Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Randolph GW

PROVIDER: S-EPMC9526471 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Publications

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

Randolph Gregory W GW Sosa Julie Ann JA Hao Yangyang Y Angell Trevor E TE Shonka David C DC LiVolsi Virginia A VA Ladenson Paul W PW Blevins Thomas C TC Duh Quan-Yang QY Ghossein Ronald R Harrell Mack M Patel Kepal Narendra KN Shanik Michael H MH Traweek S Thomas ST Walsh P Sean PS Yeh Michael W MW Abdelhamid Ahmed Amr H AH Ho Allen S AS Wong Richard J RJ Klopper Joshua P JP Huang Jing J Kennedy Giulia C GC Kloos Richard T RT Sadow Peter M PM

Thyroid : official journal of the American Thyroid Association 20220808 9

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 perform ...[more]

PMID: 35793115

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Project description:BackgroundRadiomics strategies exhibit great promise in the context of thyroid nodule diagnosis. This study aimed to compare radiomics features of different sizes of medullary thyroid carcinoma (MTC) and papillary thyroid carcinoma (PTC) tumors and to compare the efficiency of radiomics approaches as a means of differentiating between these tumor types.MethodsIn total, 86 MTC and 330 PTC nodules were divided into the macronodular (>10 mm) and micronodular (⩽10 mm) categories. The radiomics features of these nodules were analyzed to identify independent prognosis factors and evaluate the efficacy of individual and combined indicators as predictors of tumor type.ResultsIn total, 12 radiomics features were found to differ significantly between MTC and PTC macronodules, while 6 differed significantly between MTC and PTC micronodules. Shape 2D_Sphericity, firstorder_Skewness, glrlm_RunLengthNonUniformity, glszm_GrayLevelNonUniformity, and glszm_SizeZoneNonUniformity were features that were independently associated with the differential diagnoses of MTC and PTC macronodules. Receiver operating characteristic (ROC) curve analyses of the efficacy of these 5 single indicators and a combined indicator composed thereof yielded area under the curve (AUC) values of 0.621, 0.678, 0.704, 0.762, 0.747, and 0.824, respectively, with respective sensitivities of 55.3%, 43.0%, 53.1%, 56.3%, 46.9%, and 65.6%, and respective specificity values of 65.6%, 89.1%, 81.6%, 88.8%, 95.0%, and 91.1%. The glrlm_RunEntropy and glszm_SizeZoneNonUniformity features were identified as independent factors associated with the differential diagnoses of MTC and PTC micronodules. Receiver operating characteristic curve analyses of the efficacy of these 2 single indicators and a combined indicator composed thereof yielded respective AUC values of 0.678, 0.678, and 0.771; Sensitivities of 57.0%, 72.7%, and 72.7%; and specificities of 77.3%, 64.2%, and 77.5%.ConclusionsA range of different radiomics features can enable effective differentiation between MTC and PTC nodules of different sizes. Moreover, analyses of combinations of radiomics features yielded diagnostic efficiency values higher than those associated with single radiomics features, highlighting a more reliable approach to diagnosing MTC and PTC tumors.

Project description:Background: With the incidence of papillary thyroid carcinoma rising worldwide, the decision about lobectomy versus total thyroidectomy will become relevant for an increasing number of patients. There exists no reliable biomarker for metastatic potential or tendency of recurrence that could assist in the risk stratification of patients. Objective: To develop a gene expression classifier for metastatic potential by measuring RNA expression in the primary tumor at the time of cancer surgery. Further, to investigate the ability of the gene expression classifier to identify metastatic and recurrent cases. Method: Genome-wide expression analyses. The development cohort consisted of freshly frozen tissues from 38 patients collected between the years 1986 and 2009. Validation was performed on a consecutive cohort of formalin fixated paraffin embedded surgical tissue specimens from 183 patients treated at Odense and Aarhus University Hospitals. Results: A 17 gene classifier (ADAMTS1, ANTXR1, C7, CXCL12, EBF1, FBLN2, FOSL2, GGT5, GPR124, JAM3, LRIG1, NDRG1, PRRX1, ROBO1, SORL1, TCF4, and ZEB1) was identified based on the expression values of these genes in the groups with and without metastasis in the development cohort. The 17 gene classifier for regional and/or distant metastasis identified was tested against the clinical status in the validation cohort. Sensitivity was 51.6% (95% CI 41.2%-61.8) and specificity 61.6 % (95% CI 50.5%-71.9%). Further, the Kaplan-Meyer method was used to estimate whether the classifier was useful as a prognostic marker for recurrences. Log-rank testing failed to identify any significance (p=0.32). Conclusion: A 17 gene classifier for metastatic potential was developed, and the results showed a clear biological difference between groups. However, through validation, the prognostic significance of this classifier could not be shown in identifying metastatic cases or in the ability of dichotomizing patients according to risk of recurrence after primary treatment.

Project description:Background: Molecular testing (MT) refines risk stratification for thyroid nodules that are indeterminate for cancer by fine needle aspiration (FNA) cytology. Criteria for selecting nodules for MT vary and remain largely untested, raising questions about the best strategy for maximizing the usefulness of MT while minimizing the harms of overtesting. We used a unique data set to examine the effects of repeat FNA cytology-based criteria for MT on management decisions and nodule outcomes. Methods: This was a study of adults (age 25-90 years; 281 women and 72 men) with cytologically indeterminate (Bethesda III/IV) thyroid nodules who underwent repeat FNA biopsy and Afirma Gene Expression Classifier (GEC) testing (N = 363 nodules from 353 patients) between June 2013 and October 2017 at a single institution, with follow-up data collected until December 2019. Subgroup analysis was performed based on classification of repeat FNA cytology. Outcomes of GEC testing, clinical/sonographic surveillance of unresected nodules, and histopathologic diagnoses of thyroidectomies were compared between three testing approaches: (i) Reflex (MT sent on the basis of the initial Bethesda III/IV FNA), (ii) SemiRestrictive (MT sent if repeat FNA is Bethesda I-IV), and (iii) Restrictive (MT sent only if repeat FNA is Bethesda III/IV) testing approaches. Results: Restricting MT to nodules that remain Bethesda III/IV on repeat FNA would have missed 4 low-risk cancers and 3 noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) (collectively 2% of the test population) but would have avoided diagnostic surgery for 42 benign nodules (12% of the test population). The Restrictive testing strategy was more specific (delta 0.126 confidence interval [CI 0.093 to 0.159] and 0.129 [CI 0.097 to 0.161], respectively) but less sensitive (delta -0.339 [CI -0.424 to -0.253] and -0.340 [CI -0.425 to -0.255], respectively) than the Reflex and SemiRestrictive approaches for detecting NIFTP or cancer. Conclusions: Repeat FNA cytology can guide the selection of cytologically indeterminate thyroid nodules that warrant MT. The Restrictive model of performing Afirma GEC only on nodules with two separate biopsies showing Bethesda III/IV cytology would reduce the rate of diagnostic surgery for histologically benign nodules while missing only rare low-risk tumors. Given the low but nontrivial risks of thyroidectomy, the higher specificity of the Restrictive testing approach disproportionately outweighs the potential harms.

Dataset Information

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

Publications

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

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