Machine Learning by Ultrasonography for Genetic Risk Stratification of Thyroid Nodules.

Daniels K1, Gummadi S2,3, Zhu Z4, Wang S2, Patel J5, Swendseid B5, Lyshchik A2, Curry J5, Cottrill E5, Eisenbrey J2.

Author information

1: Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania.
2: Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania.
3: Department of Surgery, Lankenau Medical Center, Wynnewood, Pennsylvania.
4: Beijing Friendship Hospital, Capital Medical University, Beijing, China.
5: Department of Otolaryngology-Head and Neck Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania.

Abstract

IMPORTANCE:

Thyroid nodules are common incidental findings. Ultrasonography and molecular testing can be used to assess risk of malignant neoplasm.

OBJECTIVE:

To examine whether a model developed through automated machine learning can stratify thyroid nodules as high or low genetic risk by ultrasonography imaging alone compared with stratification by molecular testing for high- and low-risk mutations.

DESIGN, SETTING, AND PARTICIPANTS:

This diagnostic study was conducted at a single tertiary care urban academic institution and included patients (n = 121) who underwent ultrasonography and molecular testing for thyroid nodules from January 1, 2017, through August 1, 2018. Nodules were classified as high risk or low risk on the basis of results of an institutional molecular testing panel for thyroid risk genes. All thyroid nodules that underwent genetic sequencing for cytological results with Bethesda System categories III and IV were reviewed. Patients without diagnostic ultrasonographic images within 6 months of fine-needle aspiration or who received definitive treatment at an outside medical center were excluded.

MAIN OUTCOMES AND MEASURES:

Thyroid nodules were categorized by the model as high risk or low risk using ultrasonographic images. Results were compared using genetic testing.

RESULTS:

Among the 134 lesions identified in 121 patients (mean [SD] age, 55.7 [14.2] years; 102 women [84.3%]), 683 diagnostic ultrasonographic images were selected. Of the 683 images, 556 (81.4%) were used for training the model, 74 (10.8%) for validation, and 53 (7.8%) for testing. Most nodules had no mutation (75 [56.0%]), whereas 43 nodules (32.1%) had a high-risk mutation and 16 (11.9%) had an unknown or a low-risk mutation (χ2 = 39.060; P < .001). In total, 228 images (33.4%) were of nodules classified as genetically high risk (n = 43), and 455 (66.6%) were of low-risk nodules (n = 91). The model performed with a sensitivity of 45% (95% CI, 23.1%-68.5%), a specificity of 97% (95% CI, 84.2%-99.9%), a positive predictive value of 90% (95% CI, 55.2%-98.5%), a negative predictive value of 74.4% (95% CI, 66.1%-81.3%), and an overall accuracy of 77.4% (95% CI, 63.8%-97.7%).

CONCLUSIONS AND RELEVANCE:

The study found that the model developed through automated machine learning could produce high specificity for identifying nodules with high-risk mutations on molecular testing. This finding shows promise for the diagnostic applications of machine learning interpretation of sonographic imaging of indeterminate thyroid nodules.