LAUSR

AIM To identify combined positron-emission tomography (PET)/magnetic resonance imaging (MRI)-based radiomics as a surrogate biomarker of intratumour disease risk for molecular subtype ccA and ccB in patients with primary clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS PET/MRI data were analysed retrospectively from eight patients. One hundred and sixty-eight radiomics features for each tumour sampling based on the regionally sampled tumours with 23 specimens were extracted. Sparse partial least squares discriminant analysis (SPLS-DA) was applied to feature screening on high-throughput radiomics features and project the selected features to low-dimensional intrinsic latent components as radiomics signatures. In addition, multilevel omics datasets were leveraged to explore the complementing information and elevate the discriminative ability. RESULTS The correct classification rate (CCR) for molecular subtype classification by SPLS-DA using only radiomics features was 86.96% with permutation test p=7×10-4. When multi-omics datasets including mRNA, microvascular density, and clinical parameters from each specimen were combined with radiomics features to refine the model of SPLS-DA, the best CCR was 95.65% with permutation test, p<10-4; however, even in the case of generating the classification based on transcription features, which is the reference standard, there is roughly 10% classification ambiguity. Thus, this classification level (86.96-95.65%) of the proposed method represents the discriminating level that is consistent with reality. CONCLUSION Featured with high accuracy, an integrated multi-omics model of PET/MRI-based radiomics could be the first non-invasive investigation for disease risk stratification and guidance of treatment in patients with primary ccRCC.