ArticleCommunications engineering2024
Automatic renal carcinoma biopsy guidance using forward-viewing endoscopic optical coherence tomography and deep learning.
Article in Communications engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Deep learning fusion of multi-channel imaging from polarization-sensitive optical coherence tomography.Biomedical optics express · 2026Article
- Prevention of Gastric Cancer.Medicina (Kaunas, Lithuania) · 2026Review
- Percutaneous nephrostomy guidance by a convolutional-neural-network-based optical coherence tomography endoscope.Communications engineering · 2026Article
- Evaluation of radio frequency ablation in human left atrial tissues for atrial fibrillation using optical coherence tomography.Biomedical optics express · 2025Article
- Octascope: A Lightweight Pre-Trained Model for Optical Coherence Tomography.IEEE access : practical innovations, open solutions · 2025Article
- Microfluidics and molecular diagnostics in renal cell carcinoma: advances, challenges, and future directions.Frontiers in oncology · 2025Review
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Percutaneous renal biopsy is commonly used for kidney cancer diagnosis. However, the biopsy procedure remains challenging in sampling accuracy. Here we introduce a forward-viewing optical coherence tomography probe for differentiating tumor and normal tissues, aiming at precise biopsy guidance. Totally, ten human kidney samples, nine of which had malignant renal carcinoma and one had benign oncocytoma, were used for system evaluation. Based on their distinct imaging features, carcinoma could be efficiently distinguished from normal renal tissues. Additionally, oncocytoma could be differentiated from carcinoma. We developed convolutional neural networks for tissue recognition. Compared to the conventional attenuation coefficient method, convolutional neural network models provided more accurate carcinoma predictions. These models reached a tissue recognition accuracy of 99.1% on a hold-out set of four kidney samples. Furthermore, they could efficiently distinguish oncocytoma from carcinoma. In conclusion, our convolutional neural network-aided endoscopic imaging platform could enhance carcinoma diagnosis during percutaneous renal biopsy procedures.
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Registered trials
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 Socratic graph.