The Diagnostic Role of Computed Tomography for ACR TI-RADS 4-5 Thyroid Nodules With Coarse Calcifications

Benign thyroid nodules classified as ACR TI-RADS 4 or 5: Imaging and histological features

BACKGROUND

American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) being most widely applied in clinical practice, there is an overlap in US imaging manifestations between benign and malignant thyroid nodules.

OBJECTIVES

To analyze the imaging and histological characteristics of pathological benign thyroid nodules categorized as American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) 4 or 5, and to explore the correlation between the suspicious sonographic signs resulting in the misdiagnoses and the histopathological features.

MATERIALS AND METHODS

Overall, 227 benign thyroid nodules (215 patients) in ACR TI-RADS 4 or 5 sampled through surgical excision were analyzed between December 2020 and August 2022. We retrospectively reread the ultrasound (US) images of the pathological discordant cases, after which we performed a systematic analysis focusing on the histopathological characteristics of thyroid lesions and recorded the findings. Qualitative US features and pathological significance of the thyroid nodules were analyzed using the chi-square and Fisher's exact tests.

RESULTS

The pathological type of 227 thyroid nodules (n = 103 in ACR TI-RADS 4 and n = 124 in ACR TI-RADS 5) was nodular goiter together with other histopathological features, namely, fibrosis (n = 103, 45.4 %), calcification (n = 70, 30.8 %), adenomatous hyperplasia (n = 31, 13.7 %), follicular epithelial hyperplasia (n = 23, 10.1 %), Hashimoto's thyroiditis (n = 18, 7.9 %), and cystic degeneration (n = 16, 7.1 %). Fibrosis was the most common histopathological feature in both ACR TI-RADS 4 (n = 42, 40.8 %) and 5 (n = 61, 49.2 %) categories of benign thyroid nodules. Thyroid nodules with fibrosis demonstrated sonographic features of "taller than wide" (p < 0.05), while lesions with follicular epithelial hyperplasia were likely to be detected with irregular and/or lobulated margins and very hypoechoic on US (p < 0.05 for both).

CONCLUSION

Benign thyroid nodules with histopathological findings such as fibrosis are associated with suspicious US features, which may give inappropriately higher TIRADS stratification.

The auxiliary diagnosis of thyroid echogenic foci based on a deep learning segmentation model: A two-center study

OBJECTIVE

The aim of this study is to develop AI-assisted software incorporating a deep learning (DL) model based on static ultrasound images. The software aims to aid physicians in distinguishing between malignant and benign thyroid nodules with echogenic foci and to investigate how the AI-assisted DL model can enhance radiologists' diagnostic performance.

METHODS

For this retrospective study, a total of 2724 ultrasound (US) scans were collected from two independent institutions, encompassing 1038 echogenic foci nodules. All echogenic foci were confirmed by pathology. Three DL segmentation models (DeepLabV3+, U-Net, and PSPNet) were developed, with each model using two different backbones to extract features from the nodular regions with echogenic foci. Evaluation indexes such as Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA), and Dice coefficients were employed to assess the performance of the segmentation model. The model demonstrating the best performance was selected to develop the AI-assisted diagnostic software, enabling radiologists to benefit from AI-assisted diagnosis. The diagnostic performance of radiologists with varying levels of seniority and beginner radiologists in assessing high-echo nodules was then compared, both with and without the use of auxiliary strategies. The area under the receiver operating characteristic curve (AUROC) was used as the primary evaluation index, both with and without the use of auxiliary strategies.

RESULTS

In the analysis of Institution 2, the DeepLabV3+ (backbone is MobileNetV2 exhibited optimal segmentation performance, with MIoU = 0.891, MPA = 0.945, and Dice = 0.919. The combined AUROC (0.693 [95% CI 0.595-0.791]) of radiology beginners using AI-assisted strategies was significantly higher than those without such strategies (0.551 [0.445-0.657]). Additionally, the combined AUROC of junior physicians employing adjuvant strategies improved from 0.674 [0.574-0.774] to 0.757 [0.666-0.848]. Similarly, the combined AUROC of senior physicians increased slightly, rising from 0.745 [0.652-0.838] to 0.813 [0.730-0.896]. With the implementation of AI-assisted strategies, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of both senior physicians and beginners in the radiology department underwent varying degrees of improvement.

CONCLUSIONS

This study demonstrates that the DL-based auxiliary diagnosis model using US static images can improve the performance of radiologists and radiology students in identifying thyroid echogenic foci.

Incidental Thyroid Nodule on Chest Computed Tomography: Application of Computed Tomography Texture Analysis in Prediction of Ultrasound Classification

OBJECTIVE

The aim of the study was to evaluate the value of computed tomography (CT) texture analysis (CTTA) in predicting ultrasound (US) classification of incidentally detected thyroid nodule (ITN) on chest CT.

METHODS

A total of 117 ITNs (≥1 cm in the longest diameter) on chest CT scan of 107 patients was divided into 4 categories according to the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) classification on recent thyroid US within 3 months. Computed tomography texture features were extracted with or without filtration using commercial software. The texture features were compared between the benign (K-TIRADS 2; n = 21) and the suspicious (K-TIRADS 3, 4, 5; n = 96) nodules. Multivariate regression and area under the receiver operating characteristic curve analysis were performed to determine significant prediction factors of the suspicious nodules.

RESULTS

The mean value of positive pixels was significantly higher in the suspicious nodules except the unfiltered image (P < 0.05). Entropy of the suspicious nodules was significantly higher with unfiltered and fine filters (P < 0.05), and kurtosis of the suspicious nodules was significantly higher with medium and coarse filters (P < 0.05). A logistic regression model incorporating mean value of positive pixels and kurtosis with a medium filter using volumetric analysis demonstrated the best performance to predict the suspicious nodules with an area under the receiver operating characteristic curve of 0.842 (P < 0.001, sensitivity 82.3%, and specificity 81.0%).

CONCLUSIONS

Computed tomography texture analysis for ITN larger than 1 cm showed significant correlation with systematic thyroid US classification and presented excellent performance to predict the suspicious nodules.