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Model development to predict central lymph node metastasis in cN0 papillary thyroid microcarcinoma by machine

Yaocheng Yu1, Zhiwei Yu1, Mengxuan Li1

  • 1Department of Thyroid, Breast and Vascular Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.

Annals of Translational Medicine
|September 16, 2022
PubMed
Summary

Machine learning models can predict central lymph node metastasis (CLNM) in papillary thyroid microcarcinoma (PTMC) patients. An online risk calculator using the random forest model aids surgical decision-making for cN0 PTMC.

Keywords:
Thyroid cancercentral lymph node metastasis (CLNM)machine learning (ML)microcarcinomamodel

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Area of Science:

  • Oncology
  • Surgical Decision Support
  • Machine Learning in Medicine

Background:

  • Prophylactic central lymph node dissection (CLND) necessity in cN0 papillary thyroid microcarcinoma (PTMC) is debated.
  • Surgeons require tools for informed surgical decision-making in PTMC management.
  • Machine learning (ML) models offer potential for improved predictive performance over traditional methods.

Purpose of the Study:

  • To develop and validate ML models for predicting central lymph node metastasis (CLNM) risk in cN0 PTMC patients.
  • To identify key predictors of CLNM in this patient cohort.
  • To create a user-friendly tool for assessing individual CLNM probability.

Main Methods:

  • Retrospective analysis of 1,121 cN0 PTMC patients' clinical records (2014-2018).
  • Univariate and multivariate analyses to identify CLNM risk factors.
  • Development and internal validation of six ML algorithms for CLNM prediction, including random forest (RF).

Main Results:

  • 33.5% of patients exhibited CLNM.
  • Independent predictors of CLNM included gender, age, tumor size, multifocal lesions, and extrathyroidal extension (ETE).
  • The RF model demonstrated the highest predictive performance with an AUROC of 0.794.

Conclusions:

  • ML algorithms are feasible for developing predictive models of CLNM in cN0 PTMC.
  • An online risk calculator based on the RF model can assist surgeons in surgical decision-making.
  • This tool provides a valuable resource for personalized PTMC management.