Computer-aided diagnosis system of thyroid nodules ultrasonography: Diagnostic performance difference between computer-aided diagnosis and 111 radiologists

Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistance

Background

Data-driven digital learning could improve the diagnostic performance of novice students for thyroid nodules.

Objective

To evaluate the efficacy of digital self-learning and artificial intelligence-based computer-assisted diagnosis (AI-CAD) for inexperienced readers to diagnose thyroid nodules.

Methods

Between February and August 2023, a total of 26 readers (less than 1 year of experience in thyroid US from various departments) from 6 hospitals participated in this study. Readers completed an online learning session comprising 3,000 thyroid nodules annotated as benign or malignant independently. They were asked to assess a test set consisting of 120 thyroid nodules with known surgical pathology before and after a learning session. Then, they referred to AI-CAD and made their final decisions on the thyroid nodules. Diagnostic performances before and after self-training and with AI-CAD assistance were evaluated and compared between radiology residents and readers from different specialties.

Results

AUC (area under the receiver operating characteristic curve) improved after the self-learning session, and it improved further after radiologists referred to AI-CAD (0.679 vs 0.713 vs 0.758, p<0.05). Although the 18 radiology residents showed improved AUC (0.7 to 0.743, p=0.016) and accuracy (69.9% to 74.2%, p=0.013) after self-learning, the readers from other departments did not. With AI-CAD assistance, sensitivity (radiology 70.3% to 74.9%, others 67.9% to 82.3%, all p<0.05) and accuracy (radiology 74.2% to 77.1%, others 64.4% to 72.8%, all p <0.05) improved in all readers.

Conclusion

While AI-CAD assistance helps improve the diagnostic performance of all inexperienced readers for thyroid nodules, self-learning was only effective for radiology residents with more background knowledge of ultrasonography.

Clinical Impact

Online self-learning, along with AI-CAD assistance, can effectively enhance the diagnostic performance of radiology residents in thyroid cancer.

Learnability of Thyroid Nodule Assessment on Ultrasonography: Using a Big Data Set

OBJECTIVE

The aims of the work described here were to evaluate the learnability of thyroid nodule assessment on ultrasonography (US) using a big data set of US images and to evaluate the diagnostic utilities of artificial intelligence computer-aided diagnosis (AI-CAD) used by readers with varying experience to differentiate benign and malignant thyroid nodules.

METHODS

Six college freshmen independently studied the "learning set" composed of images of 13,560 thyroid nodules, and their diagnostic performance was evaluated after their daily learning sessions using the "test set" composed of images of 282 thyroid nodules. The diagnostic performance of two residents and an experienced radiologist was evaluated using the same "test set." After an initial diagnosis, all readers once again evaluated the "test set" with the assistance of AI-CAD.

RESULTS

Diagnostic performance of almost all students increased after the learning program. Although the mean areas under the receiver operating characteristic curves (AUROCs) of residents and the experienced radiologist were significantly higher than those of students, the AUROCs of five of the six students did not differ significantly compared with that of the one resident. With the assistance of AI-CAD, sensitivity significantly increased in three students, specificity in one student, accuracy in four students and AUROC in four students. Diagnostic performance of the two residents and the experienced radiologist was better with the assistance of AI-CAD.

CONCLUSION

A self-learning method using a big data set of US images has potential as an ancillary tool alongside traditional training methods. With the assistance of AI-CAD, the diagnostic performance of readers with varying experience in thyroid imaging could be further improved.

Diagnostic performance of artificial intelligence-based computer-aided diagnosis system in longitudinal and transverse ultrasonic views for differentiating thyroid nodules

OBJECTIVE

To evaluate the diagnostic performance of different ultrasound sections of thyroid nodule (TN) using computer-aided diagnosis system based on artificial intelligence (AI-CADS) in predicting thyroid malignancy.

MATERIALS AND METHODS

This is a retrospective study. From January 2019 to July 2019, patients with preoperative thyroid ultrasound data and postoperative pathological results were enrolled, which were divided into two groups: lower risk group (ACR TI-RADS 1, 2 and 3) and higher risk group (ACR TI-RADS 4 and 5). The malignant risk scores (MRS) of TNs were obtained from longitudinal and transverse sections using AI-CADS. The diagnostic performance of AI-CADS and the consistency of each US characteristic were evaluated between these sections. The receiver operating characteristic (ROC) curve and the Cohen κ-statistic were performed.

RESULTS

A total of 203 patients (45.61 ± 11.59 years, 163 female) with 221 TNs were enrolled. The area under the ROC curve (AUC) of criterion 3 [0.86 (95%CI: 0.80~0.91)] was lower than criterion 1 [0.94 (95%CI: 0.90~ 0.99)], 2 [0.93 (95%CI: 0.89~0.97)] and 4 [0.94 (95%CI: 0.90, 0.99)] significantly (P<0.001, P=0.01, P<0.001, respectively). In the higher risk group, the MRS of transverse section was higher than longitudinal section (P<0.001), and the agreement of extrathyroidal extension and shape was moderate and fair (κ =0.48, 0.31 respectively). The diagnostic agreement of other ultrasonic features was substantial or almost perfect (κ >0.60).

