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...
Autores principales: | , , , , , , , , |
---|---|
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 |