ArticleInternational journal of computer assisted radiology and surgery2019
Tissue classification of oncologic esophageal resectates based on hyperspectral data.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Clinical applications of hyperspectral imaging in gastroenterology and hepatology: A systematic review.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2026Pooled it
- Real-Time High-Definition Hyperspectral Endoscopy via Spatial-Temporal Low-Frequency-Stochastic Spectral Encoding.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Classification of esophageal cancer by using hyperspectral data.International journal of computer assisted radiology and surgery · 2026Article
- Detection of flap malperfusion after microsurgical tissue reconstruction using hyperspectral imaging and machine learning.Scientific reports · 2025Article
- Current Role of Artificial Intelligence in the Management of Esophageal Cancer.Journal of clinical medicine · 2025Review
- Intraoral perfusion assessment using endoscopic hyperspectral imaging (EHSI)- first description of a novel approach.Clinical oral investigations · 2025Article
- Proto-DS: A Self-Supervised Learning-Based Nondestructive Testing Approach for Food Adulteration with Imbalanced Hyperspectral Data.Foods (Basel, Switzerland) · 2024Article
- Separating Surface Reflectance from Volume Reflectance in Medical Hyperspectral Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- A proof of concept for microcirculation monitoring using machine learning based hyperspectral imaging in critically ill patients: a monocentric observational study.Critical care (London, England) · 2024Observational
- Hyperspectral Imaging Detects Clitoral Vascular Issues in Gender-Affirming Surgery.Diagnostics (Basel, Switzerland) · 2024Article
- Targeted multispectral filter array design for the optimization of endoscopic cancer detection in the gastrointestinal tract.Journal of biomedical optics · 2024Article
- Computer-aided endoscopic diagnostic system modified with hyperspectral imaging for the classification of esophageal neoplasms.Frontiers in oncology · 2024Article
- Self-supervised learning for gastritis detection with gastric X-ray images.International journal of computer assisted radiology and surgery · 2023Article
- Artificial intelligence in esophageal cancer diagnosis and treatment: where are we now?-a narrative review.Annals of translational medicine · 2023Review
- Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging.Biomedical optics express · 2023Article
- HeiPorSPECTRAL - the Heidelberg Porcine HyperSPECTRAL Imaging Dataset of 20 Physiological Organs.Scientific data · 2023Article
- Deep Learning in Medical Hyperspectral Images: A Review.Sensors (Basel, Switzerland) · 2022Review
- Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging.Journal of biomedical optics · 2022Article
- Intelligent Identification of Early Esophageal Cancer by Band-Selective Hyperspectral Imaging.Cancers · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
purposeEsophageal carcinoma is the eighth most common cancer worldwide. Esophageal resection with gastric pull-up is a potentially curative therapeutic option. After this procedure, the specimen is examined by the pathologist to confirm complete removal of the cancer. An intraoperative analysis of the resectate would be less time-consuming and therefore improve patient safety.
methodsHyperspectral imaging (HSI) is a relatively new modality, which has shown promising results for the detection of tumors. Automatic approaches could support the surgeon in the visualization of tumor margins. Therefore, we evaluated four supervised classification algorithms: random forest, support vector machines (SVM), multilayer perceptron, and k-nearest neighbors to differentiate malignant from healthy tissue based on HSI recordings of esophago-gastric resectates in 11 patients.
resultsThe best performances were obtained with a cancerous tissue detection of 63% sensitivity and 69% specificity with the SVM. In a leave-one patient-out cross-validation, the classification showed larger performance differences according to the patient data used. In less than 1 s, data classification and visualization was shown.
conclusionIn this work, we successfully tested several classification algorithms for the automatic detection of esophageal carcinoma in resected tissue. A larger data set and a combination of several methods would probably increase the performance. Moreover, the implementation of software tools for intraoperative tumor boundary visualization will further support the surgeon during oncologic operations.
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