Thyroid nodules risk stratification through deep learning based on ultrasound images

Abstract

Purpose Clinically, the risk stratification of thyroid nodules is usually used to formulate the next treatment plan. The American College of Radiology (ACR) thyroid imaging reporting and data system (TI‐RADS) is a type of medical standard widely used in classification diagnosis. It divides the nodule’s ACR TI‐RADS level into five levels by quantitative scoring, from benign to high suspicion of malignancy. However, such assessment often relies on the radiologists’ experience and is time consuming. So computer‐aided diagnosis is necessary. But many deep learning (DL) models are difficult for doctors to understand, limiting their applicability in clinical practice. In this work, we mainly focus on how to achieve automatic thyroid nodules risk stratification based on deep integration of deep learning and clinical experience. Methods An automatic hierarchical method of thyroid nodules risk based on deep learning is proposed, called risk stratification network (RS‐Net). It incorporates medical experience based on ACR TI‐RADS. The convolutional neural network (CNN) is used to classify the five categories in ACR TI‐RADS and assign their points respectively. According to the point totals, the level of risk can be obtained. In addition, a dataset involving 13 984 thyroid ultrasound images is established to develop and evaluate the proposed method. Results We have extensively compared the results of this paper with the evaluation results of sonographers. The accuracy of the risk stratification (TR1 to TR5) of the proposed method is 65%, and the mean absolute error (MAE) is 0.54. The MAE of the point totals (0 to 13 points) is 1.67. The Pearson's correlation between our method evaluation and doctor evaluation reached 0.84. For the benign and malignant classification, the performance indices accuracy, sensitivity, specificity, PPV, and NPV were 88.0%, 98.1%, 79.1%, 80.5%, and 97.9%, respectively. Our method's level of thyroid nodules risk stratification is comparable to that of a senior doctor. Conclusions This work provides a way to automate the risk stratification of thyroid nodules. Our method can effectively avoid missed diagnosis and misdiagnosis caused by the difference of observers so as to assist doctors to improve efficiency and diagnosis rate. Compared with the previous benign and malignant classification, the proposed method incorporates clinical experience. So it can greatly increase the clinicians’ trust in the DL model, thereby improving the applicability of the model in clinical practice.

Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives

“…The novel AI TI-RADS had better accuracy than ARC TI-RADS when performed by inexperienced radiologists (55% vs. 48%) and experts (65% vs. 47%). Similar to other studies, ARC TI-RADS-based classifiers had higher sensitivity and slightly lower specificity ( 21 – 24 ). Wu et al.…”

Artificial Intelligence for Thyroid Nodule Characterization: Where Are We Standing?

“…All the evaluated studies showed significant high overall diagnostic accuracy of CNNs, above 90%, which does not differ much from that of expert radiologists. In particular, most of the studies demonstrate a comparable diagnostic accuracy, such as Watkins et al, Bai et al, Ye et al, Koh et al, and Fresilli et al [ 4 , 16 , 20 , 30 , 40 ]. Approximately the same number of studies demonstrate a higher diagnostic accuracy of AI systems compared to that of expert radiologists (e.g., Sun et al, Peng et al, and Zhou et al) [ 15 , 22 , 23 ], or vice versa, a superiority of diagnostic accuracy by expert radiologists compared to that of AI systems (e.g., Zhang et al and Han et al) [ 32 , 33 ].…”

“…In the world of DL, many authors are focusing on convolutional neural networks (CNNs), introduced by LeCun [ 65 , 66 ]. Before their diagnostic accuracy can be assessed, CNNs are trained by subjecting them to specific algorithm-segmented US images of thyroid nodules with known histological diagnosis; at the end of the learning phase the CNNs are able to analyze the captures of thyroid nodules and to suggest a risk stratification of these nodules in correlation to a specific TI-RADS level [ 16 ]. Most of the existing literature evaluates the diagnostic accuracy of various types of properly trained convolutional neural networks by comparing them to those of radiologists with variable degrees of experience.…”

“…The search identified 166 studies from January 2012 to April 2022; of these, 63 were further considered. After a full text read, the final studies included in the review were 30 in number; they are all listed below in Table 1 [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ].…”

Automatic Classification of Hepatic Cystic Echinococcosis Using Ultrasound Images and Deep Learning

“…The subjectivity of diagnostic criteria to each sonographer may also result in a poor interobserver agreement. An alternative approach would be the use of the computer‐aided diagnosis (CAD) system, which has been applied in various diseases in the past few decades and achieved outstanding performance in most cases 16–20 . Recently, deep learning with convolutional neural networks (CNNs) has been gaining attention with respect to pattern recognition of images and as an artificial intelligence strategy used in CAD systems as it has distinct advantages over traditional machine learning methods in providing an end‐to‐end feature extraction and efficient classification framework to free users from the troublesome handcrafted feature extraction 21–24 .…”