Protein Classifier for Thyroid Nodules Learned from Rapidly Acquired Proteotypes
Sun, Y.; Selvarajan, S.; Zang, Z.; Liu, W.; Zhu, Y. J.; Zhang, H.; Chen, H.; Cai, X.; Gao, H.; Wu, Z.; Chen, L.; Teng, X.; Zhao, Y.; Mantoo, S.; Lim, T. K.-H.; Hariraman, B.; Yeow, S.; Syed Abdillah, S. M. F.; Lee, S. S.; Ruan, G.; Zhang, Q.; Zhu, T.; Wang, W.; Wang, G.; Xiao, J.; He, Y.; Wang, Z.; Sun, W.; Qin, Y.; Xiao, Q.; Zheng, X.; Wang, L.; Zheng, X.; Xu, K.; Shao, Y.; Liu, K.; Zheng, S.; Li, S. Z.; Kon, O. L.; Iyer, N. G.; Guo, T.
Show abstract
Up 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.
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