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 in | Scientific reports Vol. 12; no. 1; p. 21636 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
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Nature Publishing Group UK
14.12.2022
Nature Publishing Group Nature Portfolio |
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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. |
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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. |
Author_xml | – sequence: 1 givenname: Lucian G. surname: Eftimie fullname: Eftimie, Lucian G. organization: Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Pathology Department, Central University Emergency Military Hospital – sequence: 2 givenname: Remus R. surname: Glogojeanu fullname: Glogojeanu, Remus R. organization: Department of Special Motricity and Medical Recovery, The National University of Physical Education and Sports – sequence: 3 givenname: A. surname: Tejaswee fullname: Tejaswee, A. organization: Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur – sequence: 4 givenname: Pavel surname: Gheorghita fullname: Gheorghita, Pavel organization: Faculty of Energetics, University Politehnica of Bucharest – sequence: 5 givenname: Stefan G. surname: Stanciu fullname: Stanciu, Stefan G. organization: Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest – sequence: 6 givenname: Augustin surname: Chirila fullname: Chirila, Augustin organization: Pathology Department, Central University Emergency Military Hospital – sequence: 7 givenname: George A. surname: Stanciu fullname: Stanciu, George A. organization: Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest – sequence: 8 givenname: Angshuman surname: Paul fullname: Paul, Angshuman organization: Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur – sequence: 9 givenname: Radu surname: Hristu fullname: Hristu, Radu email: radu.hristu@upb.ro organization: Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest |
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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 |
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