Dataset Information

Screening adequacy of unstained thyroid fine needle aspiration samples using a deep learning-based classifier.

ABSTRACT: Fine needle aspiration (FNA) biopsy of thyroid nodules is a safe, cost-effective, and accurate diagnostic method for detecting thyroid cancer. However, about 10% of initial FNA biopsy samples from patients are non-diagnostic and require repeated FNA, which delays the diagnosis and appropriate care. On-site evaluation of the FNA sample can be performed to filter out non-diagnostic FNA samples. Unfortunately, it involves a time-consuming staining process, and a cytopathologist has to be present at the time of FNA. To bypass the staining process and expert interpretation of FNA specimens at the clinics, we developed a deep learning-based ensemble model termed FNA-Net that allows in situ screening of adequacy of unstained thyroid FNA samples smeared on a glass slide which can decrease the non-diagnostic rate in thyroid FNA. FNA-Net combines two deep learning models, a patch-based whole slide image classifier and Faster R-CNN, to detect follicular clusters with high precision. Then, FNA-Net classifies sample slides to be non-diagnostic if the total number of detected follicular clusters is less than a predetermined threshold. With bootstrapped sampling, FNA-Net achieved a 0.81 F1 score and 0.84 AUC in the precision-recall curve for detecting the non-diagnostic slides whose follicular clusters are less than six. We expect that FNA-Net can dramatically reduce the diagnostic cost associated with FNA biopsy and improve the quality of patient care.

SUBMITTER: Jang J

PROVIDER: S-EPMC10439921 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Screening adequacy of unstained thyroid fine needle aspiration samples using a deep learning-based classifier.

Jang Junbong J Kim Young H YH Westgate Brian B Zong Yang Y Hallinan Caleb C Akalin Ali A Lee Kwonmoo K

Scientific reports 20230819 1

Fine needle aspiration (FNA) biopsy of thyroid nodules is a safe, cost-effective, and accurate diagnostic method for detecting thyroid cancer. However, about 10% of initial FNA biopsy samples from patients are non-diagnostic and require repeated FNA, which delays the diagnosis and appropriate care. On-site evaluation of the FNA sample can be performed to filter out non-diagnostic FNA samples. Unfortunately, it involves a time-consuming staining process, and a cytopathologist has to be present at ...[more]

PMID: 37598279

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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

Screening adequacy of unstained thyroid fine needle aspiration samples using a deep learning-based classifier.

Publications

Screening adequacy of unstained thyroid fine needle aspiration samples using a deep learning-based classifier.

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