Salivary biomarkers-assisted ultrasound-based differentiation of malignant and benign thyroid nodules

An Improved k-Nearest Neighbor Algorithm for Recognition and Classification of Thyroid Nodules

OBJECTIVES

To complete the task of automatic recognition and classification of thyroid nodules and solve the problem of high classification error rates when the samples are imbalanced.

METHODS

An improved k-nearest neighbor (KNN) algorithm is proposed and a method for automatic thyroid nodule classification based on the improved KNN algorithm is established. In the improved KNN algorithm, we consider not only the number of class labels for various classes of data in KNNs, but also the corresponding weights. And we use the Minkowski distance measure instead of the Euclidean distance measure.

RESULTS

A total of 508 ultrasound images of thyroid nodules, including 415 benign nodules and 93 malignant nodules, were used in the paper. Experimental results show the improved KNN has 0.872549 accuracy, 0.867347 precision, 1 recall, and 0.928962 F1-score. At the same time, we also considered the influence of different distance weights, the value of k, different distance measures on the classification results.

CONCLUSIONS

A comparison result shows that our method has a better performance than the traditional KNN and other classical machine learning methods.

[Latest Research Findings on Health Management Based on Saliva Testing]

Being one of the most important exocrine fluids of the human body, saliva can reflect the health status of the body. Soliva collection has various advantages--it is simple, painless, fast, and safe, and soliva can be collected multiple times a day. Therefore, it has been gradually applied in the exploration for biomarkers for disease detection, providing a basis for the monitoring of the course of diseases, medication monitoring, and efficacy evaluation. We should implement health management based on saliva testing, collect the medical data of the healthy and diseased individuals and monitor their whole life cycle health, perform clinical cohort study, aggregate the data on platforms for big data on health and medicine, manage and provide guidance for the health status of populations, pinpoint the high-risk factors for pathogenesis, and provide effective intervention, early warning, and assessment of the vital signs of individuals, thereby reinforcing health management of the whole life cycle and safeguarding people's health in an all-round way. In addition, it drives the development of the health industry and bears strategic significance for the promotion of national economic development. It is becoming a hot research topic promising great potential and impressive applicational prospects. Herein, we reviewed new techniques for clinical saliva testing and health management based on saliva testing.

Machine learning reveals salivary glycopatterns as potential biomarkers for the diagnosis and prognosis of papillary thyroid cancer

The diagnosis of thyroid cancer, especially papillary thyroid cancer (PTC), is increasing rapidly worldwide. In this study, we aimed to study the glycosylation of salivary proteins associated with PTC and assess the likelihood that salivary glycopatterns may be a potential biomarker of PTC diagnosis. Firstly, 22 benign thyroid nodule (BTN) samples, 27 PTC samples, and 30 healthy volunteers (HV) samples were collected to probe the difference of salivary glycopatterns associated with PTC using lectin microarrays. Then, five machine learning models including K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) were established to distinguish HV, BTN and PTC based on the changes of salivary glycopatterns. As a result, SVM had the best diagnostic effect with an accuracy rate of 92 % in testing set. Besides, lectin microarrays were used to explore the differences in salivary glycopatterns of 26 paired salivary samples of PTC patients before and after operation in order to probe into salivary glycopatterns as potential biomarkers for prognosis of PTC patients. The results showed that the levels of salivary glycopatterns recognized by 6 different lectins in patients after the operation almost convergenced with HVs. This study could help to screen and assess patients with PTC and their prognosis based on precise changes of salivary glycopatterns.