한빛사 논문
Kyoung Sik Park, Seong Hoon Kim, Jung Hun Oh and Sung Young Kim*
Kyoung Sik Park, MD, PhD, is a professor at Konkuk University School of Medicine, South Korea.
Seong Hoon Kim, MD, is a staff surgeon at Konkuk University School of Medicine, South Korea.
Jung Hun Oh, PhD, is an attending physicist in the Department of Medical Physics at Memorial Sloan Kettering Cancer Center, USA.
Sung Young Kim, MD, PhD, is a professor at Konkuk University School of Medicine, South Korea.
*Corresponding author: Sung Young Kim, Department of Biochemistry, Konkuk University School of Medicine, Seoul 143-701, Republic of Korea,
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 accurate multi-study-derived diagnostic model for PTCs using personalized biological pathways coupled with a sophisticated machine learning algorithm. Surprisingly, the algorithm achieved near-perfect performance in discriminating PTCs from non-tumoral thyroid samples with an overall cross-study-validated area under the receiver operating characteristic curve (AUROC) of 0.999 (95% confidence interval [CI]: 0.995–1) and a Brier score of 0.013 on three independent development cohorts. In addition, the algorithm showed excellent generalizability and transferability on two large-scale external blind PTC cohorts consisting of The Cancer Genome Atlas (TCGA), which is the largest genomic PTC cohort studied to date, and the post-Chernobyl cohort, which includes PTCs reported after exposure to radiation from the Chernobyl accident. When applied to the TCGA cohort, the model yielded an AUROC of 0.969 (95% CI: 0.950–0.987) and a Brier score of 0.109. On the post-Chernobyl cohort, it yielded an AUROC of 0.962 (95% CI: 0.918–1) and a Brier score of 0.073. This algorithm also is robust against other various types of clinical scenarios, discriminating malignant from benign lesions as well as clinically aggressive thyroid cancer with poor prognosis from indolent ones. Furthermore, we discovered novel pathway alterations and prognostic signatures for PTC, which can provide directions for follow-up studies.
Key words: papillary thyroid carcinomas; tall cell variants; molecular diagnosis; machine learning
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