Michael Cordes1,2, Theresa Ida Götz3, Elmar Wolfgang Lang3, Stephan Coerper4, Torsten Kuwert5, Christian Schmidkonz5. 1. Radiologisch-Nuklearmedizinisches Zentrum, Martin-Richter-Str. 43, 90489, Nürnberg, Germany. michael.cordes@rpnl.de. 2. Nuklearmedizinische Klinik, Universitätsklinikum Erlangen, Erlangen, Germany. michael.cordes@rpnl.de. 3. CIML Group, Biophysics, University of Regensburg, Regensburg, Germany. 4. Klinik für Allgemein- und Viszeralchirurgie, Krankenhaus Martha-Maria, Nürnberg, Germany. 5. Nuklearmedizinische Klinik, Universitätsklinikum Erlangen, Erlangen, Germany.
Abstract
BACKGROUND: Ultrasound is the first-line imaging modality for detection and classification of thyroid nodules. Certain characteristics observable by ultrasound have recently been identified that may indicate malignancy. This retrospective cohort study was conducted to test the hypothesis that advanced thyroid carcinomas show distinctive clinical and sonographic characteristics. Using a neural network model as proof of concept, nine clinical/sonographic features served as input. METHODS: All 96 study enrollees had histologically confirmed thyroid carcinomas, categorized (n = 32, each) as follows: group 1, advanced carcinoma (ADV) marked by local invasion or distant metastasis; group 2, non-advanced papillary carcinoma (PTC); or group 3, non-advanced follicular carcinoma (FTC). Preoperative ultrasound profiles were obtained via standardized protocols. The neural network had nine input neurons and one hidden layer. RESULTS: Mean age and the number of male patients in group 1 were significantly higher compared with groups 2 (p = 0.005) or 3 (p < 0.001). On ultrasound, tumors of larger volume and irregular shape were observed significantly more often in group 1 compared with groups 2 (p < 0.001) or 3 (p ≤ 0.01). Network accuracy in discriminating advanced vs. non-advanced tumors was 84.4% (95% confidence interval [CI]: 75.5-91), with positive and negative predictive values of 87.1% (95% CI: 70.2-96.4) and 92.3% (95% CI: 83.0-97.5), respectively. CONCLUSIONS: Our study has shown some evidence that advanced thyroid tumors demonstrate distinctive clinical and sonographic characteristics. Further prospective investigations with larger numbers of patients and multicenter design should be carried out to show whether a neural network incorporating these features may be an asset, helping to classify malignancies of the thyroid gland.
BACKGROUND: Ultrasound is the first-line imaging modality for detection and classification of thyroid nodules. Certain characteristics observable by ultrasound have recently been identified that may indicate malignancy. This retrospective cohort study was conducted to test the hypothesis that advanced thyroid carcinomas show distinctive clinical and sonographic characteristics. Using a neural network model as proof of concept, nine clinical/sonographic features served as input. METHODS: All 96 study enrollees had histologically confirmed thyroid carcinomas, categorized (n = 32, each) as follows: group 1, advanced carcinoma (ADV) marked by local invasion or distant metastasis; group 2, non-advanced papillary carcinoma (PTC); or group 3, non-advanced follicular carcinoma (FTC). Preoperative ultrasound profiles were obtained via standardized protocols. The neural network had nine input neurons and one hidden layer. RESULTS: Mean age and the number of male patients in group 1 were significantly higher compared with groups 2 (p = 0.005) or 3 (p < 0.001). On ultrasound, tumors of larger volume and irregular shape were observed significantly more often in group 1 compared with groups 2 (p < 0.001) or 3 (p ≤ 0.01). Network accuracy in discriminating advanced vs. non-advanced tumors was 84.4% (95% confidence interval [CI]: 75.5-91), with positive and negative predictive values of 87.1% (95% CI: 70.2-96.4) and 92.3% (95% CI: 83.0-97.5), respectively. CONCLUSIONS: Our study has shown some evidence that advanced thyroid tumors demonstrate distinctive clinical and sonographic characteristics. Further prospective investigations with larger numbers of patients and multicenter design should be carried out to show whether a neural network incorporating these features may be an asset, helping to classify malignancies of the thyroid gland.