Computerized cytometry and wavelet analysis of follicular lesions for detecting malignancy: A pilot study in thyroid cytology

Gilshtein, Hayim; Mekel, Michal; Malkin, Leonid; Ben‐Izhak, Ofer; Sabo, Edmond

doi:10.1016/j.surg.2016.06.078

Cited by 9 publications

(8 citation statements)

References 17 publications

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“…The assignment of a TBSRTC category did require some expertise but only for a limited number of cases in the training set (n = 145). The training process did not necessitate any complex tasks beyond those that are part of an average cytopathologist's routine workflow . Manual acquisition of morphometric data and semiqualitative features of individual cells was not needed in the development of our MLA .…”

Section: Discussionmentioning

confidence: 99%

Application of a machine learning algorithm to predict malignancy in thyroid cytopathology

Range

Dov

Kovalsky

et al. 2020

Cancer Cytopathology

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BACKGROUND:The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) comprises 6 categories used for the diagnosis of thyroid fine-needle aspiration biopsy (FNAB). Each category has an associated risk of malignancy, which is important in the management of a thyroid nodule. More accurate predictions of malignancy may help to reduce unnecessary surgery. A machine learning algorithm (MLA) was developed to evaluate thyroid FNAB via whole slide images (WSIs) to predict malignancy. METHODS: Files were searched for all thyroidectomy specimens with preceding FNAB over 8 years.All cytologic and surgical pathology diagnoses were recorded and correlated for each nodule. One representative slide from each case was scanned to create a WSI. An MLA was designed to identify follicular cells and predict the malignancy of the final pathology. The test set comprised cases blindly reviewed by a cytopathologist who assigned a TBSRTC category. The area under the receiver operating characteristic curve was used to assess the MLA performance. RESULTS: Nine hundred eight FNABs met the criteria. The MLA predicted malignancy with a sensitivity and specificity of 92.0% and 90.5%, respectively. The areas under the curve for the prediction of malignancy by the cytopathologist and the MLA were 0.931 and 0.932, respectively. CONCLUSIONS: The performance of the MLA in predicting thyroid malignancy from FNAB WSIs is comparable to the performance of an expert cytopathologist. When the MLA and electronic medical record diagnoses are combined, the performance is superior to the performance of either alone. An MLA may be used as an adjunct to FNAB to assist in refining the indeterminate categories. Cancer Cytopathol 2020;128:287-295.

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Section: Discussionmentioning

confidence: 99%

Application of a machine learning algorithm to predict malignancy in thyroid cytopathology

Range

Dov

Kovalsky

et al. 2020

Cancer Cytopathology

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“…Indeed, the ability of automated algorithms to separate nodules according to precisely measured features which are difficult to quantify manually could be of great help for diagnosis, especially in indeterminate cytology cases. This was evident prior to the advent of WSI, when traditional machine learning was applied to static images acquired by cameras mounted on microscope, attempting to extract features from follicular cell nuclei in order to discriminate between benign and malignant follicular lesions in fine‐needle smears …”

Section: Discussionmentioning

confidence: 99%

“…Thyroid pathology is another field with great potential for the use of image analysis and AI algorithms. There is already abundant literature on the application of these computer tools to digital images of thyroid pathology, and even more concerning application to thyroid ultrasound scans, as many studies have explored AI clinical usefulness to discriminate malignant from benign thyroid nodules with radiological features . Indeed, thyroid pathology has great potential for automated algorithm application as the incidence of thyroid nodules is increasing, the diagnosis of these lesions can be challenging especially with the introduction of new entities such as noninvasive follicular thyroid neoplasm with papillary‐like nuclear features (NIFTP), and the indeterminate interpretation rate of fine‐needle aspiration (FNA) remains relatively high.…”

Section: Introductionmentioning

confidence: 99%

Impact of image analysis and artificial intelligence in thyroid pathology, with particular reference to cytological aspects

