Next Article in Journal
Crack Detection in Images of Masonry Using CNNs
Next Article in Special Issue
Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review
Previous Article in Journal
Deep-Learning-Based Multimodal Emotion Classification for Music Videos
Previous Article in Special Issue
Ambient Healthcare Approach with Hybrid Whale Optimization Algorithm and Naïve Bayes Classifier
Article

A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors

1
BioImaging Lab, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
2
Department of Radiology, Urology and Nephrology Center, University of Mansoura, Mansoura 35516, Egypt
3
College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
4
Department of Radiology, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt
5
College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Hyungsoon Im
Sensors 2021, 21(14), 4928; https://doi.org/10.3390/s21144928
Received: 8 June 2021 / Revised: 9 July 2021 / Accepted: 17 July 2021 / Published: 20 July 2021
(This article belongs to the Special Issue Computer Aided Diagnosis Sensors)
Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Informed consent was obtained from a total of 140 biopsy-proven patients to participate in the study (male = 72 and female = 68, age range = 15 to 87 years). There were 70 patients who had RCC (40 clear cell RCC (ccRCC), 30 nonclear cell RCC (nccRCC)), while the other 70 had benign angiomyolipoma tumors. Contrast-enhanced computed tomography (CE-CT) images were acquired, and renal tumors were segmented for all patients to allow the extraction of discriminating imaging features. The RC-CAD system incorporates the following major steps: (i) applying a new parametric spherical harmonic technique to estimate the morphological features, (ii) modeling a novel angular invariant gray-level co-occurrence matrix to estimate the textural features, and (iii) constructing wash-in/wash-out slopes to estimate the functional features by quantifying enhancement variations across different CE-CT phases. These features were subsequently combined and processed using a two-stage multilayer perceptron artificial neural network (MLP-ANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype as well. Using the combined features and a leave-one-subject-out cross-validation approach, the developed RC-CAD system achieved a sensitivity of 95.3%±2.0%, a specificity of 99.9%±0.4%, and Dice similarity coefficient of 0.98±0.01 in differentiating malignant from benign tumors, as well as an overall accuracy of 89.6%±5.0% in discriminating ccRCC from nccRCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The obtained results using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, relational functional gradient boosting, etc.). Hence, integrating morphological, textural, and functional features enhances the diagnostic performance, making the proposal a reliable noninvasive diagnostic tool for renal tumors. View Full-Text
Keywords: renal cell carcinoma; CE-CT; morphology; texture; functionality; RC-CAD renal cell carcinoma; CE-CT; morphology; texture; functionality; RC-CAD
Show Figures

Figure 1

MDPI and ACS Style

Shehata, M.; Alksas, A.; Abouelkheir, R.T.; Elmahdy, A.; Shaffie, A.; Soliman, A.; Ghazal, M.; Abu Khalifeh, H.; Salim, R.; Abdel Razek, A.A.K.; Alghamdi, N.S.; El-Baz, A. A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors. Sensors 2021, 21, 4928. https://doi.org/10.3390/s21144928

AMA Style

Shehata M, Alksas A, Abouelkheir RT, Elmahdy A, Shaffie A, Soliman A, Ghazal M, Abu Khalifeh H, Salim R, Abdel Razek AAK, Alghamdi NS, El-Baz A. A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors. Sensors. 2021; 21(14):4928. https://doi.org/10.3390/s21144928

Chicago/Turabian Style

Shehata, Mohamed, Ahmed Alksas, Rasha T. Abouelkheir, Ahmed Elmahdy, Ahmed Shaffie, Ahmed Soliman, Mohammed Ghazal, Hadil Abu Khalifeh, Reem Salim, Ahmed A.K. Abdel Razek, Norah S. Alghamdi, and Ayman El-Baz. 2021. "A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors" Sensors 21, no. 14: 4928. https://doi.org/10.3390/s21144928

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop