ArticleWorld journal of urology2024
Deep learning using contrast-enhanced ultrasound images to predict the nuclear grade of clear cell renal cell carcinoma.
Article in World journal of urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 7 citations in OpenAlex.
- Preoperative identification of adverse pathology in clinically localized clear cell renal cell carcinoma: development and external validation of a contrast-enhanced ultrasound-based model.Abdominal radiology (New York) · 2026Article
- Artificial intelligence in urological malignancy diagnosis and prognosis: current status and future prospects.The Canadian journal of urology · 2026Review
- State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025Review
- Computer vision assisted deep transfer learning model for accurate grading of renal cell carcinoma from kidney histopathology images.Scientific reports · 2025Article
- Feasibility of intraoperative Doppler ultrasound for intersegmental plane identification in pulmonary segmentectomy: a retrospective preliminary validation study.Journal of thoracic disease · 2025Article
- Deep learning algorithm for pathological grading of renal cell carcinoma based on multi-phase enhanced CT.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Multimodal deep learning with MUF-net for noninvasive WHO/ISUP grading of renal cell carcinoma using CEUS and B-mode ultrasound.Frontiers in physiology · 2025Article
- Advances in the clinical diagnosis of lung cancer using contrast-enhanced ultrasound.Frontiers in medicine · 2025Review
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo assess the effectiveness of a deep learning model using contrastenhanced ultrasound (CEUS) images in distinguishing between low-grade (grade I and II) and high-grade (grade III and IV) clear cell renal cell carcinoma (ccRCC).
methodsA retrospective study was conducted using CEUS images of 177 Fuhrmangraded ccRCCs (93 low-grade and 84 high-grade) from May 2017 to December 2020. A total of 6412 CEUS images were captured from the videos and normalized for subsequent analysis. A deep learning model using the RepVGG architecture was proposed to differentiate between low-grade and high-grade ccRCC. The model's performance was evaluated based on sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Class activation mapping (CAM) was used to visualize the specific areas that contribute to the model's predictions.
resultsFor discriminating high-grade ccRCC from low-grade, the deep learning model achieved a sensitivity of 74.8%, specificity of 79.1%, accuracy of 77.0%, and an AUC of 0.852 in the test set.
conclusionThe deep learning model based on CEUS images can accurately differentiate between low-grade and high-grade ccRCC in a non-invasive manner.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.