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Related Experiment Video

Updated: Nov 30, 2025

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
05:41

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

904

Diagnosis of thyroid neoplasm using support vector machine algorithms based on platelet RNA-seq.

Yuling Shen1, Yi Lai1, Dong Xu1

  • 1Department of Head and Neck Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, 160 Pujian Road, Pudong District, Shanghai, 200127, China.

Endocrine
|November 12, 2020
PubMed
Summary

Support vector machine (SVM) algorithms using platelet RNA sequencing data can accurately identify thyroid cancer patients. These algorithms also show promise in classifying different types of thyroid neoplasms.

Keywords:
Bioinformatics analysisPlatelet RNA-seqSVM algorithmThyroid neoplasm

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Oncology

Background:

  • Thyroid neoplasms require accurate diagnostic methods.
  • Platelet gene expression may offer novel biomarkers for cancer detection.

Purpose of the Study:

  • To evaluate the efficacy of support vector machine (SVM) algorithms based on platelet RNA sequencing data for thyroid neoplasm identification.
  • To differentiate between thyroid adenomas, papillary thyroid cancer, and metastasized papillary thyroid cancer using these algorithms.

Main Methods:

  • Platelet RNA sequencing (RNA-seq) was performed on 109 patients and 63 healthy controls.
  • Two SVM algorithms were developed and validated using differentially expressed genes (GDTLs) from the RNA-seq data.
  • Gene Ontology (GO) and KEGG pathway enrichment analyses were conducted.

Main Results:

  • The algorithm for identifying thyroid neoplasm patients achieved 97% accuracy and an AUC of 0.998.
  • The multiclass classification algorithm for thyroid neoplasms reached an average accuracy of 80.5%.
  • GDTLs were implicated in neutrophil degranulation, autophagy, and signaling pathways like NOD-like receptor signaling.

Conclusions:

  • SVM algorithms combined with platelet RNA-seq data are effective for thyroid neoplasm diagnostics.
  • This approach enables the classification of various thyroid neoplasm subtypes.