ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2010
Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed, 90 citations in OpenAlex.
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- Follicular Lesions with Papillary Nuclear Characteristics: Differences in Chromatin Detected by Computerized Image Analysis.Archives of endocrinology and metabolism · 2021Article
- Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis.PloS one · 2021Article
- Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives.Frontiers in oncology · 2020Review
- Diagnosis of Liver Neoplasms by Computational and Statistical Image Analysis.Gastroenterology research · 2019Article
- Head and Neck Cancer Detection in Digitized Whole-Slide Histology Using Convolutional Neural Networks.Scientific reports · 2019Article
- Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association.Journal of pathology informatics · 2019Review
- Follicular thyroid lesions: is there a discriminatory potential in the computerized nuclear analysis?Endocrine connections · 2018Article
- Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images.Biomedical engineering online · 2018Article
- Correlation Filters for Detection of Cellular Nuclei in Histopathology Images.Journal of medical systems · 2017Article
- Predictive Nuclear Chromatin Characteristics of Melanoma and Dysplastic Nevi.Journal of pathology informatics · 2017Article
- Presence of free triiodothyronine and free thyroxine in thyroid follicles may be correlated with the quick secretion of thyroid hormones under certain physiological conditions.Biomedical reports · 2016Article
- A continuous linear optimal transport approach for pattern analysis in image datasets.Pattern recognition · 2016Article
- The Radon Cumulative Distribution Transform and Its Application to Image Classification.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2016Article
- Pan-cancer analysis of the extent and consequences of intratumor heterogeneity.Nature medicine · 2016Article
- Detection of malignant mesothelioma using nuclear structure of mesothelial cells in effusion cytology specimens.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2015Article
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3 authors at 2 institutions in 1 country.
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Abstract
Follicular lesions of the thyroid are traditionally difficult and tedious challenges in diagnostic surgical pathology in part due to lack of obvious discriminatory cytological and microarchitectural features. We describe a computerized method to detect and classify follicular adenoma of the thyroid, follicular carcinoma of the thyroid, and normal thyroid based on the nuclear chromatin distribution from digital images of tissue obtained by routine histological methods. Our method is based on determining whether a set of nuclei, obtained from histological images using automated image segmentation, is most similar to sets of nuclei obtained from normal or diseased tissues. This comparison is performed utilizing numerical features, a support vector machine, and a simple voting strategy. We also describe novel methods to identify unique and defining chromatin patterns pertaining to each class. Unlike previous attempts in detecting and classifying these thyroid lesions using computational imaging, our results show that our method can automatically classify the data pertaining to 10 different human cases with 100% accuracy after blind cross validation using at most 43 nuclei randomly selected from each patient. We conclude that nuclear structure alone contains enough information to automatically classify the normal thyroid, follicular carcinoma, and follicular adenoma, as long as groups of nuclei (instead of individual ones) are used. We also conclude that the distribution of nuclear size and chromatin concentration (how tightly packed it is) seem to be discriminating features between nuclei of follicular adenoma, follicular carcinoma, and normal thyroid.
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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.