4 DISCUSSION
CCSK, with unclear histological and immunohistological features, is an
unusual pediatric renal malignancy secondary to Wilms’
tumor.4 The main clinical manifestations of CCSK
include abdominal mass, abdominal pain, hematuria and other atypical
symptoms similar to Wilms’ tumor, and the lack of specificity of these
symptoms can result in misdiagnosis of CCSK as Wilms’ tumor
preoperatively.3 Thus,
the accurate preoperative
differentiation between CCSK and Wilms’ tumor is helpful for clinical
decision making in children. Kang et al found perinephric vessel
engorgement and higher tumor enhancement on CT images are useful in
differentiating CCSK from Wilms’ tumor.7 However,
these qualitative and semi-quantitative features cannot reflect
intra-tumoral heterogeneity comprehensively. Recently, radiomics
provides a promising method to evaluate tumor phenotypes
quantitatively.12,13 In this study, we used CT-based
radiomics analysis to differentiate CCSK from Wilms’ tumor for the first
time. Some differences were confirmed in radiomics features between CCSK
and Wilms’ tumor.
According to our results, among the final optimal features screened
through the variance threshold, SelectKBest and LASSO methods, all of
them were first-order features depicting the distribution of voxel
intensities, of which skewness from NP images achieved moderate to good
diagnostic performance for CCSK. A skewness is about the asymmetry of
the distribution of voxel intensities,14 indicating
that there are differences in the asymmetry of the voxel intensity
histogram between CCSK and Wilms’ tumor. Additionally, the performance
of skewness transformed by exponential and squareroot filters is
obviously superior to that of original skewness, suggesting advanced
features filtered by filters could reveal more invisible meaningful
information about tumoral heterogeneity.15 In previous
literatures, first-order histogram characteristics of renal tumors
differed in various pathological types.10,16,17 Deng
et al investigated the role of CT texture analysis in differentiating
major renal cell carcinoma subtypes, and the first-order entropy was
found to be the most meaningful biomarker in differentiating clear cell
from papillary renal neoplasms.17 Likewise, skewness
and kurtosis were demonstrated to be helpful for differentiating clear
cell renal carcinoma from oncocytoma.16 However, in
other studies on texture analysis of non-renal tumors, second-order
features, such as gray level size zone matrix or gray level difference
matrix, seem to play a more important role in characterizing
heterogeneity of non-renal tumors.18-20
When training with LR model, all the selected features from each phase
were used to construct classification model for diagnosing CCSK. The
results showed LR models combining all chosen features perform better
than the majority of single feature. Because it is hard to delineate the
boundary of tumor lesion from kidney on non-contrast-enhanced CT images,
we only chose CMP and NP images to perform radiomics analysis. Compared
to CMP images, NP images provided more useful data to the
CCSK-associated radiomics characteristics. When combining CMP and NP
images, an interesting finding was that all the optimal features are
extracted from the NP images. Meanwhile, the performance of the
composite model was similar to that of NP model, suggesting two-phase CT
images have no additional value in differentiating between CCSK and
Wilms’ tumor. Meng et al demonstrated NP features are the most sensitive
features for characterizing sarcomatoid from clear cell renal carcinoma,
the reason for which may be that sarcomatoid differentiation causes
changes in intra-tumoral enhancement patterns.11 Boo
et al found there are some unique vascular patterns in CCSK, in which
regularly-spaced fibrovascular septa separates the nests of tumor cells,
and this may cause late enhancement in CCSK compared with Wilms’
tumor.21
Despite LR model based on NP images had moderate to good performance in
diagnosing CCSK, the results of Delong test showed no significant
difference between LR model, exponential-skewness and
squareroot-skewness based on NP in training and validation set, which
further confirms the important role of skewness in differentiating CCSK
from Wilms’ tumor. The distribution of exponential-skewness and
squareroot-skewness in CCSK was different from Wilms’ tumor. And higher
skewness was statistically associated with CCSK, and lower skewness with
Wilms’ tumor. Although NP-based skewness performed moderate to good in
our study, this feature may be a supplementary biomarker contributing to
the differential diagnosis between CCSK and Wilms’ tumor. However, the
exact utility of skewness in the CCSK and Wilms’ tumor differentiation
still needs further investigation.
Admittedly, there were some limitations in the present study. First, due
to the rarity of CCSK, as a tertiary referral children’s medical center,
only 29 pediatric patients with CCSK were enrolled in this study.
Second, considering the predominant prevalence of Wilms’ tumor, 51
patients with Wilms’ tumor were selected consecutively as control group,
which may cause selection bias to our results. Third, the slice
thickness of CT images in our study was 5 mm in order to include more
patients as possible, and thin slice thickness may help to reflect more
meaningful radiomics features between CCSK and Wilms’ tumor. Finally,
the CT scans performed in our study were obtained on two different
scanners. Although preprocessing of the images was performed, the
radiomics features derived from different scanners may have some
influence on the diagnostic performance for CCSK.
In conclusion, radiomics is a promising method to differentiate CCSK
from Wilms’ tumor in children. Skewness from NP images at exponential
and squareroot filters was able to discriminate between CCSK and Wilms’
tumor, obtaining moderate to good diagnostic performance for CCSK. And
higher skewness on NP images may be a potential biomarker for diagnosing
CCSK from Wilms’ tumor.