ArticleJournal of pathology informatics2022
A Study of Thyroid Fine Needle Aspiration of Follicular Adenoma in the "Atypia of Undetermined Significance" Bethesda Category Using Digital Image Analysis.
Article in Journal of pathology informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Research progress on artificial intelligence technology-assisted diagnosis of thyroid diseases.Frontiers in oncology · 2025Review
- Large-scale validation study of an improved semiautonomous urine cytology assessment tool: AutoParis-X.Cancer cytopathology · 2023Article
- Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions.Thyroid : official journal of the American Thyroid Association · 2023Review
- Current status of machine learning in thyroid cytopathology.Journal of pathology informatics · 2023Review
- Applications of machine and deep learning to thyroid cytology and histopathology: a review.Frontiers in oncology · 2023Review
- Successful integration of thyroid cytopathology and surgical pathology education in an E-module format.Journal of pathology informatics · 2022Article
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOriginally designed for computerized image analysis, ThinPrep is underutilized in that role outside gynecological cytology. It can be used to address the inter/intra-observer variability in the evaluation of thyroid fine needle aspiration (TFNA) biopsy and help pathologists to gain additional insight into thyroid cytomorphology.
methodsWe designed and validated a feature engineering and supervised machine learning-based digital image analysis method using ImageJ and Python scikit-learn . The method was trained and validated from 400 low power (100x) and 400 high power (400x) images generated from 40 TFNA cases.
resultThe area under the curve (AUC) for receiver operating characteristics (ROC) is 0.75 (0.74-0.82) for model based from low-power images and 0.74 (0.69-0.79) for the model based from high-power images. Cytomorphologic features were synthesized using feature engineering and when performed in isolation, they achieved AUC of 0.71 (0.64-0.77) for chromatin, 0.70 (0.64-0.73) for cellularity, 0.65 (0.60-0.69) for cytoarchitecture, 0.57 (0.51-0.61) for nuclear size, and 0.63 (0.57-0.68) for nuclear shape.
conclusionOur study proves that ThinPrep is an excellent preparation method for digital image analysis of thyroid cytomorphology. It can be used to quantitatively harvest morphologic information for diagnostic purpose.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.