Cargando…

Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer

PURPOSE: The Ki67 expression is associated with the advanced clinicopathological features and poor prognosis in bladder cancer (BCa). We aimed to develop and validate magnetic resonance imaging (MRI)-based radiomics signatures to preoperatively predict the Ki67 expression status in BCa. METHODS AND...

Descripción completa

Detalles Bibliográficos
Autores principales: Zheng, Zongtai, Gu, Zhuoran, Xu, Feijia, Maskey, Niraj, He, Yanyan, Yan, Yang, Xu, Tianyuan, Liu, Shenghua, Yao, Xudong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8642943/
https://www.ncbi.nlm.nih.gov/pubmed/34863282
http://dx.doi.org/10.1186/s40644-021-00433-3
_version_ 1784609776147103744
author Zheng, Zongtai
Gu, Zhuoran
Xu, Feijia
Maskey, Niraj
He, Yanyan
Yan, Yang
Xu, Tianyuan
Liu, Shenghua
Yao, Xudong
author_facet Zheng, Zongtai
Gu, Zhuoran
Xu, Feijia
Maskey, Niraj
He, Yanyan
Yan, Yang
Xu, Tianyuan
Liu, Shenghua
Yao, Xudong
author_sort Zheng, Zongtai
collection PubMed
description PURPOSE: The Ki67 expression is associated with the advanced clinicopathological features and poor prognosis in bladder cancer (BCa). We aimed to develop and validate magnetic resonance imaging (MRI)-based radiomics signatures to preoperatively predict the Ki67 expression status in BCa. METHODS AND MATERIALS: We retrospectively collected 179 BCa patients with Ki67 expression and preoperative MRI. Radiomics features were extracted from T2-weighted (T2WI) and dynamic contrast-enhancement (DCE) images. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (low Ki67 expression group) in the training set. Minimum redundancy maximum relevance was used to identify the best features associated with Ki67 expression. Support vector machine and Least Absolute Shrinkage and Selection Operator algorithms (LASSO) were used to construct radiomics signatures in training and SMOTE-training sets, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. The decision curve analyses (DCA) and calibration curve and were used to investigate the clinical usefulness and calibration of radiomics signatures, respectively. The Kaplan-Meier test was performed to investigate the prognostic value of radiomics-predicted Ki67 expression status. RESULTS: 1218 radiomics features were extracted from T2WI and DCE images, respectively. The SMOTE-LASSO model based on nine features achieved the best predictive performance in the SMOTE-training (AUC, 0.859; accuracy, 80.3%) and validation sets (AUC, 0.819; accuracy, 81.5%) with a good calibration performance and clinical usefulness. Immunohistochemistry-based high Ki67 expression and radiomics-predicted high Ki67 expression based on the SMOTE-LASSO model were significantly associated with poor disease-free survival in training and validation sets (all P < 0.05). CONCLUSIONS: The SMOTE-LASSO model could predict the Ki67 expression status and was associated with survival outcomes of the BCa patients, thereby may aid in clinical decision-making. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-021-00433-3.
format Online
Article
Text
id pubmed-8642943
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-86429432021-12-06 Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer Zheng, Zongtai Gu, Zhuoran Xu, Feijia Maskey, Niraj He, Yanyan Yan, Yang Xu, Tianyuan Liu, Shenghua Yao, Xudong Cancer Imaging Research Article PURPOSE: The Ki67 expression is associated with the advanced clinicopathological features and poor prognosis in bladder cancer (BCa). We aimed to develop and validate magnetic resonance imaging (MRI)-based radiomics signatures to preoperatively predict the Ki67 expression status in BCa. METHODS AND MATERIALS: We retrospectively collected 179 BCa patients with Ki67 expression and preoperative MRI. Radiomics features were extracted from T2-weighted (T2WI) and dynamic contrast-enhancement (DCE) images. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (low Ki67 expression group) in the training set. Minimum redundancy maximum relevance was used to identify the best features associated with Ki67 expression. Support vector machine and Least Absolute Shrinkage and Selection Operator algorithms (LASSO) were used to construct radiomics signatures in training and SMOTE-training sets, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. The decision curve analyses (DCA) and calibration curve and were used to investigate the clinical usefulness and calibration of radiomics signatures, respectively. The Kaplan-Meier test was performed to investigate the prognostic value of radiomics-predicted Ki67 expression status. RESULTS: 1218 radiomics features were extracted from T2WI and DCE images, respectively. The SMOTE-LASSO model based on nine features achieved the best predictive performance in the SMOTE-training (AUC, 0.859; accuracy, 80.3%) and validation sets (AUC, 0.819; accuracy, 81.5%) with a good calibration performance and clinical usefulness. Immunohistochemistry-based high Ki67 expression and radiomics-predicted high Ki67 expression based on the SMOTE-LASSO model were significantly associated with poor disease-free survival in training and validation sets (all P < 0.05). CONCLUSIONS: The SMOTE-LASSO model could predict the Ki67 expression status and was associated with survival outcomes of the BCa patients, thereby may aid in clinical decision-making. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-021-00433-3. BioMed Central 2021-12-04 /pmc/articles/PMC8642943/ /pubmed/34863282 http://dx.doi.org/10.1186/s40644-021-00433-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Article
Zheng, Zongtai
Gu, Zhuoran
Xu, Feijia
Maskey, Niraj
He, Yanyan
Yan, Yang
Xu, Tianyuan
Liu, Shenghua
Yao, Xudong
Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer
title Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer
title_full Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer
title_fullStr Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer
title_full_unstemmed Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer
title_short Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer
title_sort magnetic resonance imaging-based radiomics signature for preoperative prediction of ki67 expression in bladder cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8642943/
https://www.ncbi.nlm.nih.gov/pubmed/34863282
http://dx.doi.org/10.1186/s40644-021-00433-3
work_keys_str_mv AT zhengzongtai magneticresonanceimagingbasedradiomicssignatureforpreoperativepredictionofki67expressioninbladdercancer
AT guzhuoran magneticresonanceimagingbasedradiomicssignatureforpreoperativepredictionofki67expressioninbladdercancer
AT xufeijia magneticresonanceimagingbasedradiomicssignatureforpreoperativepredictionofki67expressioninbladdercancer
AT maskeyniraj magneticresonanceimagingbasedradiomicssignatureforpreoperativepredictionofki67expressioninbladdercancer
AT heyanyan magneticresonanceimagingbasedradiomicssignatureforpreoperativepredictionofki67expressioninbladdercancer
AT yanyang magneticresonanceimagingbasedradiomicssignatureforpreoperativepredictionofki67expressioninbladdercancer
AT xutianyuan magneticresonanceimagingbasedradiomicssignatureforpreoperativepredictionofki67expressioninbladdercancer
AT liushenghua magneticresonanceimagingbasedradiomicssignatureforpreoperativepredictionofki67expressioninbladdercancer
AT yaoxudong magneticresonanceimagingbasedradiomicssignatureforpreoperativepredictionofki67expressioninbladdercancer