jian, Huang, Bo, Dong, Sunbin, Wu, Luyang, Shen, Xiping, Zheng, Zhigang
• Format: Article
• Language: English
• Subjects: Aged; Carcinoma, Renal cell; Carcinoma, Renal Cell - diagnostic imaging; Carcinoma, Renal Cell - genetics; Carcinoma, Renal Cell - mortality; Carcinoma, Renal Cell - pathology; Clear cell renal cell carcinoma; CT imaging; Deep Learning; Development and progression; Female; Humans; Kidney Neoplasms - diagnostic imaging; Kidney Neoplasms - genetics; Kidney Neoplasms - mortality; Kidney Neoplasms - pathology; Male; Medical research; Medicine, Experimental; Middle Aged; Overall survival; Prognosis; Radiomics; Retrospective Studies; Tomography, X-Ray Computed - methods; Tumor Microenvironment
• ISSN: 1470-7330; 1740-5025; 1470-7330
• DOI: 10.1186/s40644-024-00768-7

We aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment. The clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What's more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results. At multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868-0.931), 0.854(0.819-0.899) and 0.831(0.813-0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values