Contrast-Enhanced Ultrasound Improves the Accuracy of the ACR TI-RADS in the Diagnosis of Thyroid Nodules Located in the Isthmus

Objectives To evaluate the diagnostic performance of the American College of Radiology (ACR) Thyroid Image Reporting and Data System (TI-RADS), contrast-enhanced ultrasound (CEUS), and a modified TI-RADS in differentiating benign and malignant nodules located in the isthmus. Methods This retrospective study was approved by the institutional review board. Informed consent was obtained. Grayscale ultrasound (US) and CEUS images were obtained for 203 isthmic thyroid nodules (46 benign and 157 malignant) in 198 consecutive patients (156 women, mean age: 44.7 years ± 11.3 [standard deviation]; 47 men, mean age: 40.9 years ± 11.0). The area under the receiver operating characteristic curve (AUC) of the diagnostic performance of the ACR TI-RADS, CEUS, and the modified TI-RADS were evaluated. Results Lobulated or irregular margins (P = 0.001; odds ratio [OR] = 9.250) and punctate echogenic foci (P = 0.007; OR = 4.718) on US and hypoenhancement (P < 0.001; OR = 20.888) on CEUS displayed a significant association with malignancy located in the isthmus. The most valuable method to distinguish benign nodules from malignant nodules was the modified TI-RADS (AUC: 0.863 with modified TR5), which was significantly better than the ACR TI-RADS (AUC: 0.738 with ACR TR5) (P < 0.001) but showed no significant difference with respect to CEUS (AUC: 0.835 with hypoenhancement) (P = 0.205). The diagnostic value was significantly different between CEUS and the ACR TI-RADS (P = 0.028). Conclusion The modified TI-RADS could significantly improve the accuracy of the diagnosis of thyroid nodules located in the isthmus.

When Radiology Report Generation Meets Knowledge Graph

Automatic radiology report generation has been an attracting research problem towards computer-aided diagnosis to alleviate the workload of doctors in recent years. Deep learning techniques for natural image captioning are successfully adapted to generating radiology reports. However, radiology image reporting is different from the natural image captioning task in two aspects: 1) the accuracy of positive disease keyword mentions is critical in radiology image reporting in comparison to the equivalent importance of every single word in a natural image caption; 2) the evaluation of reporting quality should focus more on matching the disease keywords and their associated attributes instead of counting the occurrence of N-gram. Based on these concerns, we propose to utilize a pre-constructed graph embedding module (modeled with a graph convolutional neural network) on multiple disease findings to assist the generation of reports in this work. The incorporation of knowledge graph allows for dedicated feature learning for each disease finding and the relationship modeling between them. In addition, we proposed a new evaluation metric for radiology image reporting with the assistance of the same composed graph. Experimental results demonstrate the superior performance of the methods integrated with the proposed graph embedding module on a publicly accessible dataset (IU-RR) of chest radiographs compared with previous approaches using both the conventional evaluation metrics commonly adopted for image captioning and our proposed ones.

Artificial intelligence in diagnostic imaging: impact on the radiography profession

The arrival of artificially intelligent systems into the domain of medical imaging has focused attention and sparked much debate on the role and responsibilities of the radiologist. However, discussion about the impact of such technology on the radiographer role is lacking. This paper discusses the potential impact of artificial intelligence (AI) on the radiography profession by assessing current workflow and cross-mapping potential areas of AI automation such as procedure planning, image acquisition and processing. We also highlight the opportunities that AI brings including enhancing patient-facing care, increased cross-modality education and working, increased technological expertise and expansion of radiographer responsibility into AI-supported image reporting and auditing roles.