Multimodal ultrasound imaging: A method to improve the accuracy of diagnosing thyroid TI‐RADS 4 nodules

Abstract

Thyroid nodule is a common and frequently occurring disease in the neck in recent years, and ultrasound has become the preferred imaging diagnosis method for thyroid nodule due to its advantages of noninvasive, nonradiation, real‐time, and repeatable. The thyroid imaging, reporting and data system (TI‐RADS) classification standard scores suspicious nodules that are difficult to determine benign and malignant as grade 4, and further pathological puncture is recommended clinically, which may lead to a large number of unnecessary biopsies and operations. Including conventional ultrasound, ACR TI‐RADS, shear wave elastography, super microvascular imaging, contrast enhanced ultrasound, “firefly,” artificial intelligence, and multimodal ultrasound imaging used in combination. In order to identify the most clinically significant malignant tumors when reducing invasive operations. This article reviews the application and research progress of multimodal ultrasound imaging in the diagnosis of TI‐RADS 4 thyroid nodules.

Ultrasound imaging in thyroid nodule diagnosis, therapy, and follow‐up: Current status and future trends

“…Jin et al studied, retrospectively, the use of a deep learning method combined with a TIRADS protocol and found an improved AUC (0.902 vs. 0.845) and comparable diagnosis sensitivity and specificity for thyroid cancer as compared with a group of experienced radiologists. 59 Multimodal imaging can also be used for a specific TIRADS class, such as the TIRADS 4 class which is difficult to diagnose, as is suggested by Han et al 60 In conclusion, making use of advanced US analyses, such as 3D US and elastography, can improve the RSS's specificity and sensitivity, and subsequently may reduce the number of benign biopsies. Furthermore, the use of CAD methods in combination with TIRADS protocols seems promising and warrants further investigation.…”

“…Jin et al studied, retrospectively, the use of a deep learning method combined with a TIRADS protocol and found an improved AUC (0.902 vs. 0.845) and comparable diagnosis sensitivity and specificity for thyroid cancer as compared with a group of experienced radiologists 59 . Multimodal imaging can also be used for a specific TIRADS class, such as the TIRADS 4 class which is difficult to diagnose, as is suggested by Han et al 60 …”

“…Nevertheless, comparisons can be made, albeit with reservations and a call for more open‐access and larger datasets. Despite these challenges, the current networks offer mostly a comparable or improved sensitivity, specificity and accuracy as compared to senior radiologists 59,60,105–108 . One study showed the results of a ReS‐NET with a large dataset, 13 984 nodules, and found a comparable risk stratification when compared to senior clinicians 109 …”

“…Despite these challenges, the current networks offer mostly a comparable or improved sensitivity, specificity and accuracy as compared to senior radiologists. 59,60,[105][106][107][108] One study showed the results of a ReS-NET with a large dataset, 13 984 nodules, and found a comparable risk stratification when compared to senior clinicians. 109 Machine-and deep learning methods can be used to determine a few other parameters, for example, volume estimation.…”

The application value of deep learning-based nomograms in benign–malignant discrimination of TI-RADS category 4 thyroid nodules

“…These nodules can be further classified into subtypes 4a, 4b, and 4c, each requiring different interventions. Accurately classifying these nodules is critical to avoid unnecessary biopsies and surgeries 5 . The subjective nature of ultrasound interpretation and varying levels of diagnostic experience can result in the misclassification of nodules that should be categorized as TI-RADS 3 or 5 as category 4.…”