Radiogenomics study to predict the nuclear grade of renal clear cell carcinoma

Xuan-Ming He; Jian-Xin Zhao; Di-Liang He; Jia-Liang Ren; Lian-Ping Zhao; Gang Huang

doi:10.1016/j.ejro.2023.100476

Radiogenomics study to predict the nuclear grade of renal clear cell carcinoma

Eur J Radiol Open. 2023 Jan 28:10:100476. doi: 10.1016/j.ejro.2023.100476. eCollection 2023.

Authors

Xuan-Ming He¹, Jian-Xin Zhao¹, Di-Liang He¹, Jia-Liang Ren², Lian-Ping Zhao³, Gang Huang³

Affiliations

¹ The First Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou, China.
² GE Healthcare China, Beijing, China.
³ Department of Radiology, Gansu Provincial Hospital, Lanzhou, China.

Abstract

Purpose: To develop models based on radiomics and genomics for predicting the histopathologic nuclear grade with localized clear cell renal cell carcinoma (ccRCC) and to assess whether macro-radiomics models can predict the microscopic pathological changes.

Method: In this multi-institutional retrospective study, a computerized tomography (CT) radiomic model for nuclear grade prediction was developed. Utilizing a genomics analysis cohort, nuclear grade-associated gene modules were identified, and a gene model was constructed based on top 30 hub mRNA to predict the nuclear grade. Using a radiogenomic development cohort, biological pathways were enriched by hub genes and a radiogenomic map was created.

Results: The four-features-based SVM model predicted nuclear grade with an area under the curve (AUC) score of 0.94 in validation sets, while a five-gene-based model predicted nuclear grade with an AUC of 0.73 in the genomics analysis cohort. A total of five gene modules were identified to be associated with the nuclear grade. Radiomic features were only associated with 271 out of 603 genes in five gene modules and eight top 30 hub genes. Differences existed in the enrichment pathway between associated and un-associated with radiomic features, which were associated with two genes of five-gene signatures in the mRNA model.

Conclusion: The CT radiomics models exhibited higher predictive performance than mRNA models. The association between radiomic features and mRNA related to nuclear grade is not universal.

Keywords: Computer Applications; FDR, False discovery rate; GLRLM, Gray level run length matrix; GLSZM, Gray level size matrix; KEGG, KOBAS-Kyoto Encyclopedia of Genes and Genomes; Kidney; NGTDM, Neighborhood gray tone difference matrix; Neoplasms-Primary; PPI, Protein-Protein Interaction Networks; Pathological nuclear grade; Radiogenomics; Radiomics; TCGA, The cancer genome atlas; TCIA, The cancer imaging archive; WGCNA, Weighted gene co-expression network; WHO/ISUP, World Health Organization and International Society of Urological Pathology; ccRCC, Clear cell renal cell carcinoma.