Computer-aided detection of prostate cancer in early stages using multi-parameter MRI: : A promising approach for early diagnosis
Abstract
Background:
Transrectal ultrasound-guided prostate biopsy is the gold standard diagnostic test for prostate cancer, but it is an invasive examination of non-targeted puncture and has a high false-negative rate.
Objective:
In this study, we aimed to develop a computer-assisted prostate cancer diagnosis method based on multiparametric MRI (mpMRI) images.
Methods:
We retrospectively collected 106 patients who underwent radical prostatectomy after diagnosis with prostate biopsy. mpMRI images, including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), and dynamic-contrast enhanced (DCE), and were accordingly analyzed. We extracted the region of interest (ROI) about the tumor and benign area on the three sequential MRI axial images at the same level. The ROI data of 433 mpMRI images were obtained, of which 202 were benign and 231 were malignant. Of those, 50 benign and 50 malignant images were used for training, and the 333 images were used for verification. Five main feature groups, including histogram, GLCM, GLGCM, wavelet-based multi-fractional Brownian motion features and Minkowski function features, were extracted from the mpMRI images. The selected characteristic parameters were analyzed by MATLAB software, and three analysis methods with higher accuracy were selected.
Results:
Through prostate cancer identification based on mpMRI images, we found that the system uses 58 texture features and 3 classification algorithms, including Support Vector Machine (SVM), K-nearest Neighbor (KNN), and Ensemble Learning (EL), performed well. In the T2WI-based classification results, the SVM achieved the optimal accuracy and AUC values of 64.3% and 0.67. In the DCE-based classification results, the SVM achieved the optimal accuracy and AUC values of 72.2% and 0.77. In the DWI-based classification results, the ensemble learning achieved optimal accuracy as well as AUC values of 75.1% and 0.82. In the classification results based on all data combinations, the SVM achieved the optimal accuracy and AUC values of 66.4% and 0.73.
Conclusion:
The proposed computer-aided diagnosis system provides a good assessment of the diagnosis of the prostate cancer, which may reduce the burden of radiologists and improve the early diagnosis of prostate cancer.
References
[1]
Zhang K, Bangma CH, Roobol MJ. Prostate cancer screening in Europe and Asia. Asian J Urol. 2017; 4(2): 86-95.
[2]
Zhao K, Wang C, Hu J, Yang X, Wang H, Li F, et al. Prostate cancer identification: Quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model. Sci China Life Sci. 2015; 58(7): 666-73.
[3]
Hubner N, Shariat S, Remzi M. Prostate biopsy: Guidelines and evidence. Curr Opin Urol. 2018; 28(4): 354-9.
[4]
Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ, et al. Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol. 2019; 76(3): 340-51.
[5]
Naguib RN, Robinson MC, Neal DE, Hamdy FC. Neural network analysis of combined conventional and experimental prognostic markers in prostate cancer: A pilot study. Br J Cancer. 1998; 78(2): 246-50.
[6]
Liu C, Chen S, Yang Y, Shao DD, Peng WX, Wang Y, et al. The value of the computer-aided diagnosis system for thyroid lesions based on computed tomography images. Quant Imaging Med Surg. 2019; 9(4): 642-53.
[7]
Tsantis S, Dimitropoulos N, Cavouras D, Nikiforidis G. Morphological and wavelet features towards sonographic thyroid nodules evaluation. Comput Med Imaging Graph. 2009; 33(2): 91-9.
[8]
Hambrock T, Vos PC, Hulsbergen-van de Kaa CA, Barentsz JO, Huisman HJ. Prostate cancer: Computer-aided diagnosis with multiparametric 3-T MR imaging-effect on observer performance. Radiology. 2013; 266: 521-30.
[9]
Vos PC, Barentsz JO, Karssemeijer N, Huisman HJ. Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis. Phys Med Biol. 2012; 57: 1527-42.
[10]
Niaf E, Lartizien C, Bratan F, Roche L, Rabilloud M, Mege-Lechevallier F, Rouviere O. Prostate focal peripheral zone lesions: Characterization at multiparametric MR imaging-influence of a computer-aided diagnosis system. Radiology. 2014; 271: 761-9.
[11]
Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017; 19: 221-48.
[12]
Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015; 61: 85-117.
[13]
Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho JB, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014; 5: 4006.
[14]
Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol. 2015; 33(8): 831-8.
[15]
Le MH, Chen J, Wang L, Wang ZW, Liu WY, Tim KT, et al. Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks. Phys Med Biol. 2017; 62(16): 6497-514.
[16]
Reda I, Khalil A, Elmogy M, Abou A, Shalaby A, Abou M, et al. Deep learning role in early diagnosis of prostate cancer. Technol Cancer Res Treat. 2018; 17: 1533034618775530.
