Open AccessHighly accurate diagnosis of papillary thyroid carcinomas based on personalized pathways coupled with machine learning
Author(s) -
Kyoung Sik Park,
Seong Hoon Kim,
Jung Hun Oh,
Sung Young Kim
Publication year - 2020
Publication title -
briefings in bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.204
H-Index - 113
eISSN - 1477-4054
pISSN - 1467-5463
DOI - 10.1093/bib/bbaa336
Subject(s) - medicine , brier score , cohort , malignancy , thyroid carcinoma , thyroid cancer , oncology , thyroid nodules , receiver operating characteristic , machine learning , generalizability theory , papillary thyroid cancer , artificial intelligence , thyroid , computer science , psychology , developmental psychology
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.