Evidence map›Paper›PMID 38684222›Full record

ArticleJournal of pathology and translational medicine2024

Revisiting the utility of identifying nuclear grooves as unique nuclear changes by an object detector model.

Pedro R F Rende, Joel Machado Pires, Kátia Sakimi Nakadaira, Sara Lopes, João Vale, Fabio Hecht, Fabyan E L Beltrão, Gabriel J R Machado, Edna T Kimura, Catarina Eloy and 1 more

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Article in Journal of pathology and translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

11 authors.

Pedro R F RendeBioregulation Department, Health and Science Institute, Federal University of Bahia, Salvador, Brazil.
Joel Machado PiresInstitute of Computing, Federal University of Bahia, Salvador, Brazil.
Kátia Sakimi NakadairaInstitute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
Sara LopesEndocrinology Department, Hospital de Braga, Braga, Portugal.
João ValeLaboratory of Pathology of the Institute of Pathology and Molecular Immunology, University of Porto, Porto, Portugal.
Fabio HechtDepartment of Biomedical Genetics, University of Rochester, Rochester, New York, USA.
Fabyan E L BeltrãoBioregulation Department, Health and Science Institute, Federal University of Bahia, Salvador, Brazil.
Gabriel J R MachadoBioregulation Department, Health and Science Institute, Federal University of Bahia, Salvador, Brazil.
Edna T KimuraInstitute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
Catarina EloyLaboratory of Pathology of the Institute of Pathology and Molecular Immunology, University of Porto, Porto, Portugal.
Helton E RamosBioregulation Department, Health and Science Institute, Federal University of Bahia, Salvador, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAmong other structures, nuclear grooves are vastly found in papillary thyroid carcinoma (PTC). Considering that the application of artificial intelligence in thyroid cytology has potential for diagnostic routine, our goal was to develop a new supervised convolutional neural network capable of identifying nuclear grooves in Diff-Quik stained whole-slide images (WSI) obtained from thyroid fineneedle aspiration.

methodsWe selected 22 Diff-Quik stained cytological slides with cytological diagnosis of PTC and concordant histological diagnosis. Each of the slides was scanned, forming a WSI. Images that contained the region of interest were obtained, followed by pre-formatting, annotation of the nuclear grooves and data augmentation techniques. The final dataset was divided into training and validation groups in a 7:3 ratio.

resultsThis is the first artificial intelligence model based on object detection applied to nuclear structures in thyroid cytopathology. A total of 7,255 images were obtained from 22 WSI, totaling 7,242 annotated nuclear grooves. The best model was obtained after it was submitted 15 times with the train dataset (14th epoch), with 67% true positives, 49.8% for sensitivity and 43.1% for predictive positive value.

conclusionsThe model was able to develop a structure predictor rule, indicating that the application of an artificial intelligence model based on object detection in the identification of nuclear grooves is feasible. Associated with a reduction in interobserver variability and in time per slide, this demonstrates that nuclear evaluation constitutes one of the possibilities for refining the diagnosis through computational models.

Indexed as

Artificial intelligenceCytologyFine-needle aspirationMachine learningThyroid gland

Identifiers

PMID38684222
PMCPMC11106606

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