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Diagnosis of thyroid nodules for ultrasonographic characteristics indicative of malignancy using random forest

BACKGROUND: Various combinations of ultrasonographic (US) characteristics are increasingly utilized to classify thyroid nodules. But they lack theories, and heavily depend on radiologists’ experience, and cannot correctly classify thyroid nodules. Hence, our main purpose of this manuscript is to sel...

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Autores principales: Chen, Dan, Hu, Jun, Zhu, Mei, Tang, Niansheng, Yang, Yang, Feng, Yuran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7469308/
https://www.ncbi.nlm.nih.gov/pubmed/32905307
http://dx.doi.org/10.1186/s13040-020-00223-w
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author Chen, Dan
Hu, Jun
Zhu, Mei
Tang, Niansheng
Yang, Yang
Feng, Yuran
author_facet Chen, Dan
Hu, Jun
Zhu, Mei
Tang, Niansheng
Yang, Yang
Feng, Yuran
author_sort Chen, Dan
collection PubMed
description BACKGROUND: Various combinations of ultrasonographic (US) characteristics are increasingly utilized to classify thyroid nodules. But they lack theories, and heavily depend on radiologists’ experience, and cannot correctly classify thyroid nodules. Hence, our main purpose of this manuscript is to select the US characteristics significantly associated with malignancy and to develop an efficient scoring system for facilitating ultrasonic clinicians to correctly identify thyroid malignancy. METHODS: A logistic regression (LR) model is utilized to identify the potential thyroid malignancy, and the least absolute shrinkage and selection operator (LASSO) method is adopted to simultaneously select US characteristics significantly associated with malignancy and estimate parameters in LR model. Based on the selected US characteristics, we calculate the probability for each of thyroid nodules via random forest (RF) and extreme learning machine (ELM), and develop a scoring system to classify thyroid nodules. For comparison, we also consider eight state-of-the-art methods such as support vector machine (SVM), neural network (NET), etc. The area under the receiver operating characteristic curve (AUC) is employed to measure the accuracy of various classifiers. RESULTS: The US characteristics: nodule size, AP/T≥1, solid component, micro-calcifications, hackly border, hypoechogenicity, presence of halo, unclear border, irregular margin, and central vascularity are selected as the significant predictors associated with thyroid malignancy via the LASSO LR (LLR). Using the developed scoring system, thyroid nodules are classified into the following four categories: benign, low suspicion, intermediate suspicion, and high suspicion, whose rates of malignancy correctly identified for RF (ELM) method on the testing dataset are 0.0% (4.3%), 14.3% (50.0%), 58.1% (59.1%) and 96.1% (97.7%), respectively. CONCLUSION: LLR together with RF performs better than other methods in identifying malignancy, especially for abnormal nodules, in terms of risk scores. The developed scoring system can well predict the risk of malignancy and guide medical doctors to make management decisions for reducing the number of unnecessary biopsies for benign nodules.
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spelling pubmed-74693082020-09-03 Diagnosis of thyroid nodules for ultrasonographic characteristics indicative of malignancy using random forest Chen, Dan Hu, Jun Zhu, Mei Tang, Niansheng Yang, Yang Feng, Yuran BioData Min Research BACKGROUND: Various combinations of ultrasonographic (US) characteristics are increasingly utilized to classify thyroid nodules. But they lack theories, and heavily depend on radiologists’ experience, and cannot correctly classify thyroid nodules. Hence, our main purpose of this manuscript is to select the US characteristics significantly associated with malignancy and to develop an efficient scoring system for facilitating ultrasonic clinicians to correctly identify thyroid malignancy. METHODS: A logistic regression (LR) model is utilized to identify the potential thyroid malignancy, and the least absolute shrinkage and selection operator (LASSO) method is adopted to simultaneously select US characteristics significantly associated with malignancy and estimate parameters in LR model. Based on the selected US characteristics, we calculate the probability for each of thyroid nodules via random forest (RF) and extreme learning machine (ELM), and develop a scoring system to classify thyroid nodules. For comparison, we also consider eight state-of-the-art methods such as support vector machine (SVM), neural network (NET), etc. The area under the receiver operating characteristic curve (AUC) is employed to measure the accuracy of various classifiers. RESULTS: The US characteristics: nodule size, AP/T≥1, solid component, micro-calcifications, hackly border, hypoechogenicity, presence of halo, unclear border, irregular margin, and central vascularity are selected as the significant predictors associated with thyroid malignancy via the LASSO LR (LLR). Using the developed scoring system, thyroid nodules are classified into the following four categories: benign, low suspicion, intermediate suspicion, and high suspicion, whose rates of malignancy correctly identified for RF (ELM) method on the testing dataset are 0.0% (4.3%), 14.3% (50.0%), 58.1% (59.1%) and 96.1% (97.7%), respectively. CONCLUSION: LLR together with RF performs better than other methods in identifying malignancy, especially for abnormal nodules, in terms of risk scores. The developed scoring system can well predict the risk of malignancy and guide medical doctors to make management decisions for reducing the number of unnecessary biopsies for benign nodules. BioMed Central 2020-09-03 /pmc/articles/PMC7469308/ /pubmed/32905307 http://dx.doi.org/10.1186/s13040-020-00223-w Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Chen, Dan
Hu, Jun
Zhu, Mei
Tang, Niansheng
Yang, Yang
Feng, Yuran
Diagnosis of thyroid nodules for ultrasonographic characteristics indicative of malignancy using random forest
title Diagnosis of thyroid nodules for ultrasonographic characteristics indicative of malignancy using random forest
title_full Diagnosis of thyroid nodules for ultrasonographic characteristics indicative of malignancy using random forest
title_fullStr Diagnosis of thyroid nodules for ultrasonographic characteristics indicative of malignancy using random forest
title_full_unstemmed Diagnosis of thyroid nodules for ultrasonographic characteristics indicative of malignancy using random forest
title_short Diagnosis of thyroid nodules for ultrasonographic characteristics indicative of malignancy using random forest
title_sort diagnosis of thyroid nodules for ultrasonographic characteristics indicative of malignancy using random forest
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7469308/
https://www.ncbi.nlm.nih.gov/pubmed/32905307
http://dx.doi.org/10.1186/s13040-020-00223-w
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