## 5 Conclusions  
A fast diagnosis method for thyroid nodules was developed with the PESI-MS based machine learning and fine needle biopsy samples. In total, 267 patients contributed to the development of the machine learning method. The inhomogeneity of ultrasound guided-fine needle aspiration biopsy (FNAB) samples had a negative effect on the malignancy determination. Fortunately, it can be overcome with 3630 new features for classifier training, which were generated from 163 ions by a pairwise-coupling method. _m_/z 285, _m_/z 833.5, and _m_/z 699.4 could be good internal standards to eliminate the inhomogeneity of the FNAB samples. For the principle components, PLS is better than PCA and TSNE to separate malignant and benign samples. For the classifier training, MLP is better than SVM and RF, considering both prediction accuracy and cross-validation. The single-blind test with 17 patients had an accuracy of 82.4%, which proved the good generalization ability of the MLP classifier. The malignancy prediction of the undetermined patients had a concordance of 73.0% between prediction and 6-month follow-up. The classifier showed high accuracy for the undermined patients with suspicious for papillary carcinoma diagnosis (90.9%). Although the low prediction accuracy is not satisfying for patients with undetermined significance and an insufficient cellularity diagnosis in undetermined group, the improvement of FNAB or the development of a new classifier would be effective ways to improve the prediction accuracy. In addition, for each FNAB sample, only 10 min is needed to determine its malignancy, which is much easier and faster than traditional cytopathology and BRAF gene diagnosis. In summary, a promising auxiliary diagnosis technique was developed for the malignancy determination of thyroid nodules.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12115364/s1](https://www.mdpi.com/article/10.3390/app12115364/s1)]([https://www.mdpi.com/article/10.3390/app12115364/s1](https://www.mdpi.com/article/10.3390/app12115364/s1)), the detailed cytopathology (Method S1.1) and the BRAF V600E gene (Method S1.2) detection procedures; Table S1 single-blind test, and Table S2 the MLP prediction of the undetermined patients.  
Conceptualization, D.Z., X.L. and L.C.; methodology, Z.C., Y.W. and L.Z.; software, Z.C. and L.Z.; validation, Z.C., Y.W. and Y.L. ([PERSON]); formal analysis, Z.C., Y.W. and Y.L. ([PERSON]); investigation, Z.C. and Y.W.; resources, Y.W., D.Z., J.L. and J.D.; data curation, R.J., Y.L. ([PERSON]) and Y.W.; writing--original draft preparation, Z.C. and Y.W.; writing--review and editing, D.Z., X.L. and L.C.; visualization, Z.C.; supervision, D.Z. and X.L.; project administration, Y.L. ([PERSON]); and funding acquisition, D.Z., Y.L. ([PERSON]) and X.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the China-Japan Friendship Hospital (2019-103-K71 and 8 August 2019).  
Informed Consent Statement: Informed consent was obtained from all the subjects involved in the study.  
The data that support the findings of this study are available upon reasonable request.  
The authors declare no conflict of interest.