Multilayer Perceptron Predicting Cervical Lymph Node Metastasis for Papillary Thyroid Carcinoma

Background: Lymph node metastasis is related to thyroid cancer recurrence; hence, early identification and prediction of cervical lymph node metastasis (CLNM) in thyroid cancer are essential. Materials and methods: Ultrasound characteristics and patients’ clinical information for 478 thyroid nodules...

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Main Authors: Jing-wen Shi, Qi Zhang, Tian-tong Zhu, Ying Huang
Format: Article
Language:English
Published: Compuscript Ltd 2022-03-01
Series:BIO Integration
Subjects:
Online Access:https://www.ingentaconnect.com/contentone/cscript/bioi/2022/00000003/00000001/art00002
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author Jing-wen Shi
Qi Zhang
Tian-tong Zhu
Ying Huang
author_facet Jing-wen Shi
Qi Zhang
Tian-tong Zhu
Ying Huang
author_sort Jing-wen Shi
collection DOAJ
description Background: Lymph node metastasis is related to thyroid cancer recurrence; hence, early identification and prediction of cervical lymph node metastasis (CLNM) in thyroid cancer are essential. Materials and methods: Ultrasound characteristics and patients’ clinical information for 478 thyroid nodules from 383 patients were collected, and a multilayer perceptron (MLP) was used to train and test the veracity to predict CLNM and form a network model. Sixty new patients with papillary thyroid carcinoma (PTC) were evaluated with the MLP neural network model. The metastasis status of these patients was then compared with the pathological results. The prediction of metastasis by the MLP and by multiple regression was compared. Results: Calcification, age, sex, and maximum diameter were important predictive factors of CLNM by the MLP, and the area under the receiver operating characteristic curve was 0.715. No significant differences were found between the MLP and multiple regression in predicting CLNM. The average confidence of the model used in these new patients in predicting metastasis with PTC was 68.9%. Conclusion: Ultrasound images from thyroid nodule characteristics and patients’ clinical information can be used as predictive factors of CLNM by the MLP method to a certain extent.
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spelling doaj.art-fc822a58a6b64e48ba6713bac9ac2bb72022-12-21T18:18:56ZengCompuscript LtdBIO Integration2712-00822022-03-013131010.15212/bioi-2021-0029Multilayer Perceptron Predicting Cervical Lymph Node Metastasis for Papillary Thyroid CarcinomaJing-wen Shi0Qi Zhang1Tian-tong Zhu2Ying Huang3Department of Ultrasound, Shengjing Hospital of China Medical University, 110004, Shenyang, ChinaDepartment of Ultrasound, Shengjing Hospital of China Medical University, 110004, Shenyang, ChinaDepartment of Ultrasound, Shengjing Hospital of China Medical University, 110004, Shenyang, ChinaDepartment of Ultrasound, Shengjing Hospital of China Medical University, 110004, Shenyang, ChinaBackground: Lymph node metastasis is related to thyroid cancer recurrence; hence, early identification and prediction of cervical lymph node metastasis (CLNM) in thyroid cancer are essential. Materials and methods: Ultrasound characteristics and patients’ clinical information for 478 thyroid nodules from 383 patients were collected, and a multilayer perceptron (MLP) was used to train and test the veracity to predict CLNM and form a network model. Sixty new patients with papillary thyroid carcinoma (PTC) were evaluated with the MLP neural network model. The metastasis status of these patients was then compared with the pathological results. The prediction of metastasis by the MLP and by multiple regression was compared. Results: Calcification, age, sex, and maximum diameter were important predictive factors of CLNM by the MLP, and the area under the receiver operating characteristic curve was 0.715. No significant differences were found between the MLP and multiple regression in predicting CLNM. The average confidence of the model used in these new patients in predicting metastasis with PTC was 68.9%. Conclusion: Ultrasound images from thyroid nodule characteristics and patients’ clinical information can be used as predictive factors of CLNM by the MLP method to a certain extent.https://www.ingentaconnect.com/contentone/cscript/bioi/2022/00000003/00000001/art00002cancerlymph nodemetastasispredictthyroid
spellingShingle Jing-wen Shi
Qi Zhang
Tian-tong Zhu
Ying Huang
Multilayer Perceptron Predicting Cervical Lymph Node Metastasis for Papillary Thyroid Carcinoma
BIO Integration
cancer
lymph node
metastasis
predict
thyroid
title Multilayer Perceptron Predicting Cervical Lymph Node Metastasis for Papillary Thyroid Carcinoma
title_full Multilayer Perceptron Predicting Cervical Lymph Node Metastasis for Papillary Thyroid Carcinoma
title_fullStr Multilayer Perceptron Predicting Cervical Lymph Node Metastasis for Papillary Thyroid Carcinoma
title_full_unstemmed Multilayer Perceptron Predicting Cervical Lymph Node Metastasis for Papillary Thyroid Carcinoma
title_short Multilayer Perceptron Predicting Cervical Lymph Node Metastasis for Papillary Thyroid Carcinoma
title_sort multilayer perceptron predicting cervical lymph node metastasis for papillary thyroid carcinoma
topic cancer
lymph node
metastasis
predict
thyroid
url https://www.ingentaconnect.com/contentone/cscript/bioi/2022/00000003/00000001/art00002
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AT qizhang multilayerperceptronpredictingcervicallymphnodemetastasisforpapillarythyroidcarcinoma
AT tiantongzhu multilayerperceptronpredictingcervicallymphnodemetastasisforpapillarythyroidcarcinoma
AT yinghuang multilayerperceptronpredictingcervicallymphnodemetastasisforpapillarythyroidcarcinoma