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Related Experiment Video

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Predicting nodal metastases in papillary thyroid carcinoma using artificial intelligence.

Antoinette R Esce1, Jordan P Redemann2, Andrew C Sanchez2

  • 1Department of Surgery, Division of Otolaryngology Head and Neck Surgery, MSC10 5610, University of New Mexico, Albuquerque, NM, 87131, USA.

American Journal of Surgery
|May 25, 2021
PubMed
Summary

A convolutional neural network (CNN) can predict lymph node metastases in papillary thyroid carcinoma (PTC) using primary tumor images. This artificial intelligence (AI) approach shows high accuracy in identifying nodal involvement.

Keywords:
Artificial intelligenceHistopathologyPapillary thyroid carcinoma

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Nodal metastases are critical in papillary thyroid carcinoma (PTC) management.
  • Predicting lymph node involvement aids treatment decisions.
  • Current methods may be invasive or time-consuming.

Purpose of the Study:

  • To evaluate a convolutional neural network (CNN) for predicting nodal metastases in PTC.
  • To assess the feasibility of using primary tumor histopathology for metastasis prediction.
  • To develop an AI tool for improved PTC staging.

Main Methods:

  • A dataset of 174 PTC cases was analyzed.
  • Histopathology images of primary tumors were used for analysis.
  • An artificial intelligence (AI) algorithm, specifically a CNN, was trained and validated.

Main Results:

  • The best-performing CNN achieved 94% sensitivity for detecting nodal metastases.
  • The algorithm demonstrated 100% specificity in identifying nodal metastases.
  • Visual histopathology data from the primary tumor alone was sufficient for prediction.

Conclusions:

  • A CNN can accurately predict the likelihood of nodal metastases in PTC.
  • AI analysis of primary tumor histopathology offers a non-invasive prediction method.
  • This approach has potential to improve PTC patient management and staging.