Protein Classifier for Thyroid Nodules Learned from Rapidly Acquired Proteotypes
Author:
Sun YaotingORCID, Selvarajan Sathiyamoorthy, Zang Zelin, Liu Wei, Zhu YiORCID, Zhang Hao, Chen Hao, Cai Xue, Gao Huanhuan, Wu Zhicheng, Chen Lirong, Teng Xiaodong, Zhao Yongfu, Mantoo Sangeeta, Lim Tony Kiat-Hon, Hariraman Bhuvaneswari, Yeow Serene, bin Syed Abdillah Syed Muhammad Fahmy, Lee Sze Sing, Ruan Guan, Zhang Qiushi, Zhu Tiansheng, Wang Weibin, Wang Guangzhi, Xiao Junhong, He Yi, Wang Zhihong, Sun Wei, Qin Yuan, Xiao Qi, Zheng Xu, Wang Linyan, Zheng Xi, Xu Kailun, Shao Yingkuan, Liu Kexin, Zheng Shu, Aebersold Ruedi, Li Stan Z., Kon Oi Lian, Iyer N. Gopalakrishna, Guo TiannanORCID
Abstract
SUMMARYUp to 30% of thyroid nodules cannot be accurately classified as benign or malignant by cytopathology. Diagnostic accuracy can be improved by nucleic acid-based testing, yet a sizeable number of diagnostic thyroidectomies remains unavoidable. In order to develop a protein classifier for thyroid nodules, we analyzed the quantitative proteomes of 1,725 retrospective thyroid tissue samples from 578 patients using pressure-cycling technology and data-independent acquisition mass spectrometry. With artificial neural networks, a classifier of 14 proteins achieved over 93% accuracy in classifying malignant thyroid nodules. This classifier was validated in retrospective samples of 271 patients (91% accuracy), and prospective samples of 62 patients (88% accuracy) from four independent centers. These rapidly acquired proteotypes and artificial neural networks supported the establishment of an effective protein classifier for classifying thyroid nodules.
Publisher
Cold Spring Harbor Laboratory
Reference45 articles.
1. Integrated Genomic Characterization of Papillary Thyroid Carcinoma 2. Korea's Thyroid-Cancer “Epidemic” — Screening and Overdiagnosis 3. Preoperative Diagnosis of Benign Thyroid Nodules with Indeterminate Cytology 4. Bartolazzi, A. , Sciacchitano, S. , and D’Alessandria, C. (2018). Galectin-3: The Impact on the Clinical Management of Patients with Thyroid Nodules and Future Perspectives. International journal of molecular sciences 19. 5. Becht, E. , McInnes, L. , Healy, J. , Dutertre, C. , Kwok, I. , Ng, L. , Ginhoux, F. , and Newell, E. (2018). Dimensionality reduction for visualizing single-cell data using UMAP. Nature biotechnology.
Cited by
10 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|