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Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
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Age-stratified deep learning model for thyroid tumor classification: a multicenter diagnostic study.

Weijie Zou1,2,3,4, Yahan Zhou2,5, Jincao Yao1,2,3,4

  • 1Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou, China.

European Radiology
|February 4, 2025
PubMed
Summary
This summary is machine-generated.

An age-stratified deep learning model (ASMCNet) significantly improved thyroid nodule classification accuracy compared to non-stratified models and radiologists. This AI tool enhances diagnostic performance, aiding in reduced unnecessary biopsies for thyroid cancer.

Keywords:
AgeArtificial intelligenceDeep learningThyroid cancerUltrasonography

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Oncology Diagnostics

Background:

  • Thyroid cancer incidence is rising, with age being a key survival predictor.
  • Current diagnostic methods may lead to overdiagnosis and overtreatment due to low mortality rates.
  • The diagnostic impact of age in thyroid nodule classification requires further investigation.

Purpose of the Study:

  • To develop an age-stratified deep learning (DL) model, ASMCNet, for thyroid nodule classification.
  • To evaluate the impact of age stratification on DL model accuracy.
  • To explore ASMCNet's potential to enhance radiologists' diagnostic performance and reduce unnecessary biopsies.

Main Methods:

  • Retrospective analysis of 10,391 ultrasound images from 5934 patients across three hospitals.
  • Development and validation of an age-stratified deep learning model (ASMCNet).
  • Comparison of ASMCNet's performance against non-age-stratified models and radiologists using the DeLong test.

Main Results:

  • ASMCNet achieved an AUROC of 0.906, sensitivity of 86.1%, and specificity of 85.1%.
  • ASMCNet significantly outperformed non-age-stratified models (AUROC 0.867) and all radiologists.
  • Radiologist performance improved with AI assistance, particularly using explaining heatmaps.

Conclusions:

  • Age stratification in DL models significantly enhances thyroid tumor classification accuracy.
  • ASMCNet demonstrates clinical applicability, assisting radiologists in improving diagnostic accuracy.
  • The age-stratified approach is crucial for accurate thyroid nodule diagnosis, potentially reducing overtreatment.