Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.
Objectives: To develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed tomography (CECT) images.Methods: This institutional review board-approved retrospective study evaluated CECT in 315 patients...
| Publicado en: | European Radiology Vol. 30; no. 9; pp. 5183 - 5191 |
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| Autores principales: | , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145263116&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145263116 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Sep2020 vid: 30 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145263116 143970025 145263116 NLM32350661 145263116 10.1007/s00330-020-06787-9 NLM32350661 145263116 ppf: 5183 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion. aug: au: Zabihollahy, Fatemeh Schieda, Nicola Krishna, Satheesh Ukwatta, Eranga affil: Department of Systems and Computer Engineering, Carleton University, 339 Riversedge Crescent, K1V 0Y6, Ottawa, ON, Canada sug: subj: Tomography, X-Ray Computed Methods Adenoma Neoplasms, Adipose Tissue Kidney Neoplasms Carcinoma, Renal Cell Image Interpretation, Computer Assisted Methods Kidney Neoplasms Pathology Algorithms Diagnosis, Differential Carcinoma, Renal Cell Pathology Retrospective Design Contrast Media Neoplasms, Adipose Tissue Pathology Adenoma Pathology Human ab: Objectives: To develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed tomography (CECT) images.Methods: This institutional review board-approved retrospective study evaluated CECT in 315 patients with 77 benign (57 oncocytomas, and 20 fat-poor angiomyolipoma) and 238 malignant (RCC: 123 clear cell, 69 papillary, and 46 chromophobe subtypes) tumors identified consecutively between 2015 and 2017. We employed a decision fusion-based model to aggregate slice level predictions determined by convolutional neural network (CNN) via a majority voting system to evaluate renal masses on CECT. The CNN-based model was trained using 7023 slices with renal masses manually extracted from CECT images of 155 patients, cropped automatically around kidneys, and augmented artificially. We also examined the fully automated approach for renal mass evaluation on CECT. Moreover, a 3D CNN was trained and tested using the same datasets and the obtained results were compared with those acquired from slice-wise algorithms.Results: For differentiation of RCC versus benign solid masses, the semi-automated majority voting-based CNN algorithm achieved accuracy, precision, and recall of 83.75%, 89.05%, and 91.73% using 160 test cases, respectively. Fully automated pipeline yielded accuracy, precision, and recall of 77.36%, 85.92%, and 87.22% on the same test cases, respectively. 3D CNN reported accuracy, precision, and recall of 79.24%, 90.32%, and 84.21% using 160 test cases, respectively.Conclusions: A semi-automated majority voting CNN-based methodology enabled accurate classification of RCC from benign neoplasms among solid renal masses on CECT.Key Points: • Our proposed semi-automated majority voting CNN-based algorithm achieved accuracy of 83.75% for the diagnosis of RCC from benign solid renal masses on CECT images. • A fully automated CNN-based methodology classified solid renal masses with moderate accuracy of 77.36% using the same test images. • Employing 3D CNN-based methodology yielded slightly lower accuracy for renal mass classification compared with the semi- automated 2D CNN-based algorithm (79.24%). pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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