Quantitative Framework for Risk Stratification of Thyroid Nodules With Ultrasound: A Step Toward Automated Triage of Thyroid Cancer.

OBJECTIVE. The purpose of this study was to explore whether a quantitative framework can be used to sonographically differentiate benign and malignant thyroid nodules at a level comparable to that of experts. MATERIALS AND METHODS. A dataset of ultrasound images of 92 biopsy-confirmed nodules was collected retrospectively. The nodules were delineated and annotated by two expert radiologists using the standardized Thyroid Imaging Reporting and Data System lexicon of the American College of Radiology. In the framework studied, quantitative features of echogenicity, texture, edge sharpness, and margin curvature properties of thyroid nodules were analyzed in a regularized logistic regression model to predict malignancy of a nodule. The framework was validated by leave-one-out cross-validation technique, and ROC AUC, sensitivity, and specificity were used to compare with those obtained with six expert annotation-based classifiers. RESULTS. The AUC of the proposed method was 0.828 (95% CI, 0.715-0.942), which was greater than or comparable to that of the expert classifiers, for which the AUC values ranged from 0.299 to 0.829 (p = 0.99). Use of the proposed framework could have avoided biopsy of 20 of 46 benign nodules in a curative strategy (at sensitivity of 1, statistically significantly higher than three expert classifiers) or helped identify 10 of 46 malignancies in a conservative strategy (at specificity of 1, statistically significantly higher than five expert classifiers). CONCLUSION. When the proposed quantitative framework was used, thyroid nodule malignancy was predicted at the level of expert classifiers. Such a framework may ultimately prove useful as the basis for a fully automated system of thyroid nodule triage.

Central echogenic areas in thyroid nodules: Diagnostic performance in prediction of papillary cancer.

PURPOSE: To determine the diagnostic performance of the "central echogenic area" sonographic finding in differentiating papillary carcinomas from benign nodules and to how this finding may be used to improve fine needle aspiration(FNA) technique/utilization. MATERIALS AND METHODS: We retrospectively analyzed ultrasound guided FNAs of thyroid nodules between 1 and 3 cm for central echogenic areas. 92 patients (evenly distributed benign vs papillary carcinoma) were evaluated by a blinded reader for areas of non-shadowing homogenously echogenic centers within the nodules and correlated with FNA proven pathologic diagnosis. A selection of nodules with the central echogenic area finding were selected for further slide review to establish a pathologic basis for the finding. RESULTS: Diagnostic performance of the "central echogenic area" feature in papillary thyroid cancers was 52.2% sensitive and 91.3% specific for papillary thyroid carcinoma with a PPV of 85.7% and NPV of 65.6%. There was a significant correlation with a p < 0.01 between the central echogenic area finding and papillary carcinoma. On pathologic slide review, nodules with central echogenic areas consistently demonstrated a central scar with conglomerate fibrosis and very few viable cells. CONCLUSION: Despite its relatively low sensitivity, the central echogenic area finding is highly specific for papillary carcinoma of the thyroid and can be a useful sonographic finding in decisions regarding FNA. Additionally, due to the paucity of cells and high density of conglomerate fibrosis, central echogenic areas should be avoided during FNA to decrease the chance of an inadequate sample collection.

Diagnostic Performance of Margin Features in Thyroid Nodules in Prediction of Malignancy.

OBJECTIVE: The purpose of this study was to evaluate thyroid nodule margins for specific morphologic features and determine the diagnostic performance of these features in differentiating papillary carcinoma from benign thyroid nodules. MATERIALS AND METHODS: Nodules measuring 1-3 cm in largest diameter that had been evaluated with high-resolution ultrasound (12-18 MHz) and ultrasound-guided biopsy with definitive pathologic diagnosis were analyzed. Three blinded board-certified readers evaluated high-resolution images of each nodule for jagged edges, lobulated borders, and curved borders along their margins. Reader interpretations were correlated with the pathologic diagnosis to determine the diagnostic performance of each feature. A board-certified pathologist analyzed 10 randomly selected nodules with jagged edges by slide review to evaluate for structural correlation with the imaging finding. RESULTS: The diagnostic performance of jagged edges in papillary carcinoma of the thyroid was 67.4% sensitive and 78.3% specific (odds ratio, 7.44; p < 0.001) for malignancy. Jagged edges correlated with infiltrative variant expansion at slide review. Lobulated borders had sensitivity of 76.1% and specificity of 60.9% for papillary carcinoma (odds ratio, 4.95; p = 0.001) for malignancy. Curved borders were not a significant predictor of papillary carcinoma. CONCLUSION: Jagged edges and lobulated borders of thyroid nodule margins are statistically significant predictors of papillary carcinoma of the thyroid. Jagged edges correlate with infiltrative-type expansion and may be useful predictors of more aggressive papillary carcinomas.

Toward Automated Pre-Biopsy Thyroid Cancer Risk Estimation in Ultrasound.

We propose a computational framework for automated cancer risk estimation of thyroid nodules visualized in ultrasound (US) images. Our framework estimates the probability of nodule malignancy using random forests on a rich set of computational features. An expert radiologist annotated thyroid nodules in 93 biopsy-confirmed patients using semantic image descriptors derived from standardized lexicon. On our dataset, the AUC of the proposed method was 0.70, which was comparable to five baseline expert annotation-based classifiers with AUC values from 0.72 to 0.81. Moreover, the use of the framework for decision making on nodule biopsy could have spared five out of 46 benign nodule biopsies at no cost to the health of patients with malignancies. Our results confirm the feasibility of computer-aided tools for noninvasive malignancy risk estimation in patients with thyroid nodules that could help to decrease the number of unnecessary biopsies and surgeries.

Implantable, multifunctional, bioresorbable optics.

Advances in personalized medicine are symbiotic with the development of novel technologies for biomedical devices. We present an approach that combines enhanced imaging of malignancies, therapeutics, and feedback about therapeutics in a single implantable, biocompatible, and resorbable device. This confluence of form and function is accomplished by capitalizing on the unique properties of silk proteins as a mechanically robust, biocompatible, optically clear biomaterial matrix that can house, stabilize, and retain the function of therapeutic components. By developing a form of high-quality microstructured optical elements, improved imaging of malignancies and of treatment monitoring can be achieved. The results demonstrate a unique family of devices for in vitro and in vivo use that provide functional biomaterials with built-in optical signal and contrast enhancement, demonstrated here with simultaneous drug delivery and feedback about drug delivery with no adverse biological effects, all while slowly degrading to regenerate native tissue.

Silk-based conformal, adhesive, edible food sensors.

An array of passive metamaterial antennas fabricated on all protein-based silk substrates were conformally transferred and adhered to the surface of an apple. This process allows the opportunity for intimate contact of micro- and nanostructures that can probe, and accordingly monitor changes in, their surrounding environment. This provides in situ monitoring of food quality. It is to be noted that this type of sensor consists of all edible and biodegradable components, holding utility and potential relevance for healthcare and food/consumer products and markets.