MRI Functional Imaging Characteristics and Fat Quantification of CT-fat-free Renal Neoplasms: Relationships With Histological Classifications and Molecular Markers

Overview

The knowledge of the histological diagnosis and its subtype of a renal parenchymal tumor is important for determine whether the choice of a specific regimen of chemotherapy, target therapy and immunotherapy could be suitable and effective for treating this tumor. Computed tomography (CT) has been considered as an excellent imaging modality for detecting intra-tumoral fat, and most of renal angiomyolipomas (AML) could be thus confidently diagnosed on computed tomography by showing intra-tumoral fat. However, if a renal parenchymal tumor has no detectable fat in the tumor on computed tomography, there is a long list of its diagnosis including benign neoplasms as angiomyolipoma with minimal fat, oncocytoma, metanephric adenoma, etc., epitheloid angiomyolipoma (eAML) malignant potential, malignant neoplasms as renal cell carcinoma (RCC), sarcoma, malignant eAML, etc. Furthermore, there are three kinds of anticancer drug (antiangiogenetic drug, mammalian target of rapamycin inhibitors, immune modulators, and whether the anticancer drug is effective mainly depending on subtypes of RCCs. Nonetheless, computed tomography could not reliably differentiate histological types of renal parenchymal masses except renal AMLs with abundant fat. Therefore, for patients without established diagnoses by imaging examinations, further biopsy of the renal tumor is usually mandatory to validate the histological diagnosis and subtype. Thus, this study plans to enroll 60 patients with renal parenchymal masses which show no intra-tumoral fat on computed tomography. All enrolled patients will undergo multiparametric and fat-detection magnetic resonance imaging (MRI).

Multiparametric MRI includes not only conventional T1-weighted and T2-weighted images but also diffusion weighted images and dynamic contrast enhanced images. Fat-detection MRI include fat-suppressed images by frequency selection, out-of-phase images (compared with in-phase image) and fat quantification pulse sequence for determining the presence or absence of intra-tumoral fat as well as measuring fat amount. We will record MRI characteristics of the renal parenchymal tumors of the patients. If the patients undergo further biopsy for establishing histological diagnoses and subtypes after MRI examination, the specimens of the renal tumor obtained from biopsy will be further analyzed by immunohistochemistry stain (IHC), quantitative real-time polymerase chain reaction (qPCR) and western blot after obtainment of the patient's informed consent. The MRI characteristics of the renal parenchymal tumors of the patients will be then correlated with histological examination, IHC, qPCR and western blot examinations of the tumors. We will then use univariate and multivariate analyses to determine whether MRI characteristics are useful as imaging surrogates for predicting biomarkers as histological diagnosis, subtypes, IHC, qPCR and western blot results.