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Additional Supporting Information may be found in the
online version of this article at the publisher’s web-site:
Table S1. Diagnostic ﬁndings in each pathology based on
subjective analysis and intensity ratio curve analysis.
Table S2. Interobserver variability of ﬁndings and diagnosis
between subjective analysis and intensity ratio curve analysis.
Table S3. Tumor types by intensity ratio curve analysis and
pathologies in each tumor type by subjective analysis.
Editorial Comment to Intensity ratio curve analysis of small renal masses
on T2-weighted magnetic resonance imaging: Differentiation of fat-poor
angiomyolipoma from renal cell carcinoma
Small renal masses (SRMs; ≤4 cm) are commonly detected
incidentally on abdominal ultrasound or computed tomography
(CT) due to the lack of clinical symptoms. Contrast-enhanced
CT is the preferred imaging modality to characterize SRMs (for
example, tumor size, baseline attenuation, contrast effect, and
anatomical relationships between the tumor and adjacent struc-
However, the visual characteristics of CT imaging are
insufﬁciently speciﬁc or overlap among various SRMs; for
instance, between fat-poor angiomyolipoma and renal cell car-
cinoma subtypes. Recently, quantitative methods have been
developed to detect subtle variations beyond visual assessment
on CT images.
Magnetic resonance imaging (MRI) can be applied if iodi-
nated contrast is contraindicated, or to better characterize
complex cystic masses. Numerous radiologists or urologists
have studied the potential of MRI in the histological subtyp-
ing of renal cell carcinoma and in the differentiation of
benign from malignant renal masses. Kay et al. reported the
diagnostic performance and inter-reader agreement of a stan-
dardized diagnostic algorithm used to determine the histologi-
cal type of SRM at multiparametric MRI,
as the Prostate
Imaging and Reporting Data System,
which is a validated
diagnostic system developed for assessment of multiparamet-
ric prostate MRI. Despite important data supporting the utility
of MRI features in the characterization of SRMs, there is as
yet no easily applicable, standardized and robust diagnostic
system for multiparametric MRI in clinical practice.
In this study, Moriyama S et al. showed that the novel
semiquantitative model for the combined assessment of key
features in fat-poor angiomyolipoma, including low intensity,
homogeneity and absence of pseudo-capsule on T2-weighted
image, might make the diagnosis of fat-poor angiomyolipoma
As described by the authors, the important
limitation of this study was the lack of any expert radiologists
as image readers. Nevertheless, these results could contribute
to the creation of a standardized diagnostic algorithm using
multiparametric MRI. Further reﬁnements in the algorithm
and standardization of the CT or MRI protocol are necessary
to improve the diagnostic performance in the characterization
of SRMs in order to avoid invasive tumor biopsy and unnec-
Department of Uro-Oncology, Saitama Medical University
International Medical Center, Hidaka, Saitama, Japan
Conﬂict of interest
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© 2018 The Japanese Urological Association
S MORIYAMA ET AL.