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W. Rathmell
Last active: 11/27/2019

Integrative radiomics expression predicts molecular subtypes of primary clear cell renal cell carcinoma.

Yin Q, Hung SC, Rathmell WK, Shen L, Wang L, Lin W, Fielding JR, Khandani AH, Woods ME, Milowsky MI, Brooks SA, Wallen EM, Shen D
Clin Radiol. 2018 73 (9): 782-791

PMID: 29801658 · PMCID: PMC6078810 · DOI:10.1016/j.crad.2018.04.009

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. 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; 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.

Published by Elsevier Ltd.

MeSH Terms (12)

Biomarkers, Tumor Carcinoma, Renal Cell Contrast Media Humans Imaging, Three-Dimensional Kidney Neoplasms Magnetic Resonance Imaging Multimodal Imaging Neoplasm Grading Neoplasm Staging Positron-Emission Tomography Retrospective Studies

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