Girolami¹,

Marletta²,

Pantanowitz

et al. 2020

Cytopathology

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Objective Thyroid pathology has great potential for automated/artificial intelligence algorithm application as the incidence of thyroid nodules is increasing and the indeterminate interpretation rate of fine‐needle aspiration remains relatively high. The aim of the study is to review the published literature on automated image analysis and artificial intelligence applications to thyroid pathology with whole‐slide imaging. Methods Systematic search was carried out in electronic databases. Studies dealing with thyroid pathology and use of automated algorithms applied to whole‐slide imaging were included. Quality of studies was assessed with a modified QUADAS‐2 tool. Results Of 919 retrieved articles, 19 were included. The main themes addressed were the comparison of automated assessment of immunohistochemical staining with manual pathologist's assessment, quantification of differences in cellular and nuclear parameters among tumour entities, and discrimination between benign and malignant nodules. Correlation coefficients with manual assessment were higher than 0.76 and diagnostic performance of automated models was comparable with an expert pathologist diagnosis. Computational difficulties were related to the large size of whole‐slide images. Conclusions Overall, the results are promising and it is likely that, with the resolution of technical issues, the application of automated algorithms in thyroid pathology will increase and be adopted following suitable validation studies.

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“…Gilshtein et al used computer-based image analysis to distinguish thyroid follicular malignant lesions from benign follicular lesions. 6 To determine nuclear size, they quantified nuclear area, radius, and perimeter. To characterize nuclear shape, they measured ellipticity, nuclear contour regularity, and fractal dimension.…”

Section: Ishii Et Al Conducted Papanicolaou Staining Of Ec Cells Vs mentioning

confidence: 99%

“…In recent years, the use of computer‐based image analysis technology for pathological diagnosis has increased, as these methods allow detection of subtle histological changes. Gilshtein et al used computer‐based image analysis to distinguish thyroid follicular malignant lesions from benign follicular lesions . To determine nuclear size, they quantified nuclear area, radius, and perimeter.…”

Section: Introductionmentioning

confidence: 99%

Image quantification technology of the heterochromatin and euchromatin region for differential diagnosis in the lobular endocervical glandular hyperplasia

Kimura

Kobayashi

Kanai

et al. 2019

Diagnostic Cytopathology

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Background Lobular endocervical glandular hyperplasia (LEGH) was first described by Nucci et al. in 1999 and is believed to be a precancerous lesion of minimal deviation adenocarcinoma and gastric‐type adenocarcinoma in the uterine cervix. LEGH lesions do not always exhibit apparent cellular and structural atypia, so are difficult to distinguish from normal endocervical cells (EC cells) with cytological examination. Therefore, we often struggle to make a definite diagnosis of LEGH. Methods We used microscopy images of cytological specimens that were diagnosed as EC cells and LEGH cells. Signal intensity in whole nuclear area and in heterochromatin and euchromatin regions, euchromatin area ratio, and nuclear morphological features were quantified in each cell nucleus of the cases. Statistical analyses were conducted to determine statistical significance. Finally, we performed linear support vector machine (LSVM) modeling as a discriminant analysis using the quantified features. Results Signal intensity in whole nuclear area, and heterochromatin and euchromatin regions of EC cell nuclei were higher than that of the LEGH cell nuclei. Morphologically, EC cell nuclei were larger than LEGH cell nuclei, and nuclei of LEGH cells had irregular nuclear respectively membrane structure and an elongated shape. The LSVM accuracy of 10‐fold cross validation and leave‐one‐case‐out cross‐validation (LOCOCV) using all measured features were 84.7% to 89.3% and 78.6% to 86.0%, respectively. Conclusions The LVSM analysis using features extracted from signal intensity and morphological analysis was useful for discrimination of EC cells vs LEGH cells. We therefore believe that this image analysis method could be used for early detection of LEGH.

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Computerized cytometry and wavelet analysis of follicular lesions for detecting malignancy: A pilot study in thyroid cytology

Cited by 9 publications

References 17 publications

Application of a machine learning algorithm to predict malignancy in thyroid cytopathology

Application of a machine learning algorithm to predict malignancy in thyroid cytopathology

Impact of image analysis and artificial intelligence in thyroid pathology, with particular reference to cytological aspects

Image quantification technology of the heterochromatin and euchromatin region for differential diagnosis in the lobular endocervical glandular hyperplasia

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