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Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer

PURPOSE: Radiomic models have been demonstrated to have acceptable discrimination capability for detecting lymph node metastasis (LNM). We aimed to develop a computed tomography–based radiomic model and validate its usefulness in the prediction of normal-sized LNM at node level in cervical cancer. M...

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Autores principales: Liu, Yujia, Fan, Huijian, Dong, Di, Liu, Ping, He, Bingxi, Meng, Lingwei, Chen, Jiaming, Chen, Chunlin, Lang, Jinghe, Tian, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Neoplasia Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8131712/
https://www.ncbi.nlm.nih.gov/pubmed/33975178
http://dx.doi.org/10.1016/j.tranon.2021.101113
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author Liu, Yujia
Fan, Huijian
Dong, Di
Liu, Ping
He, Bingxi
Meng, Lingwei
Chen, Jiaming
Chen, Chunlin
Lang, Jinghe
Tian, Jie
author_facet Liu, Yujia
Fan, Huijian
Dong, Di
Liu, Ping
He, Bingxi
Meng, Lingwei
Chen, Jiaming
Chen, Chunlin
Lang, Jinghe
Tian, Jie
author_sort Liu, Yujia
collection PubMed
description PURPOSE: Radiomic models have been demonstrated to have acceptable discrimination capability for detecting lymph node metastasis (LNM). We aimed to develop a computed tomography–based radiomic model and validate its usefulness in the prediction of normal-sized LNM at node level in cervical cancer. METHODS: A total of 273 LNs of 219 patients from 10 centers were evaluated in this study. We randomly divided the LNs from the 2 centers with the largest number of LNs into the training and internal validation cohorts, and the rest as the external validation cohort. Radiomic features were extracted from the arterial and venous phase images. We trained an artificial neural network (ANN) to develop two single-phase models. A radiomic model reflecting the features of two-phase images was also built for directly predicting LNM in cervical cancer. Moreover, four state-of-the-art methods were used for comparison. The performance of all models was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS: Among the models we built, the models combining the features of two phases surpassed the single-phase models, and the models generated by ANN had better performance than the others. We found that the radiomic model achieved the highest AUCs of 0.912 and 0.859 in the training and internal validation cohorts, respectively. In the external validation cohort, the AUC of the radiomic model was 0.800. CONCLUSION: We constructed a radiomic model that exhibited great ability in the prediction of LNM. The application of the model could optimize clinical staging and decision-making.
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spelling pubmed-81317122021-05-24 Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer Liu, Yujia Fan, Huijian Dong, Di Liu, Ping He, Bingxi Meng, Lingwei Chen, Jiaming Chen, Chunlin Lang, Jinghe Tian, Jie Transl Oncol Original Research PURPOSE: Radiomic models have been demonstrated to have acceptable discrimination capability for detecting lymph node metastasis (LNM). We aimed to develop a computed tomography–based radiomic model and validate its usefulness in the prediction of normal-sized LNM at node level in cervical cancer. METHODS: A total of 273 LNs of 219 patients from 10 centers were evaluated in this study. We randomly divided the LNs from the 2 centers with the largest number of LNs into the training and internal validation cohorts, and the rest as the external validation cohort. Radiomic features were extracted from the arterial and venous phase images. We trained an artificial neural network (ANN) to develop two single-phase models. A radiomic model reflecting the features of two-phase images was also built for directly predicting LNM in cervical cancer. Moreover, four state-of-the-art methods were used for comparison. The performance of all models was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS: Among the models we built, the models combining the features of two phases surpassed the single-phase models, and the models generated by ANN had better performance than the others. We found that the radiomic model achieved the highest AUCs of 0.912 and 0.859 in the training and internal validation cohorts, respectively. In the external validation cohort, the AUC of the radiomic model was 0.800. CONCLUSION: We constructed a radiomic model that exhibited great ability in the prediction of LNM. The application of the model could optimize clinical staging and decision-making. Neoplasia Press 2021-05-08 /pmc/articles/PMC8131712/ /pubmed/33975178 http://dx.doi.org/10.1016/j.tranon.2021.101113 Text en © 2021 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Research
Liu, Yujia
Fan, Huijian
Dong, Di
Liu, Ping
He, Bingxi
Meng, Lingwei
Chen, Jiaming
Chen, Chunlin
Lang, Jinghe
Tian, Jie
Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer
title Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer
title_full Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer
title_fullStr Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer
title_full_unstemmed Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer
title_short Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer
title_sort computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8131712/
https://www.ncbi.nlm.nih.gov/pubmed/33975178
http://dx.doi.org/10.1016/j.tranon.2021.101113
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