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Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis.

Xin Li1, Wen-Yu Yang2, Fan Zhang3

  • 1Department of General Surgery, Peking University Third Hospital, Beijing, China.

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|July 11, 2025
PubMed
Summary

Predicting follicular thyroid neoplasm (FTN) malignancy risk is challenging. This study identified size-specific predictors, including calcification and nodule appearance, to aid preoperative diagnosis of FTNs.

Keywords:
XGBoostfollicular thyroid adenomafollicular thyroid cancerfollicular thyroid neoplasmmachine learningmalignancyrandom foresttumor size

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Area of Science:

  • Endocrinology
  • Oncology
  • Radiology

Background:

  • Distinguishing benign from malignant follicular thyroid neoplasms (FTNs) poses a surgical challenge, especially for small tumors.
  • Preoperative risk stratification for FTNs is crucial for surgical planning and patient management.

Purpose of the Study:

  • To identify preoperative predictors for malignancy risk in follicular thyroid neoplasms (FTNs).
  • To investigate size-specific predictors for malignancy risk in FTNs, differentiating between small and large tumors.

Main Methods:

  • A retrospective cohort study included 1494 patients with follicular thyroid adenoma (FTA) or carcinoma (FTC).
  • Follicular thyroid neoplasms (FTNs) were categorized as small (<3.0 cm) or large (≥3.0 cm) based on diameter.
  • Machine learning identified key predictors from demographic, sonographic, and hormonal variables, with odds ratios calculated for malignancy risk.

Main Results:

  • Small FTNs with macrocalcification, peripheral calcification, or in younger patients had higher malignancy risk.
  • Large FTNs with a nodule-in-nodule appearance showed increased malignancy risk.
  • Lower thyroid-stimulating hormone levels and larger mean diameter were associated with malignancy risk in both small and large FTNs.

Conclusions:

  • Size-specific predictors for FTN malignancy risk were identified.
  • Stratified prediction based on tumor size is essential for improving the accuracy of preoperative diagnosis of follicular thyroid neoplasms.