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Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer

BACKGROUND: Preoperative lymph node (LN) status is important for the treatment of bladder cancer (BCa). However, a proportion of patients are at high risk for inaccurate clinical nodal staging by current methods. Here, we report an accurate magnetic resonance imaging (MRI)-based radiomics signature...

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Autores principales: Wu, Shaoxu, Zheng, Junjiong, Li, Yong, Wu, Zhuo, Shi, Siya, Huang, Ming, Yu, Hao, Dong, Wen, Huang, Jian, Lin, Tianxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6116473/
https://www.ncbi.nlm.nih.gov/pubmed/30078735
http://dx.doi.org/10.1016/j.ebiom.2018.07.029
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author Wu, Shaoxu
Zheng, Junjiong
Li, Yong
Wu, Zhuo
Shi, Siya
Huang, Ming
Yu, Hao
Dong, Wen
Huang, Jian
Lin, Tianxin
author_facet Wu, Shaoxu
Zheng, Junjiong
Li, Yong
Wu, Zhuo
Shi, Siya
Huang, Ming
Yu, Hao
Dong, Wen
Huang, Jian
Lin, Tianxin
author_sort Wu, Shaoxu
collection PubMed
description BACKGROUND: Preoperative lymph node (LN) status is important for the treatment of bladder cancer (BCa). However, a proportion of patients are at high risk for inaccurate clinical nodal staging by current methods. Here, we report an accurate magnetic resonance imaging (MRI)-based radiomics signature for the individual preoperative prediction of LN metastasis in BCa. METHODS: In total, 103 eligible BCa patients were divided into a training set (n = 69) and a validation set (n = 34). And 718 radiomics features were extracted from the cancerous volumes of interest (VOIs) on T2-weighted MRI images. A radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm in the training set, whose performance was assessed and then validated in the validation set. Stratified analyses were also performed. Based on the multivariable logistic regression analysis, a radiomics nomogram was developed incorporating the radiomics signature and selected clinical predictors. Discrimination, calibration and clinical usefulness of the nomogram were assessed. FINDINGS: Consisting of 9 selected features, the radiomics signature showed a favorable discriminatory ability in the training set with an AUC of 0.9005, which was confirmed in the validation set with an AUC of 0.8447. Encouragingly, the radiomics signature also showed good discrimination in the MRI-reported LN negative (cN0) subgroup (AUC, 0.8406). The nomogram, consisting of the radiomics signature and the MRI-reported LN status, showed good calibration and discrimination in the training and validation sets (AUC, 0.9118 and 0.8902, respectively). The decision curve analysis indicated that the nomogram was clinically useful. INTERPRETATION: The MRI-based radiomics nomogram has the potential to be used as a non-invasive tool for individualized preoperative prediction of LN metastasis in BCa. External validation is further required prior to clinical implementation.
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spelling pubmed-61164732018-08-31 Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer Wu, Shaoxu Zheng, Junjiong Li, Yong Wu, Zhuo Shi, Siya Huang, Ming Yu, Hao Dong, Wen Huang, Jian Lin, Tianxin EBioMedicine Research Paper BACKGROUND: Preoperative lymph node (LN) status is important for the treatment of bladder cancer (BCa). However, a proportion of patients are at high risk for inaccurate clinical nodal staging by current methods. Here, we report an accurate magnetic resonance imaging (MRI)-based radiomics signature for the individual preoperative prediction of LN metastasis in BCa. METHODS: In total, 103 eligible BCa patients were divided into a training set (n = 69) and a validation set (n = 34). And 718 radiomics features were extracted from the cancerous volumes of interest (VOIs) on T2-weighted MRI images. A radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm in the training set, whose performance was assessed and then validated in the validation set. Stratified analyses were also performed. Based on the multivariable logistic regression analysis, a radiomics nomogram was developed incorporating the radiomics signature and selected clinical predictors. Discrimination, calibration and clinical usefulness of the nomogram were assessed. FINDINGS: Consisting of 9 selected features, the radiomics signature showed a favorable discriminatory ability in the training set with an AUC of 0.9005, which was confirmed in the validation set with an AUC of 0.8447. Encouragingly, the radiomics signature also showed good discrimination in the MRI-reported LN negative (cN0) subgroup (AUC, 0.8406). The nomogram, consisting of the radiomics signature and the MRI-reported LN status, showed good calibration and discrimination in the training and validation sets (AUC, 0.9118 and 0.8902, respectively). The decision curve analysis indicated that the nomogram was clinically useful. INTERPRETATION: The MRI-based radiomics nomogram has the potential to be used as a non-invasive tool for individualized preoperative prediction of LN metastasis in BCa. External validation is further required prior to clinical implementation. Elsevier 2018-08-02 /pmc/articles/PMC6116473/ /pubmed/30078735 http://dx.doi.org/10.1016/j.ebiom.2018.07.029 Text en © 2018 The Authors http://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 Research Paper
Wu, Shaoxu
Zheng, Junjiong
Li, Yong
Wu, Zhuo
Shi, Siya
Huang, Ming
Yu, Hao
Dong, Wen
Huang, Jian
Lin, Tianxin
Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
title Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
title_full Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
title_fullStr Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
title_full_unstemmed Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
title_short Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
title_sort development and validation of an mri-based radiomics signature for the preoperative prediction of lymph node metastasis in bladder cancer
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6116473/
https://www.ncbi.nlm.nih.gov/pubmed/30078735
http://dx.doi.org/10.1016/j.ebiom.2018.07.029
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