Synopsis
Hobnail
papillary thyroid carcinoma (HPTC) is a moderately differentiated PTC variant
with aggressive clinical behavior and significant mortality.The
purpose of this study was to recognize the unique MRI features of HPTC .61 patients with PTC
confirmed histopathologically[19 lesions of HPTC and 42 NHPTC (PTC without hobnail
features)lesions] undergoing
MRI with T1W, T2W,DWI and contrast material–enhanced sequences prior to thyroidectomy
were included retrospectively. there was a significant
tendency toward T2WI significantly high signal 、T2WI linear low signal 、Lace levy 、Gyrus-like structure 、higher ADC values 、irregular shape present in
HPTCs. HPTC lesions have unique
MRI features.
Purpose
Hobnail
papillary thyroid carcinoma (HPTC) is a recently described rare variant that
appears to behave more aggressively[1]. PTC with a prominent hobnail pattern is a moderately
differentiated PTC variant with aggressive clinical behavior and significant
mortality regardless of location by a higher rate of initial presentation at an
advanced tumor stage and poor outcome[2-4]. Although the number of reported cases of PTC
with a prominent hobnail pattern is small, it is important to note that the
long-term survival rate of the variant is much lower than that of classic PTC[4]. And micropapillary carcinoma is a histologic
pattern, rather than an independent entity[2]. So it is more important to comment on
clinically relevant morphological characteristics of the tumor, rather than
religiously trying to classify an individual tumor into a particular variant[5]. Identification of the hobnail pattern can be
used not only for the diagnosis of PTC, but also for predicting aggressive PTC[6]. The purpose of this study was to recognize the
unique MRI features of papillary thyroid carcinoma with hobnail pattern(HPTC) to
help to reduce the rate of missed diagnosis and misdiagnosis of this rare
variant before surgery.
Method
61 patients with PTC confirmed histopathologically[19 lesions of HPTC
and 42 NHPTC (PTC without
hobnail features)lesions]
undergoing MRI with T1-weighted, T2-weighted, diffusion-weighted magnetic
resonance imaging (DWI) and contrast material–enhanced sequences prior to
thyroidectomy were included retrospectively. Images were evaluated by two
observers for age,sex,tumor size, Apparent diffusion coefficients (ADC) value
, ADC value
classification and MRI features (lesion shape,T1WI
、T2WI、DWI signal uniformity, significantly high signal、linear low signal、lace levy、gyrus-like
structure on T2WI,
early enhancement degree, delayed enhancement pattern) on images
obtained with each sequence. Descriptive statistics and t-test were used to determine
the difference of age、tumor
size and ADC value of HPTC and NHPTC. Chi-square test were used to determine
the difference of sex 、ADC value classification and each MRI
features. The sensitivity、
specificity、 positive predictive
value、 negative predictive
value
and accuracy of some significant features were determined. A correlation analysis was used to
analyze the correlation between the T2WI significantly high signal ratio and
Hobnail pattern components ratio. A binary logistic regression
model was developed to identify features that were independently predictive of HPTC. A
statistical analysis was performed using P values less than 0.05 considered
statistically significant.Result
Comparing HPTC to NHPTC: The age
of patient of HPTC was
significantly
younger (37.632 ,P=0.026) ;The lesion size
of HPTC was larger (1.747, P=0.013); Irregular
was more (65.7% ,P=0.043); the ADC value of HPTC was
significantly
higher (1.559,ADC
value≥1.4×10-3
mm2/s at a percentage of 84.2%, P=0.000);The frequency of T2WI significantly high signal ,linear low signal, lace levy, gyrus-like
structure of HPTC was significantly higher (100%,100%,68.4%,52.6%, P=0.000 all); T2WI signal of HPTCs showed
mixed signals Most (94.7%,
P=0.005); Slightly hyperenhanced was more on early
enhancement degree(57.9%, P=0.024).
No significant differences were found between HPTC and NHPTC with respect
to sex (P=0.095), T1WI signal
uniformity (P=0.094), DWI signal
uniformity(P=0.153), Delayed
enhancement pattern(P=0.543). The following
indicators was all more than 90%: sensitivity of T2WI significantly high signal、T2WI linear low signal、 T2WI Mixed signal; specificity of T2WI
significantly high signal、 Lace levy 、Gyrus-like structure 、ADC classification(≥1.4); positive predictive value of
Gyrus-like structure; negative predictive value of T2WI significantly high
signal 、T2WI linear low signal、 T2WI Mixed signal 、ADC classification(≥1.4); accuracy of T2WI significantly high
signal 、ADC
classification(≥1.4). By binary
logistic regression analysis, Lace levy and ADC value
classification were two very strong independent indicators of HPTC (odds ratio : 23.111、116.276; 95% confidence interval: 5.012-106.570, 10.026-1348.547; P value:0.007、0.000; respectively).There was significant correlation between T2WI significantly high signal ratio and Hobnail pattern components
ratio,r2=0.5627,P=0.0002.Discussion/Conclusion
HPTCs are not completely solid tumors,
including liquid components.They showed loosely or individually arranged
cancer cells without classic papillary or follicular structures at the
periphery of tumor with loss of cellular cohesiveness of the cancer cells, which lead to a gap between their cell mass,
and the abundant extracellular fluid filling caused more water. These pathologic features can cause T2WI
significantly high signal 、higher ADC values. In our study, there
was a significant tendency toward T2WI significantly high signal 、T2WI linear low signal 、Lace levy 、Gyrus-like structure 、higher ADC values 、irregular shape present in HPTCs. The
Multivariate Statistical Results suggest that Lace levy and high ADC value may
be imaging marker that can guide critical diagnostic recommendations for
patients with HPTCs. So
HPTC
lesions have unique MRI features from NHPTC.
Acknowledgements
I am very grateful to Bin Song, and he has made a lot of comments on the innovative aspects
of the article and has given much support in image analysis and statistical
data. Thanks to Hao Wang and Zedong Dai in data collection and image
analysis support.
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