ArticleDiagnostics (Basel, Switzerland)2021
Automatic Recognition of Colon and Esophagogastric Cancer with Machine Learning and Hyperspectral Imaging.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 3 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 3 syntheses or guidelines pooled it, 66 citations in OpenAlex.
- 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
- Machine learning applications for early detection of esophageal cancer: a systematic review.BMC medical informatics and decision making · 2023Pooled it
- Hyperspectral Fingerprints of Abdominal and Pelvic Organs.Journal of imaging · 2026Article
- Enhancing esophageal cancer detection using a deep learning framework and a novel spectrum-aided vision enhancer for virtual narrow band imaging.Scientific reports · 2026Article
- Hyperspectral imaging for orthopedic surgery: lightweight AI enables real-time bone and cartilage segmentation.Medical & biological engineering & computing · 2026Article
- Classification of esophageal cancer by using hyperspectral data.International journal of computer assisted radiology and surgery · 2026Article
- From Image-Guided Surgery to Computer-Assisted Real-Time Diagnosis with Hyperspectral and Multispectral Imaging: A Systematic Review in Gynecologic Oncology.Diagnostics (Basel, Switzerland) · 2026Review
- From Innovation to Application: Can Emerging Imaging Techniques Transform Breast Cancer Diagnosis?Diagnostics (Basel, Switzerland) · 2025Review
- Exploring the role of preprocessing combinations in hyperspectral imaging for deep learning colorectal cancer detection.Scientific reports · 2025Article
- Toward real-time margin assessment in breast-conserving surgery with hyperspectral imaging.Scientific reports · 2025Article
- Precision Imaging for Early Detection of Esophageal Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review.APL bioengineering · 2024Review
- Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging.Bioengineering (Basel, Switzerland) · 2024Review
- Application of Hyperspectral Imaging and Multi-Module Joint Hierarchical Residual Network in Seed Cotton Foreign Fiber Recognition.Sensors (Basel, Switzerland) · 2024Article
- Separating Surface Reflectance from Volume Reflectance in Medical Hyperspectral Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Efficient colorectal polyp segmentation using wavelet transformation and AdaptUNet: A hybrid U-Net.Heliyon · 2024Article
- Evaluation of Spectrum-Aided Visual Enhancer (SAVE) in Esophageal Cancer Detection Using YOLO Frameworks.Diagnostics (Basel, Switzerland) · 2024Article
- Exploring the mystery of colon cancer from the perspective of molecular subtypes and treatment.Scientific reports · 2024Article
- Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
There are approximately 1.8 million diagnoses of colorectal cancer, 1 million diagnoses of stomach cancer, and 0.6 million diagnoses of esophageal cancer each year globally. An automatic computer-assisted diagnostic (CAD) tool to rapidly detect colorectal and esophagogastric cancer tissue in optical images would be hugely valuable to a surgeon during an intervention. Based on a colon dataset with 12 patients and an esophagogastric dataset of 10 patients, several state-of-the-art machine learning methods have been trained to detect cancer tissue using hyperspectral imaging (HSI), including Support Vector Machines (SVM) with radial basis function kernels, Multi-Layer Perceptrons (MLP) and 3D Convolutional Neural Networks (3DCNN). A leave-one-patient-out cross-validation (LOPOCV) with and without combining these sets was performed. The ROC-AUC score of the 3DCNN was slightly higher than the MLP and SVM with a difference of 0.04 AUC. The best performance was achieved with the 3DCNN for colon cancer and esophagogastric cancer detection with a high ROC-AUC of 0.93. The 3DCNN also achieved the best DICE scores of 0.49 and 0.41 on the colon and esophagogastric datasets, respectively. These scores were significantly improved using a patient-specific decision threshold to 0.58 and 0.51, respectively. This indicates that, in practical use, an HSI-based CAD system using an interactive decision threshold is likely to be valuable. Experiments were also performed to measure the benefits of combining the colorectal and esophagogastric datasets (22 patients), and this yielded significantly better results with the MLP and SVM models.
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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.