CONCLUSION

The diagnostic performance of computer-aided diagnosis system based on artificial intelligence (AI-CADS) in longitudinal and transverse ultrasonic views for differentiating thyroid nodules (TN) was different, which was higher in the transverse section. It was more dependent on the section for the AI-CADS diagnosis of suspected malignant TNs.

Ultrasound-based deep learning using the VGGNet model for the differentiation of benign and malignant thyroid nodules: A meta-analysis

Objective

The aim of this study was to evaluate the accuracy of deep learning using the convolutional neural network VGGNet model in distinguishing benign and malignant thyroid nodules based on ultrasound images.

Methods

Relevant studies were selected from PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), and Wanfang databases, which used the deep learning-related convolutional neural network VGGNet model to classify benign and malignant thyroid nodules based on ultrasound images. Cytology and pathology were used as gold standards. Furthermore, reported eligibility and risk bias were assessed using the QUADAS-2 tool, and the diagnostic accuracy of deep learning VGGNet was analyzed with pooled sensitivity, pooled specificity, diagnostic odds ratio, and the area under the curve.

Results

A total of 11 studies were included in this meta-analysis. The overall estimates of sensitivity and specificity were 0.87 [95% CI (0.83, 0.91)] and 0.85 [95% CI (0.79, 0.90)], respectively. The diagnostic odds ratio was 38.79 [95% CI (22.49, 66.91)]. The area under the curve was 0.93 [95% CI (0.90, 0.95)]. No obvious publication bias was found.

Conclusion

Deep learning using the convolutional neural network VGGNet model based on ultrasound images performed good diagnostic efficacy in distinguishing benign and malignant thyroid nodules.

Systematic Review Registration

https://www.crd.york.ac.nk/prospero, identifier CRD42022336701.

Differential diagnosis and feature visualization for thyroid nodules using computer-aided ultrasonic diagnosis system: initial clinical assessment

BACKGROUND

To assess the diagnostic efficacy of the computer-aided ultrasonic diagnosis system (CAD system) in differentiating benign and malignant thyroid nodules.

METHODS

The images of 296 thyroid nodules were included in validation sets. The diagnostic efficacy of the CAD system was compared with that of junior physicians and senior physicians, as well as that of the combination diagnosis of the CAD system with junior physicians. The diagnostic efficacy of the CAD system for different sizes of thyroid nodules was compared.

RESULTS

The diagnostic sensitivity and accuracy of the CAD system were higher than those of junior physicians (83.4% vs. 72.2%, 73.0% vs. 69.6%), but the diagnostic specificity of the CAD system was lower than that of junior physicians (62.1% vs. 66.9%). The diagnostic accuracy of the CAD system was lower than that of senior physicians (73.0% vs. 83.8%). However, the combination diagnosis of the CAD system with junior physicians had higher accuracy (81.8%) and AUC (0.842) than those of either the CAD system or junior physicians alone, and comparable diagnostic performance with those of senior physicians. The Kappa was 0.635 in the combination diagnosis of the CAD system with junior physicians, showing good consistency with the pathological results. The accuracy (76.4%) of the CAD system was the highest for nodules of 1-2 cm.

CONCLUSION

The CAD system can effectively assist physicians to identify malignant and benign thyroid nodules, reduce the overdiagnosis and overtreatment of thyroid nodules, avoid unnecessary invasive fine needle aspiration, and improve the diagnostic accuracy of junior physicians.

Comparative Analysis of Computer-Aided Diagnosis and Computer-Assisted Subjective Assessment in Thyroid Ultrasound

The value of computer-aided diagnosis (CAD) and computer-assisted techniques equipped with different TIRADS remains ambiguous. Parallel diagnosis performances of computer-assisted subjective assessments and CAD were compared based on AACE, ATA, EU, and KSThR TIRADS. CAD software computed the diagnosis of 162 thyroid nodule sonograms. Two raters (R₁ and R₂) independently rated the sonographic features of the nodules using an online risk calculator while blinded to pathology results. Diagnostic efficiency measures were calculated based on the final pathology results. R₁ had higher diagnostic performance outcomes than CAD with similarities between KSThR (SEN: 90.3% vs. 83.9%, p = 0.57; SPEC: 46% vs. 51%, p = 0.21; AUROC: 0.76 vs. 0.67, p = 0.02), and EU (SEN: 85.5% vs. 79%, p = 0.82; SPEC: 62% vs. 55%, p = 0.27; AUROC: 0.74 vs. 0.67, p = 0.06). Similarly, R₂ had higher AUROC and specificity but lower sensitivity than CAD (KSThR-AUROC: 0.74 vs. 0.67, p = 0.13; SPEC: 61% vs. 46%, p = 0.02 and SEN: 75.8% vs. 83.9%, p = 0.31, and EU-AUROC: 0.69 vs. 0.67, p = 0.57, SPEC: 64% vs. 55%, p = 0.19, and SEN: 71% vs. 79%, p = 0.51, respectively). CAD had higher sensitivity but lower specificity than both R₁ and R₂ with AACE for 114 specified nodules (SEN: 92.5% vs. 88.7%, p = 0.50; 92.5% vs. 79.3%, p = 0.02, and SPEC: 26.2% vs. 54.1%, p = 0.001; 26.2% vs. 62.3%, p < 0.001, respectively). All diagnostic performance outcomes were comparable for ATA with 96 specified nodules. Computer-assisted subjective interpretation using KSThR is more ideal for ruling out papillary thyroid carcinomas than CAD. Future larger multi-center and multi-rater prospective studies with a diverse representation of thyroid cancers are necessary to validate these findings.

