Journal of Pathology Informatics

ORIGINAL ARTICLE
Year
: 2019  |  Volume : 10  |  Issue : 1  |  Page : 29-

Differentiating noninvasive follicular thyroid neoplasm with papillary-like nuclear features from classic papillary thyroid carcinoma: Analysis of cytomorphologic descriptions using a novel machine-learning approach


Sara Maleki1, Amin Zandvakili2, Shweta Gera3, Seema D Khutti4, Adam Gersten3, Samer N Khader3 
1 Department of Pathology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York; Department of Pathology, Rhode Island Hospital, Alpert Medical School of Brown University, Providence, RI, USA
2 Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
3 Department of Pathology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA
4 Department of Pathology and Laboratory Medicine, Hofstra Northwell Health School of Medicine, New York, USA

Correspondence Address:
Sara Maleki
Rhode Island Hospital 593 Eddy Street APC-12, Providence 02903, RI
USA

Background: Recent studies show various cytomorphologic features that can assist in the differentiation of classic papillary thyroid carcinoma (cPTC) from noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Differentiating these two entities changes the clinical management significantly. We evaluated the performance of support vector machine (SVM), a machine learning algorithm, in differentiating cases of NIFTP and encapsulated follicular variant of papillary thyroid carcinoma with no capsular or lymphovascular invasion (EFVPTC) from cases of cPTC with the use of microscopic descriptions. SVM is a supervised learning algorithm used in classification problems. It assigns the input data to one of two categories by building a model based on a set of training examples (learning) and then using that learned model to classify new examples. Methods: Surgical pathology cases with the diagnosis of cPTC, NIFTP, and EFVPTC, were obtained from the laboratory information system. Only cases with existing fine-needle aspiration matching the tumor and available microscopic description were included. NIFTP cases with ipsilateral micro-PTC were excluded. The final cohort consisted of 59 cases (29 cPTCs and 30 NIFTP/EFVPTCs). Results: SVM successfully differentiated cPTC from NIFTP/EFVPTC 76.05 ± 0.96% of times (above chance,P < 0.05) with the sensitivity of 72.6% and specificity of 81.6% in detecting cPTC. Conclusions: This machine learning algorithm was successful in distinguishing NIFTP/EFVPTC from cPTC. Our results are compatible with the prior studies, which show cytologic features are helpful in differentiating these two entities. Furthermore, this study shows the power and potential of this approach for clinical use and in developing data-driven scoring systems, which can guide cytopathology and surgical pathology diagnosis.


How to cite this article:
Maleki S, Zandvakili A, Gera S, Khutti SD, Gersten A, Khader SN. Differentiating noninvasive follicular thyroid neoplasm with papillary-like nuclear features from classic papillary thyroid carcinoma: Analysis of cytomorphologic descriptions using a novel machine-learning approach.J Pathol Inform 2019;10:29-29


How to cite this URL:
Maleki S, Zandvakili A, Gera S, Khutti SD, Gersten A, Khader SN. Differentiating noninvasive follicular thyroid neoplasm with papillary-like nuclear features from classic papillary thyroid carcinoma: Analysis of cytomorphologic descriptions using a novel machine-learning approach. J Pathol Inform [serial online] 2019 [cited 2019 Oct 23 ];10:29-29
Available from: http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2019;volume=10;issue=1;spage=29;epage=29;aulast=Maleki;type=0