Differential diagnosis of thyroid nodule capsules using random forest guided selection of image features

Microscopic evaluation of tissue sections stained with hematoxylin and eosin is the current gold standard for diagnosing thyroid pathology. Digital pathology is gaining momentum providing the pathologist with additional cues to traditional routes when placing a diagnosis, therefore it is extremely i...

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Published inScientific reports Vol. 12; no. 1; p. 21636
Main Authors Eftimie, Lucian G., Glogojeanu, Remus R., Tejaswee, A., Gheorghita, Pavel, Stanciu, Stefan G., Chirila, Augustin, Stanciu, George A., Paul, Angshuman, Hristu, Radu
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 14.12.2022
Nature Publishing Group
Nature Portfolio
Subjects
631/67/1459/1843
639/766/930/2735
692/698/1460/568
Adenoma
Adenoma - pathology
Capsules
Collagen
Diagnosis, Differential
Differential diagnosis
Histopathology
Humanities and Social Sciences
Humans
Image processing
Microscopy
multidisciplinary
Nodules
Papillary thyroid carcinoma
Pathology
Random Forest
Scanners
Science
Science (multidisciplinary)
Thyroid
Thyroid cancer
Thyroid Neoplasms - diagnostic imaging
Thyroid Neoplasms - pathology
Thyroid Nodule - diagnostic imaging
Thyroid Nodule - pathology
Tumors
Ultrasonic imaging
Online AccessGet full text

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Abstract Microscopic evaluation of tissue sections stained with hematoxylin and eosin is the current gold standard for diagnosing thyroid pathology. Digital pathology is gaining momentum providing the pathologist with additional cues to traditional routes when placing a diagnosis, therefore it is extremely important to develop new image analysis methods that can extract image features with diagnostic potential. In this work, we use histogram and texture analysis to extract features from microscopic images acquired on thin thyroid nodule capsules sections and demonstrate how they enable the differential diagnosis of thyroid nodules. Targeted thyroid nodules are benign (i.e., follicular adenoma) and malignant (i.e., papillary thyroid carcinoma and its sub-type arising within a follicular adenoma). Our results show that the considered image features can enable the quantitative characterization of the collagen capsule surrounding thyroid nodules and provide an accurate classification of the latter’s type using random forest.
AbstractList Abstract Microscopic evaluation of tissue sections stained with hematoxylin and eosin is the current gold standard for diagnosing thyroid pathology. Digital pathology is gaining momentum providing the pathologist with additional cues to traditional routes when placing a diagnosis, therefore it is extremely important to develop new image analysis methods that can extract image features with diagnostic potential. In this work, we use histogram and texture analysis to extract features from microscopic images acquired on thin thyroid nodule capsules sections and demonstrate how they enable the differential diagnosis of thyroid nodules. Targeted thyroid nodules are benign (i.e., follicular adenoma) and malignant (i.e., papillary thyroid carcinoma and its sub-type arising within a follicular adenoma). Our results show that the considered image features can enable the quantitative characterization of the collagen capsule surrounding thyroid nodules and provide an accurate classification of the latter’s type using random forest.
Abstract Microscopic evaluation of tissue sections stained with hematoxylin and eosin is the current gold standard for diagnosing thyroid pathology. Digital pathology is gaining momentum providing the pathologist with additional cues to traditional routes when placing a diagnosis, therefore it is extremely important to develop new image analysis methods that can extract image features with diagnostic potential. In this work, we use histogram and texture analysis to extract features from microscopic images acquired on thin thyroid nodule capsules sections and demonstrate how they enable the differential diagnosis of thyroid nodules. Targeted thyroid nodules are benign (i.e., follicular adenoma) and malignant (i.e., papillary thyroid carcinoma and its sub-type arising within a follicular adenoma). Our results show that the considered image features can enable the quantitative characterization of the collagen capsule surrounding thyroid nodules and provide an accurate classification of the latter’s type using random forest.
Microscopic evaluation of tissue sections stained with hematoxylin and eosin is the current gold standard for diagnosing thyroid pathology. Digital pathology is gaining momentum providing the pathologist with additional cues to traditional routes when placing a diagnosis, therefore it is extremely important to develop new image analysis methods that can extract image features with diagnostic potential. In this work, we use histogram and texture analysis to extract features from microscopic images acquired on thin thyroid nodule capsules sections and demonstrate how they enable the differential diagnosis of thyroid nodules. Targeted thyroid nodules are benign (i.e., follicular adenoma) and malignant (i.e., papillary thyroid carcinoma and its sub-type arising within a follicular adenoma). Our results show that the considered image features can enable the quantitative characterization of the collagen capsule surrounding thyroid nodules and provide an accurate classification of the latter's type using random forest.
ArticleNumber 21636
Author Stanciu, George A.
Gheorghita, Pavel
Chirila, Augustin
Hristu, Radu
Eftimie, Lucian G.
Stanciu, Stefan G.
Paul, Angshuman
Tejaswee, A.
Glogojeanu, Remus R.
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SSID ssj0000529419
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Snippet Microscopic evaluation of tissue sections stained with hematoxylin and eosin is the current gold standard for diagnosing thyroid pathology. Digital pathology...
Abstract Microscopic evaluation of tissue sections stained with hematoxylin and eosin is the current gold standard for diagnosing thyroid pathology. Digital...
Abstract Microscopic evaluation of tissue sections stained with hematoxylin and eosin is the current gold standard for diagnosing thyroid pathology. Digital...
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SubjectTerms 631/67/1459/1843
639/766/930/2735
692/698/1460/568
Adenoma
Adenoma - pathology
Capsules
Collagen
Diagnosis, Differential
Differential diagnosis
Histopathology
Humanities and Social Sciences
Humans
Image processing
Microscopy
multidisciplinary
Nodules
Papillary thyroid carcinoma
Pathology
Random Forest
Scanners
Science
Science (multidisciplinary)
Thyroid
Thyroid cancer
Thyroid Neoplasms - diagnostic imaging
Thyroid Neoplasms - pathology
Thyroid Nodule - diagnostic imaging
Thyroid Nodule - pathology
Tumors
Ultrasonic imaging
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Title Differential diagnosis of thyroid nodule capsules using random forest guided selection of image features
URI https://link.springer.com/article/10.1038/s41598-022-25788-w
https://www.ncbi.nlm.nih.gov/pubmed/36517531
https://www.proquest.com/docview/2754337488
https://search.proquest.com/docview/2754860218
https://pubmed.ncbi.nlm.nih.gov/PMC9751070
https://doaj.org/article/dddfe2e735474e38a6e1293ec6fa039b
Volume 12
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