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 Maleki, Amin Zandvakili, Shweta Gera, Seema D. Khutti, Adam Gersten, Samer N. Khader

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number29
JournalJournal of Pathology Informatics
Volume10
Issue number1
DOIs
StatePublished - Jan 1 2019

Fingerprint

Thyroid Neoplasms
Learning algorithms
Support vector machines
Learning systems
Pathology
Supervised learning
Surgical Pathology
Needles
Tumors
Information systems
Clinical Laboratory Information Systems
Learning
Factor IX
Fine Needle Biopsy
Papillary Thyroid cancer
Machine Learning
Sensitivity and Specificity
Support Vector Machine
Neoplasms

Keywords

  • Cytology
  • machine-learning
  • noninvasive follicular thyroid neoplasm with papillary-like nuclear features
  • papillary thyroid carcinoma
  • support vector machine

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Health Informatics
  • Computer Science Applications

Cite this

@article{555daa8c2b6c4a479fe17c5bde38f5c6,
title = "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",
abstract = "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.",
keywords = "Cytology, machine-learning, noninvasive follicular thyroid neoplasm with papillary-like nuclear features, papillary thyroid carcinoma, support vector machine",
author = "Sara Maleki and Amin Zandvakili and Shweta Gera and Khutti, {Seema D.} and Adam Gersten and Khader, {Samer N.}",
year = "2019",
month = "1",
day = "1",
doi = "10.4103/jpi.jpi_25_19",
language = "English (US)",
volume = "10",
journal = "Journal of Pathology Informatics",
issn = "2229-5089",
publisher = "Medknow Publications and Media Pvt. Ltd",
number = "1",

}

TY - JOUR

T1 - Differentiating noninvasive follicular thyroid neoplasm with papillary-like nuclear features from classic papillary thyroid carcinoma

T2 - Analysis of cytomorphologic descriptions using a novel machine-learning approach

AU - Maleki, Sara

AU - Zandvakili, Amin

AU - Gera, Shweta

AU - Khutti, Seema D.

AU - Gersten, Adam

AU - Khader, Samer N.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Cytology

KW - machine-learning

KW - noninvasive follicular thyroid neoplasm with papillary-like nuclear features

KW - papillary thyroid carcinoma

KW - support vector machine

UR - http://www.scopus.com/inward/record.url?scp=85074920076&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85074920076&partnerID=8YFLogxK

U2 - 10.4103/jpi.jpi_25_19

DO - 10.4103/jpi.jpi_25_19

M3 - Article

AN - SCOPUS:85074920076

VL - 10

JO - Journal of Pathology Informatics

JF - Journal of Pathology Informatics

SN - 2229-5089

IS - 1

M1 - 29

ER -