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Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
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Predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma using deep learning.

Yu Wang1, Hai-Long Tan1, Sai-Li Duan1

  • 1Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.

Peerj
|April 2, 2024
PubMed
Summary
This summary is machine-generated.

This study developed a deep learning model using ultrasound images to predict central lymph node metastasis in papillary thyroid microcarcinoma patients. The image-based model showed slight improvement over clinical factors alone.

Keywords:
Central lymph node metastasesDeep learningPapillary thyroid microcarcinomaUltrasound image

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Papillary thyroid microcarcinoma (PTMC) is a common thyroid cancer.
  • Central lymph node metastasis (CLNM) is a key factor in PTMC management.
  • Accurate preoperative prediction of CLNM is crucial for treatment planning.

Purpose of the Study:

  • To design a deep learning (DL) model for preoperative prediction of CLNM in PTMC patients.
  • To evaluate the efficacy of DL models using ultrasound images and clinical factors.
  • To identify key clinical factors associated with CLNM in PTMC.

Main Methods:

  • Collected preoperative ultrasound (US) images and clinical data from 611 PTMC patients.
  • Utilized multivariate regression for clinical factor analysis.
  • Developed and evaluated DL models based on US images, clinical factors, and combined data.

Main Results:

  • Multivariate analysis identified age ≥55, tumor diameter, macrocalcifications, and capsular invasion as independent predictors of CLNM.
  • The DL model using US images achieved an AUC of 0.65, outperforming a model with clinical factors (AUC = 0.64).
  • A combined model of US images and clinical factors showed a slightly lower AUC (0.63).

Conclusions:

  • The DL model utilizing US images provides a valuable tool for preoperative CLNM prediction in PTMC.
  • The model's reliance on imaging data suggests its potential for improving diagnostic accuracy.
  • This approach can aid in treatment decisions and patient management for PTMC.