ArticleScientific reports2023
Assessment of hyperspectral imaging and CycleGAN-simulated narrowband techniques to detect early esophageal cancer.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Comparative efficacy and outcomes of different treatments for pathologically high-risk superficial esophageal cancer: a systematic review and network meta-analysis.Frontiers in oncology · 2026Pooled it
- Article
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- Perspective on the current state of hyperspectral/multispectral imaging for minimally invasive surgery.Journal of biomedical optics · 2026Review
- DGCNet: a clinical knowledge-driven model for the segmentation of reflux esophagitis in endoscopic images.Gastroenterology report · 2026Article
- Oesophageal cancer multi-disciplinary tool: a co-designed, externally validated, machine learning tool for oesophageal cancer decision making.EClinicalMedicine · 2025Article
- StarVasc: hyper-dimensional and spectral feature expansion for lightweight vascular enhancement.Journal of robotic surgery · 2025Article
- Snail family transcriptional repressor 1 radiosensitizes esophageal cancer via epithelial-mesenchymal transition signaling: From bioinformatics to integrated study.World journal of gastrointestinal oncology · 2025Article
- Assessing the Efficacy of the Spectrum-Aided Vision Enhancer (SAVE) to Detect Acral Lentiginous Melanoma, Melanoma In Situ, Nodular Melanoma, and Superficial Spreading Melanoma: Part II.Diagnostics (Basel, Switzerland) · 2025Article
- Evaluation of Band Selection for Spectrum-Aided Visual Enhancer (SAVE) for Esophageal Cancer Detection.Journal of Cancer · 2025Article
- Assessing the Efficacy of the Spectrum-Aided Vision Enhancer (SAVE) to Detect Acral Lentiginous Melanoma, Melanoma In Situ, Nodular Melanoma, and Superficial Spreading Melanoma.Diagnostics (Basel, Switzerland) · 2024Article
- Convolutional Neural Network Model for Intestinal Metaplasia Recognition in Gastric Corpus Using Endoscopic Image Patches.Diagnostics (Basel, Switzerland) · 2024Article
- Using Pathomics-Based Model for Predicting Positive Surgical Margins in Patients with Esophageal Squamous Cell Carcinoma: A Comparative Study of Decision Tree and Nomogram.International journal of general medicine · 2024Article
- Development and validation of esophageal cancer prediction model based on DeepSurv model: A cohort study.Science progressArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
The clinical signs and symptoms of esophageal cancer (EC) are often not discernible until the intermediate or advanced phases. The detection of EC in advanced stages significantly decreases the survival rate to below 20%. This study conducts a comparative analysis of the efficacy of several imaging techniques, including white light image (WLI), narrowband imaging (NBI), cycle-consistent adversarial network simulated narrowband image (CNBI), and hyperspectral imaging simulated narrowband image (HNBI), in the early detection of esophageal cancer (EC). In conjunction with Kaohsiung Armed Forces General Hospital, a dataset consisting of 1000 EC pictures was used, including 500 images captured using WLI and 500 images captured using NBI. The CycleGAN model was used to generate the CNBI dataset. Additionally, a novel method for HSI imaging was created with the objective of generating HNBI pictures. The evaluation of the efficacy of these four picture types in early detection of EC was conducted using three indicators: CIEDE2000, entropy, and the structural similarity index measure (SSIM). Results of the CIEDE2000, entropy, and SSIM analyses suggest that using CycleGAN to generate CNBI images and HSI model for creating HNBI images is superior in detecting early esophageal cancer compared to the use of conventional WLI and NBI techniques.
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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 OpenQuestion graph.