A New Framework for Precise Identification of Prostatic Adenocarcinoma

Sensors

Ali Mahmoud¹  Ayman El-Baz¹  Ahmed Alksas¹  Mohamed Shehata¹  H. Arafat Ali²  Nahla B. Abdel-Hamid²  Labib M. Labib²  Sarah M. Ayyad²  Moumen El-Melegy³  Mohammed Ghazal⁴  Mohamed Abou El-Ghar⁵  Mohamed A. Badawy⁵ 

[1] BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA;Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt;Department of Electrical Engineering, Assiut University, Assiut 71511, Egypt;Department of Electrical and Computer Engineering, College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt;

关键词: prostate cancer;    MRI;    texture analysis;    shape features;    functional features;    computer-aided diagnosis;   

DOI  :  10.3390/s22051848

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown