Annotation-free genetic mutation estimation of thyroid cancer using cytological slides from multi-centers

Diagn Pathol. 2025 Feb 21;20(1):22. doi: 10.1186/s13000-025-01618-1.

Abstract

Thyroid cancer is the most common form of endocrine malignancy and fine needle aspiration (FNA) cytology is a reliable method for clinical diagnosis. Identification of genetic mutation status has been proved efficient for accurate diagnosis and prognostic risk stratification. In this study, a dataset with thyroid cytological images of 310 indeterminate (TBS3 or 4) and 392 PTC (TBS5 or 6) was collected. We introduced a multimodal cascaded network framework to estimate BARF V600E and RAS mutations directly from thyroid cytological slides. The area under the curve in the external testing set achieved 0.902 ± 0.063 and 0.801 ± 0.137 AUCs for BRAF, and RAS, respectively. The results demonstrated that deep neural networks have the potential in cytologically predicting valuable diagnosis and comprehensive genetic status.

Keywords: Annotation-free; Deep convolutional network; Fine needle aspiration cytology; Gene mutation estimation; Thyroid cancer.

MeSH terms

  • Adult
  • Biomarkers, Tumor / genetics
  • Biopsy, Fine-Needle
  • DNA Mutational Analysis / methods
  • Female
  • Humans
  • Male
  • Middle Aged
  • Mutation*
  • Neural Networks, Computer
  • Proto-Oncogene Proteins B-raf* / genetics
  • Proto-Oncogene Proteins p21(ras) / genetics
  • Thyroid Cancer, Papillary* / diagnosis
  • Thyroid Cancer, Papillary* / genetics
  • Thyroid Cancer, Papillary* / pathology
  • Thyroid Neoplasms* / diagnosis
  • Thyroid Neoplasms* / genetics
  • Thyroid Neoplasms* / pathology

Substances

  • Proto-Oncogene Proteins B-raf
  • BRAF protein, human
  • Proto-Oncogene Proteins p21(ras)
  • Biomarkers, Tumor