The previous study showed that contrast-enhanced ultrasound (CEUS) allowed easy identification of collateral vessels of cervical body tumor mass [11]. Similar to CEUS, SMI allows radiologists to visualize the microvascular patterns of lesions in detail without the additional use of a contrast agent [12]. Previous studies also described that SMI provided a better depiction of vessel branching details than did color Doppler flow imaging in thyroid nodules [13]. In our study, we found that SMI is superior to CDFI in assessing the blood flow of CBT. By SMI, the pattern of two transformed from Adler 0 to Adler I, the pattern of six transformed from Adler I to Adler II or III, and the pattern of ten masses transformed from Adler II to Adler III. It may be due to some feeding vessels was too small to be observed by CDFI. CDFI is unable to detect low-velocity blood flow, since the presence of extraneous Doppler signals due to nearby structures. SMI is able to show lower-velocity blood flow, since SMI can analyze clutter motion, and uses a new adaptive algorithm to identify and remove tissue motion and reveal true blood flow. Since there are almost no angle dependence, clutter, and overflow at lower scales, SMI shows much more complete and genuine vascular branches.
We showed that SMI was reported to be superior to CDFI in detecting the feeding artery of CBTs. Two lesions (7.7%) that stemmed from ICA was not detected with CDFI, and three lesions (11.5%) that stemmed from both ICA and ECA was not detected accurately with CDFI. CDFI imaging relied on long and transverse section imaging and showed the feeding artery in a single plane, so CDFI couldn’t fully obtain the vascular spatially heterogeneity. To identify the feeding artery has important significance for both pre-operative diagnosis and successful resection. With the preoperative embolization of the carotid artery, a bloodless operative field could be achieved to reduce the operative morbidity [14, 15]. For the tumor close to the carotid artery, forcibly separating the tumor may cause the rupture of the blood vessel and cause massive bleeding. For the CBTs which invaded the internal and external carotid arteries, it is often difficult to separate from the external carotid artery, and the distal end of the external carotid artery can be clamped before the tumor is peeled off.
We classified the feeding artery of CBTs into originating from ICA and others (including ECA and MIX) and found that vascularity was different in the feeding artery of CBTs from ICA or others. All CBTs originating from ICA are with the pattern of Adler I/II, however, only 30.4% of CBTs originating from ECA and MIX are with the pattern of Adler II, most of CBTs (69.6%) originating from ECA are with the pattern of Adler III. The results showed that CBTs mainly originating from ICA has less vascularity compared with those from ECA.
There are several limitations to our study. First, vascularity patterns were not analyzed according to malignant or benign lesions because all the lesions were benign CBTs. Lacking malignant CBTs and neurogenic tumors, such as schwannoma, may have been a hindrance. Second, our study included 26 lesions and future studies that involve more subjects may produce more accurate results. Third, it may have been affected by selection bias as only patients who underwent both ultrasound and surgery were enrolled in the study.
In conclusion, we showed that SMI could be used to better investigate vascularity of the CBTs comparing to CDFI, and SMI is superior to detecting the origin of feeding vessels of the lesion in comparison to CDFI. In addition, CBTs mainly originating from ICA has less vascularity compared with those from ECA or MIX. We expect that with the application of SMI, this approach could become an invaluable tool for CBTs diagnostic and pre-operative workup.