A New Computer-Aided Diagnostic (Cad) System For Precise Identification Of Renal Tumors
| Autor: | Mohammed Ghazal, Ahmed Elmahdy, Ayman El-Baz, Ahmed Abdel Khalek Abdel Razek, Hadil Abu Khalifeh, Ahmed Shaffie, Ahmed Alksas, Rasha T. Abouelkheir, Mohamed Shehata, Ahmed Soliman |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Angiomyolipoma business.industry Feature extraction Cancer CAD urologic and male genital diseases medicine.disease Malignancy 030218 nuclear medicine & medical imaging Random forest 03 medical and health sciences 0302 clinical medicine Renal cell carcinoma 030220 oncology & carcinogenesis medicine Radiology Stage (cooking) business |
| Zdroj: | ISBI |
| Popis: | Renal cell carcinoma (RCC) is the most common and aggressive renal cancer. Hence, early identification of RCC is essential to provide the proper management plan. In this paper, we develop a novel computer-aided diagnostic (CAD) system that integrates texture and functional features, extracted from contrast-enhanced computed tomography (CE-CT), to differentiate benign from malignant RCC renal tumors and identify malignancy sub-types. Our study includes renal tumors obtained from 105 biopsy-proven cases of which 70 were diagnosed as malignant tumors (clear cell RCC (ccRCC) =40 and non-clear cell RCC (nccRCC) =30) and 35 were diagnosed as benign tumors (angiomyolipoma (AML) =35). The proposed CAD system mainly consists of three steps: (i) preprocessing of grey images to obtain 3D segmented renal tumor objects; (ii) extracting different discriminating features (texture and functional) from segmented objects; and (iii) performing a two-stage classification process using different machine learning classifiers to obtain the final diagnosis of the renal tumor. In the first stage, the classification performance of the proposed CAD system was evaluated using the individual features along with a random forest machine learning classifier. Then, a weighted majority voting criteria was applied on the output class-membership to determine if the renal tumor is benign (AML) or malignant (RCC). In case of the latter, the second stage defines the sub-type of malignant tumor as ccRCC vs. nccRCC. Using a leave-one-subject-out cross-validation approach, the developed CAD system achieved 96.0% accuracy, 100% sensitivity, 89% specificity, and 0.97 F 1 score in the first classification stage and achieved 71.4% accuracy and 0.75 F 1 score in the second classification stage, respectively. These obtained results suggests that integrating first and second order texture features with functional features enhances the diagnostic performance of the developed CAD system making the developed a reliable noninvasive diagnostic tool for renal tumors. |
| Databáze: | OpenAIRE |
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