Cargando…
Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models
BACKGROUND: Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4524105/ https://www.ncbi.nlm.nih.gov/pubmed/26242589 http://dx.doi.org/10.1186/s12880-015-0069-9 |
_version_ | 1782384159546671104 |
---|---|
author | Khalvati, Farzad Wong, Alexander Haider, Masoom A. |
author_facet | Khalvati, Farzad Wong, Alexander Haider, Masoom A. |
author_sort | Khalvati, Farzad |
collection | PubMed |
description | BACKGROUND: Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data. METHODS: In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models. RESULTS: The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy. CONCLUSIONS: Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a comprehensive set of texture features and a feature selection method, optimal texture feature models were constructed that improved the prostate cancer auto-detection significantly compared to conventional MP-MRI texture feature models. |
format | Online Article Text |
id | pubmed-4524105 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-45241052015-08-05 Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models Khalvati, Farzad Wong, Alexander Haider, Masoom A. BMC Med Imaging Research Article BACKGROUND: Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data. METHODS: In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models. RESULTS: The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy. CONCLUSIONS: Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a comprehensive set of texture features and a feature selection method, optimal texture feature models were constructed that improved the prostate cancer auto-detection significantly compared to conventional MP-MRI texture feature models. BioMed Central 2015-08-05 /pmc/articles/PMC4524105/ /pubmed/26242589 http://dx.doi.org/10.1186/s12880-015-0069-9 Text en © Khalvati et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Khalvati, Farzad Wong, Alexander Haider, Masoom A. Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models |
title | Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models |
title_full | Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models |
title_fullStr | Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models |
title_full_unstemmed | Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models |
title_short | Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models |
title_sort | automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4524105/ https://www.ncbi.nlm.nih.gov/pubmed/26242589 http://dx.doi.org/10.1186/s12880-015-0069-9 |
work_keys_str_mv | AT khalvatifarzad automatedprostatecancerdetectionviacomprehensivemultiparametricmagneticresonanceimagingtexturefeaturemodels AT wongalexander automatedprostatecancerdetectionviacomprehensivemultiparametricmagneticresonanceimagingtexturefeaturemodels AT haidermasooma automatedprostatecancerdetectionviacomprehensivemultiparametricmagneticresonanceimagingtexturefeaturemodels |