According to an article published in the American Journal of Roentgenology (AJR), a Stanford University team has developed a quantitative framework able to sonographically differentiate between benign and malignant thyroid nodules at a level comparable to that of expert radiologists, which may prove useful for establishing a fully automated system of thyroid nodule triage.

Alfiia Galimzianova et al. retrospectively collected ultrasound images of 92 biopsy-confirmed nodules, which were annotated by two expert radiologists using the American College of Radiology's Thyroid Imaging Reporting and Data System (TI-RADS).

In the researchers' framework, nodule features of echogenicity, texture, edge sharpness, and margin curvature properties were analysed in a regularised logistic regression model to predict nodule malignancy.

Authenticating their method with leave-one-out cross-validation, the Stanford team used ROC AUC, sensitivity, and specificity to compare the framework's results with those obtained by six expert annotation-based classifiers.

The AUC of the proposed framework measured 0.828 (95% CI, 0.715-0.942) - "greater than or comparable," Galimzianova noted, "to that of the expert classifiers" - whose AUC values ranged from 0.299 to 0.829 (p = 0.99).

Additionally, in a curative strategy at sensitivity of 1, use of the framework could have avoided biopsy in 20 of 46 benign nodules - statistically significantly higher than three expert classifiers.

In a conservative strategy at specificity of 1, the framework could have helped to identify 10 of 46 malignancies - statistically significantly higher than five expert classifiers.

"Our results confirm the ultimate feasibility of computer-aided diagnostic systems for thyroid cancer risk estimation," concluded Galimzianova. "Such systems could provide second-opinion malignancy risk estimation to clinicians and ultimately help decrease the number of unnecessary biopsies and surgical procedures."

Source: American Roentgen Ray Society