A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors
| العنوان: | A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors |
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| المؤلفون: | Mohamed Shehata, Rasha T. Abouelkheir, Ahmed Shaffie, Ayman El-Baz, Reem Salim, Ahmed Alksas, Ahmed Abdel Khalek Abdel Razek, Hadil Abu Khalifeh, Ahmed Soliman, Mohammed Ghazal, Norah Saleh Alghamdi, Ahmed Elmahdy |
| المصدر: | Sensors, Vol 21, Iss 4928, p 4928 (2021) Sensors Volume 21 Issue 14 Sensors (Basel, Switzerland) |
| بيانات النشر: | MDPI AG, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Adult, Male, medicine.medical_specialty, renal cell carcinoma, Angiomyolipoma, Adolescent, TP1-1185, Malignancy, Biochemistry, Article, 030218 nuclear medicine & medical imaging, Analytical Chemistry, Diagnosis, Differential, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Renal cell carcinoma, morphology, medicine, Humans, RC-CAD, Diagnosis, Computer-Assisted, Electrical and Electronic Engineering, Carcinoma, Renal Cell, Instrumentation, functionality, Aged, Aged, 80 and over, Artificial neural network, business.industry, Chemical technology, Cancer, Middle Aged, medicine.disease, CE-CT, Kidney Neoplasms, Atomic and Molecular Physics, and Optics, Random forest, Support vector machine, 030220 oncology & carcinogenesis, Multilayer perceptron, Female, Radiology, business, texture |
| الوصف: | 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. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1424-8220 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::83c45062ec2e84a6eac1a5f44075cb62Test https://www.mdpi.com/1424-8220/21/14/4928Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....83c45062ec2e84a6eac1a5f44075cb62 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14248220 |
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