Robinson, Kathryn A.

Thyroid Ultrasound (US) is the primary method to evaluate thyroid nodules. Deep learning (DL) has been playing a significant role in evaluating thyroid cancer. We propose a DL-based pipeline to detect and classify thyroid nodules into benign or malignant groups relying on two views of US imaging. Transverse and longitudinal US images of thyroid nodules from 983 patients were collected retrospectively. Eighty-one cases were held out as a testing set, and the rest of the data were used in five-fold cross-validation (CV). Two You Look Only Once (YOLO) v5 models were trained to detect nodules and classify them. For each view, five models were developed during the CV, which was ensembled by using non-max suppression (NMS) to boost their collective generalizability. An extreme gradient boosting (XGBoost) model was trained on the outputs of the ensembled models for both views to yield a final prediction of malignancy for each nodule. The test set was evaluated by an expert radiologist using the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS). The ensemble models for each view achieved a mAP0.5 of 0.797 (transverse) and 0.716 (longitudinal). The whole pipeline reached an AUROC of 0.84 (CI 95%: 0.75–0.91) with sensitivity and specificity of 84% and 63%, respectively, while the ACR-TIRADS evaluation of the same set had a sensitivity of 76% and specificity of 34% (p-value = 0.003). Our proposed work demonstrated the potential possibility of a deep learning model to achieve diagnostic performance for thyroid nodule evaluation.

Abstract Breast pain is extremely common, occurring in 70% to 80% of women. Most cases of breast pain are from physiologic or benign causes, and patients should be reassured and offered treatment strategies to alleviate symptoms, often without diagnostic imaging. A complete clinical history and physical examination is key for distinguishing intrinsic breast pain from extramammary pain. Breast pain without other suspicious symptoms and with a negative history and physical examination result is rarely associated with malignancy, although it is a common reason for women to undergo diagnostic imaging. When breast imaging is indicated, guidelines according to the American College of Radiology Appropriateness Criteria should be followed as to whether mammography, US, or both are recommended. This review article summarizes the initial clinical evaluation of breast pain and evidence-based guidelines for imaging. Additionally, the article reviews cyclical and noncyclical breast pain and provides an image-rich discussion of the imaging presentation and management of benign and malignant breast pain etiologies.

Low specificity in current ultrasound modalities for thyroid cancer detection necessitates the development of new imaging modalities for optimal characterization of thyroid nodules. Herein, the quantitative biomarkers of a new high-definition microvessel imaging (HDMI) were evaluated for discrimination of benign from malignant thyroid nodules. Without the help of contrast agents, this new ultrasound-based quantitative technique utilizes processing methods including clutter filtering, denoising, vessel enhancement filtering, morphological filtering, and vessel segmentation to resolve tumor microvessels at size scales of a few hundred microns and enables the extraction of vessel morphological features as new tumor biomarkers. We evaluated quantitative HDMI on 92 patients with 92 thyroid nodules identified in ultrasound. A total of 12 biomarkers derived from vessel morphological parameters were associated with pathology results. Using the Wilcoxon rank-sum test, six of the twelve biomarkers were significantly different in distribution between the malignant and benign nodules (all p < 0.01). A support vector machine (SVM)-based classification model was trained on these six biomarkers, and the receiver operating characteristic curve (ROC) showed an area under the curve (AUC) of 0.9005 (95% CI: [0.8279,0.9732]) with sensitivity, specificity, and accuracy of 0.7778, 0.9474, and 0.8929, respectively. When additional clinical data, namely TI-RADS, age, and nodule size were added to the features, model performance reached an AUC of 0.9044 (95% CI: [0.8331,0.9757]) with sensitivity, specificity, and accuracy of 0.8750, 0.8235, and 0.8400, respectively. Our findings suggest that tumor vessel morphological features may improve the characterization of thyroid nodules.

ObjectiveTo analyze and classify arterial supply and venous drainage of postcatheterization femoral arteriovenous fistulas (AVFs).MethodsA review of extremity Doppler reports identified 77 femoral AVFs in 75 patients. Doppler exams were reviewed retrospectively. Fistulas were classified as above or below the common femoral artery bifurcation and subclassified based on the location of arterial inflow and venous outflow.ResultsArterial inflow originated above the femoral bifurcation in 32 cases. The communication was between the common femoral artery and the superficial circumflex iliac vein in 25 of 32 cases and between a branch of the common femoral or external iliac artery and the common femoral or external iliac vein in 4 of 32 cases. In 3 of 32 cases, AVFs arose from the common femoral artery, but the venous outflow was not determined. Arterial inflow originated from the superficial femoral artery in 23 cases. Venous outflow originated from the common femoral vein in 10 of 23 cases, the femoral vein in 7 of 23 cases, and the lateral circumflex femoral vein in 6 of 23 cases. Arterial inflow originated from the deep femoral artery in 12 cases. Venous outflow originated from the common femoral vein in 6 of 12 cases and from the lateral circumflex femoral vein 6 of 12 cases. In 8 cases, the AVF originated below the bifurcation, but the arterial inflow was not classified. In 2 cases, it was impossible to determine if the AVF originated above or below the bifurcation.ConclusionsIatrogenic femoral AVFs arise above the femoral bifurcation more often than previously recognized. Classification based on the arterial inflow and venous outflow provides a straightforward means of describing these fistulas.