Automated diagnosis and management of follicular thyroid nodules based on the devised small-datasets interpretable foreground optimization network deep learning: A multicenter diagnostic study

Abstract

Background: Currently, follicular thyroid carcinoma (FTC) has a relatively low incidence with a lack of effective preoperative diagnostic means. To reduce the need for invasive diagnostic procedures and to address information deficiencies inherent in a small dataset, we utilized interpretable foreground optimization network deep learning to develop a reliable preoperative FTC detection system. Methods: In this study, a deep learning model (FThyNet) was established using preoperative ultrasound images. Data on patients in the training and internal validation cohort (n=432) were obtained from Ruijin Hospital, China. Data on patients in the external validation cohort (n=71) were obtained from four other clinical centers. We evaluated the predictive performance of FThyNet and its ability to generalize across multiple external centers and compared the results yielded with assessments from physicians directly predicting FTC outcomes. In addition, the influence of texture information around the nodule edge on the prediction results was evaluated. Results: FThyNet had a consistently high accuracy in predicting FTC with an area under the receiver operating characteristic curve (AUC) of 89.0% [95% CI 87.0–90.9]. Particularly, the AUC for grossly invasive FTC reached 90.3%, which was significantly higher than that of the radiologists (56.1% [95% CI 51.8–60.3]). The parametric visualization study found that those nodules with blurred edges and relatively distorted surrounding textures were more likely to have FTC. Furthermore, edge texture information played an important role in FTC prediction with an AUC of 68.3% [95% CI 61.5–75.5], and highly invasive malignancies had the highest texture complexity. Conclusion: FThyNet could effectively predict FTC, provide explanations consistent with pathological knowledge, and improve clinical understanding of the disease.

Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review

“…Relative to thyroid disease diagnosis, ultrasound (US) is widely acknowledged as the primary diagnostic technique for examining thyroid nodules and assessing papillary thyroid carcinomas (PTCs) before surgery [34]. DL networks with excellent diagnostic efficiency have been deployed to distinguish between benign nodules and thyroid carcinoma [35], improve the detection of follicular carcinoma, differentiate between atypical and typical medullary carcinoma [36], and assess for gross extrathyroidal extension in thyroid cancer [37]. AI systems can be very useful in eliminating the operator dependence of US and ameliorating diagnosis precision, especially in inexperienced radiologists.…”

Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population

“…Given sample size imbalance, AI models might be subject to unfairness by omitting features of the less-prevalent subgroups. This may lead to misdiagnosis that negatively impacts the health outcomes of patients from these groups 17 – 19 . Current medical AI research, however, doesn’t provide ample quantitative evaluations of these biases.…”