A New Framework for Precise Identification of Prostatic Adenocarcinoma
Prostate cancer, which is also known as prostatic adenocarcinoma, is an unconstrained growth of epithelial cells in the prostate and has become one of the leading causes of cancer-related death worldwide. The survival of patients with prostate cancer relies on detection at an early, treatable stage....
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MDPI AG
2022-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/5/1848 |
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| author | Sarah M. Ayyad Mohamed A. Badawy Mohamed Shehata Ahmed Alksas Ali Mahmoud Mohamed Abou El-Ghar Mohammed Ghazal Moumen El-Melegy Nahla B. Abdel-Hamid Labib M. Labib H. Arafat Ali Ayman El-Baz |
| author_facet | Sarah M. Ayyad Mohamed A. Badawy Mohamed Shehata Ahmed Alksas Ali Mahmoud Mohamed Abou El-Ghar Mohammed Ghazal Moumen El-Melegy Nahla B. Abdel-Hamid Labib M. Labib H. Arafat Ali Ayman El-Baz |
| author_sort | Sarah M. Ayyad |
| collection | DOAJ |
| description | Prostate cancer, which is also known as prostatic adenocarcinoma, is an unconstrained growth of epithelial cells in the prostate and has become one of the leading causes of cancer-related death worldwide. The survival of patients with prostate cancer relies on detection at an early, treatable stage. In this paper, we introduce a new comprehensive framework to precisely differentiate between malignant and benign prostate cancer. This framework proposes a noninvasive computer-aided diagnosis system that integrates two imaging modalities of MR (diffusion-weighted (DW) and T2-weighted (T2W)). For the first time, it utilizes the combination of functional features represented by apparent diffusion coefficient (ADC) maps estimated from DW-MRI for the whole prostate in combination with texture features with its first- and second-order representations, extracted from T2W-MRIs of the whole prostate, and shape features represented by spherical harmonics constructed for the lesion inside the prostate and integrated with PSA screening results. The dataset presented in the paper includes 80 biopsy confirmed patients, with a mean age of 65.7 years (43 benign prostatic hyperplasia, 37 prostatic carcinomas). Experiments were conducted using different well-known machine learning approaches including support vector machines (SVM), random forests (RF), decision trees (DT), and linear discriminant analysis (LDA) classification models to study the impact of different feature sets that lead to better identification of prostatic adenocarcinoma. Using a leave-one-out cross-validation approach, the diagnostic results obtained using the SVM classification model along with the combined feature set after applying feature selection (88.75% accuracy, 81.08% sensitivity, 95.35% specificity, and 0.8821 AUC) indicated that the system’s performance, after integrating and reducing different types of feature sets, obtained an enhanced diagnostic performance compared with each individual feature set and other machine learning classifiers. In addition, the developed diagnostic system provided consistent diagnostic performance using 10-fold and 5-fold cross-validation approaches, which confirms the reliability, generalization ability, and robustness of the developed system. |
| first_indexed | 2024-03-09T20:21:42Z |
| format | Article |
| id | doaj.art-cf23b3fc6f2147508525c6036054e69b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T20:21:42Z |
| publishDate | 2022-02-01 |
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| spelling | doaj.art-cf23b3fc6f2147508525c6036054e69b2023-11-23T23:47:11ZengMDPI AGSensors1424-82202022-02-01225184810.3390/s22051848A New Framework for Precise Identification of Prostatic AdenocarcinomaSarah M. Ayyad0Mohamed A. Badawy1Mohamed Shehata2Ahmed Alksas3Ali Mahmoud4Mohamed Abou El-Ghar5Mohammed Ghazal6Moumen El-Melegy7Nahla B. Abdel-Hamid8Labib M. Labib9H. Arafat Ali10Ayman El-Baz11Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, EgyptRadiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, EgyptBioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USABioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USABioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USARadiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, EgyptDepartment of Electrical and Computer Engineering, College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab EmiratesDepartment of Electrical Engineering, Assiut University, Assiut 71511, EgyptComputers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, EgyptComputers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, EgyptComputers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, EgyptBioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USAProstate cancer, which is also known as prostatic adenocarcinoma, is an unconstrained growth of epithelial cells in the prostate and has become one of the leading causes of cancer-related death worldwide. The survival of patients with prostate cancer relies on detection at an early, treatable stage. In this paper, we introduce a new comprehensive framework to precisely differentiate between malignant and benign prostate cancer. This framework proposes a noninvasive computer-aided diagnosis system that integrates two imaging modalities of MR (diffusion-weighted (DW) and T2-weighted (T2W)). For the first time, it utilizes the combination of functional features represented by apparent diffusion coefficient (ADC) maps estimated from DW-MRI for the whole prostate in combination with texture features with its first- and second-order representations, extracted from T2W-MRIs of the whole prostate, and shape features represented by spherical harmonics constructed for the lesion inside the prostate and integrated with PSA screening results. The dataset presented in the paper includes 80 biopsy confirmed patients, with a mean age of 65.7 years (43 benign prostatic hyperplasia, 37 prostatic carcinomas). Experiments were conducted using different well-known machine learning approaches including support vector machines (SVM), random forests (RF), decision trees (DT), and linear discriminant analysis (LDA) classification models to study the impact of different feature sets that lead to better identification of prostatic adenocarcinoma. Using a leave-one-out cross-validation approach, the diagnostic results obtained using the SVM classification model along with the combined feature set after applying feature selection (88.75% accuracy, 81.08% sensitivity, 95.35% specificity, and 0.8821 AUC) indicated that the system’s performance, after integrating and reducing different types of feature sets, obtained an enhanced diagnostic performance compared with each individual feature set and other machine learning classifiers. In addition, the developed diagnostic system provided consistent diagnostic performance using 10-fold and 5-fold cross-validation approaches, which confirms the reliability, generalization ability, and robustness of the developed system.https://www.mdpi.com/1424-8220/22/5/1848prostate cancerMRItexture analysisshape featuresfunctional featurescomputer-aided diagnosis |
| spellingShingle | Sarah M. Ayyad Mohamed A. Badawy Mohamed Shehata Ahmed Alksas Ali Mahmoud Mohamed Abou El-Ghar Mohammed Ghazal Moumen El-Melegy Nahla B. Abdel-Hamid Labib M. Labib H. Arafat Ali Ayman El-Baz A New Framework for Precise Identification of Prostatic Adenocarcinoma Sensors prostate cancer MRI texture analysis shape features functional features computer-aided diagnosis |
| title | A New Framework for Precise Identification of Prostatic Adenocarcinoma |
| title_full | A New Framework for Precise Identification of Prostatic Adenocarcinoma |
| title_fullStr | A New Framework for Precise Identification of Prostatic Adenocarcinoma |
| title_full_unstemmed | A New Framework for Precise Identification of Prostatic Adenocarcinoma |
| title_short | A New Framework for Precise Identification of Prostatic Adenocarcinoma |
| title_sort | new framework for precise identification of prostatic adenocarcinoma |
| topic | prostate cancer MRI texture analysis shape features functional features computer-aided diagnosis |
| url | https://www.mdpi.com/1424-8220/22/5/1848 |
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