Highly accurate diagnosis of papillary thyroid carcinomas based on personalized pathways coupled with machine learning

• Published in: Briefings in bioinformatics Vol. 22; no. 4
• Main Authors: Park, Kyoung Sik, Kim, Seong Hoon, Oh, Jung Hun, Kim, Sung Young
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.07.2021; Oxford Publishing Limited (England)
• Subjects: Algorithms; Cancer; Case Study; Confidence intervals; Customization; Diagnostic systems; Learning algorithms; Machine learning; Malignancy; Neoplasms; Nodules; Nuclear accidents; Nuclear power plants; Papillary thyroid carcinoma; Radiation effects; Radioactive fallout; Thyroid; Thyroid cancer; papillary thyroid carcinomas; machine learning; tall cell variants; molecular diagnosis

Abstract Thyroid nodules are neoplasms commonly found among adults, with papillary thyroid carcinoma (PTC) being the most prevalent malignancy. However, current diagnostic methods often subject patients to unnecessary surgical burden. In this study, we developed and validated an automated, highly ac...