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Preoperative Identification of Medullary Thyroid Carcinoma (MTC): Clinical Validation of the Afirma MTC RNA-Sequencing Classifier.

Gregory W RandolphJulie Ann SosaYangyang HaoTrevor E AngellDavid C ShonkaVirginia A LiVolsiPaul W LadensonThomas C BlevinsQuan-Yang DuhRonald GhosseinMack HarrellKepal Narendra PatelMichael H ShanikS Thomas TraweekP Sean WalshMichael W YehAmr H Abdelhamid AhmedAllen S HoRichard J WongJoshua P KlopperJing HuangGiulia C KennedyRichard T KloosPeter M Sadow
Published in: Thyroid : official journal of the American Thyroid Association (2022)
Background: Cytopathological evaluation of thyroid fine-needle aspiration biopsy (FNAB) specimens can fail to raise preoperative suspicion of medullary thyroid carcinoma (MTC). The Afirma RNA-sequencing MTC classifier identifies MTC among FNA samples that are cytologically indeterminate, suspicious, or malignant (Bethesda categories III-VI). In this study we report the development and clinical performance of this MTC classifier. Methods: Algorithm training was performed with a set of 483 FNAB specimens (21 MTC and 462 non-MTC). A support vector machine classifier was developed using 108 differentially expressed genes, which includes the 5 genes in the prior Afirma microarray-based MTC cassette. Results: The final MTC classifier was blindly tested on 211 preoperative FNAB specimens with subsequent surgical pathology, including 21 MTC and 190 non-MTC specimens from benign and malignant thyroid nodules independent from those used in training. The classifier had 100% sensitivity (21/21 MTC FNAB specimens correctly called positive; 95% confidence interval [CI] = 83.9-100%) and 100% specificity (190/190 non-MTC FNAs correctly called negative; CI = 98.1-100%). All positive samples had pathological confirmation of MTC, while all negative samples were negative for MTC on surgical pathology. Conclusions: The RNA-sequencing MTC classifier accurately identified MTC from preoperative thyroid nodule FNAB specimens in an independent validation cohort. This identification may facilitate an MTC-specific preoperative evaluation and resulting treatment.
Keyphrases
  • fine needle aspiration
  • ultrasound guided
  • patients undergoing
  • machine learning
  • deep learning
  • transcription factor
  • bioinformatics analysis
  • combination therapy