[17]
Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang HY, Baxter SL, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018; 172(5): 122-31.
[18]
Jin A, Li Y, Shen J, Zhang Y, Wang Y. Clinical value of a computer-aided diagnosis system in thyroid nodules: analysis of a reading map competition. Ultrasound Med Biol. 2019; 45(10): 2666-71.
[19]
Song Y, Zhang YD, Yan X, Liu H, Zhou MX, Hu BW, et al. Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI. J Magn Reson Imaging. 2018; 48(6): 1570-7.
[20]
Lee SJ. Infection after transrectal ultrasound-guided prostate biopsy. Korean J Urol. 2015; 56(5): 346-50.
[21]
Lim S, Jun C, Chang D, Petrisor D, Han M, Stoianovici D. Robotic transrectal ultrasound guided prostate biopsy. IEEE Trans Biomed Eng. 2019; 66(9): 2527-37.
[22]
Perrot T, Scheffler M, Boto J, Delattre MA, Combescure C, Pusztaszeri M, et al. Diffusion in prostate cancer detection on a 3T scanner: How many b-values are needed. J Magn Reson Imaging. 2016; 44(3): 601-9.
[23]
Kalavagunta C, Zhou X, Schmechel SC, Metzger GJ. Registration of in vivo prostate MRI and pseudo-whole mount histology using Local Affine Transformations guided by Internal Structures (LATIS). Journal of Magnetic Resonance Imaging. 2014; 41(4): 1104-14.
[24]
Oliveira FP, Tavares JMR. Medical image registration: A review. Computer Methods in Biomechanics and Biomedical Engineering. 2014; 17: 73-93.
[25]
Mehta P, Antonelli M, Hashim U, Emberton M, Punwani S, Ourselin S. Computer-aided diagnosis of prostate cancer using multiparametric MRI and clinical features: A patient-level classification framework. Med Image Anal. 2021; 73: 102153.
[26]
Giannini V, Rosati S, Regge D. Specifificity improvement of a CAD system for multiparametric MR prostate cancer using texture features and artifificial neural networks. Health Technol. 2017; 7: 71-80.
[27]
Giannini V, Mazzetti S, Defeudis A, Stranieri G, Calandri M, Bollito E, et al. A fully automatic artificial intelligence system able to detect and characterize prostate cancer using multiparametric MRI: Multicenter and multi-scanner validation. Front Oncol. 2021; 1(11): 718155.
[28]
Roethke MC, Kuru TH, Mueller-Wolf MB, Agterhuis E, Edler C, Hohenfellner M, et al. Evaluation of an automated analysis tool for prostate cancer prediction using multiparametric magnetic resonance imaging. PLoS One. 2016; 11: e0159803.
[29]
Hoeks CM, Barentsz JO, Hambrock T, Yakar D, Somford DM, Heijmink S, et al. Prostate cancer: Multiparametric MR imaging for detection, localization, and staging. Radiology. 2011; 261(1): 46-66.
[30]
Shah V, Turkbey B, Mani H, Pang YX, Pohida T, Merino MJ, et al. Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging. Med Phys. 2012; 39(7): 4093-103.
[31]
Gao WJ, Zhang PB, Wang H, Tuo PF, Li ZQ. Magnetic resonance imaging image feature analysis algorithm under convolutional neural network in the diagnosis and risk stratification of prostate cancer. J Healthc Eng. 2021; 2021: 1034661.
[32]
Xiong H, He XJ, Guo DJ. Value of MRI texture analysis for predicting high-grade prostate cancer. Clinical Imaging. 2021; 72: 168-74.
Recommendations
Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI
Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by ...
Prostate Cancer Detection: Performance of Radiomics Analysis in Multiparametric MRI
Image Analysis and Processing - ICIAP 2023 WorkshopsAbstractThe purpose of the study was to evaluate the performance of radiomics analysis of MR images for the detection of prostate cancer. The radiomics analysis was conducted using axial T2-weighted images from 49 prostate cancers. The study employs a ...
Computer-aided breast cancer detection and classification in mammography: A comprehensive review
AbstractCancer is the second cause of mortality worldwide and it has been identified as a perilous disease. Breast cancer accounts for ∼20% of all new cancer cases worldwide, making it a major cause of morbidity and mortality. Mammography is an effective ...
Highlights- Review of the automated detection/classification of breast cancer in mammograms.
- Comprehensive comparison of the effectiveness of various dissimilar approaches.
- Overview of studies utilizing sequential mammograms for increased ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© 2024 – The authors. Published by IOS Press.
This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher
IOS Press
Netherlands
Publication History
Published: 31 May 2024
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 26 Dec 2024