Diagnosing thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance cytology with the deep convolutional neural network

To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined significance (FLUS) results on fine-needle aspiration (FNA). This study included 202 patients with 202 nodules ≥ 1 cm AUS/FLUS on FNA, and underwent surgery in one of 3 different institutions. Diagnostic performances were compared between 8 physicians (4 radiologists, 4 endocrinologists) with varying experience levels and CNN, and AUS/FLUS subgroups were analyzed. Interobserver variability was assessed among the 8 physicians. Of the 202 nodules, 158 were AUS, and 44 were FLUS; 86 were benign, and 116 were malignant. The area under the curves (AUCs) of the 8 physicians and CNN were 0.680–0.722 and 0.666, without significant differences (P > 0.05). In the subgroup analysis, the AUCs for the 8 physicians and CNN were 0.657–0.768 and 0.652 for AUS, 0.469–0.674 and 0.622 for FLUS. Interobserver agreements were moderate (k = 0.543), substantial (k = 0.652), and moderate (k = 0.455) among the 8 physicians, 4 radiologists, and 4 endocrinologists. For thyroid nodules with AUS/FLUS cytology, the diagnostic performance of CNN to differentiate malignancy with US images was comparable to that of physicians with variable experience levels.

Effects of 1 year of training on the performance of ultrasonographic image interpretation: A preliminary evaluation using images of Sjögren syndrome patients

Purpose

This study investigated the effects of 1 year of training on imaging diagnosis, using static ultrasonography (US) salivary gland images of Sjögren syndrome patients.

Materials and Methods

This study involved 3 inexperienced radiologists with different levels of experience, who received training 1 or 2 days a week under the supervision of experienced radiologists. The training program included collecting patient histories and performing physical and imaging examinations for various maxillofacial diseases. The 3 radiologists (observers A, B, and C) evaluated 400 static US images of salivary glands twice at a 1-year interval. To compare their performance, 2 experienced radiologists evaluated the same images. Diagnostic performance was compared between the 2 evaluations using the area under the receiver operating characteristic curve (AUC).

Results

Observer A, who was participating in the training program for the second year, exhibited no significant difference in AUC between the first and second evaluations, with results consistently comparable to those of experienced radiologists. After 1 year of training, observer B showed significantly higher AUCs than before training. The diagnostic performance of observer B reached the level of experienced radiologists for parotid gland assessment, but differed for submandibular gland assessment. For observer C, who did not complete the training, there was no significant difference in the AUC between the first and second evaluations, both of which showed significant differences from those of the experienced radiologists.

Conclusion

These preliminary results suggest that the training program effectively helped inexperienced radiologists reach the level of experienced radiologists for US examinations.

Software-Based Analysis of the Taller-Than-Wide Feature of High-Risk Thyroid Nodules

BACKGROUND

Shape is one of the most important features in the diagnosis of malignant thyroid nodules. This characteristic has been described qualitatively, but only shapes that appear markedly different can be easily differentiated at first interpretation. This study sought to clarify whether software-based shape indexes are useful for the detection of thyroid cancers.

METHODS

In the final analysis, 200 participants with 231 pathologically proven nodules participated in the study. Ultrasound features were assessed by clinicians. The tumor contour was auto-defined, and shape indexes were calculated using commercial software.

RESULTS

Of the 231 nodules, 134 were benign and 97 were malignant. The presence of taller-than-wide (TTW) dimensions differed significantly between the benign and malignant thyroid tumors. Designation of TTW assessed by the software had a higher kappa value and proportional agreement than TTW assessed by clinicians. Disagreement between the clinician and software in designating nodules as TTW occurred for 28 nodules. The presence of other ultrasonic characteristics and small differences in the height and width measurements were causes for the incorrect interpretation of the TTW feature.

CONCLUSION

The proposed software-based quantitative analysis of tumor shape seems to be promising as an important advance compared with conventional TTW features evaluated by operators because it allows for a more reliable and consistent distinction and is less influenced by other ultrasonic features.