ArticleMolecules (Basel, Switzerland)2019
Detection of the BRAF V600E Mutation in Colorectal Cancer by NIR Spectroscopy in Conjunction with Counter Propagation Artificial Neural Network.
Article in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Visualizing knowledge evolution trends and research hotspots of artificial intelligence in colorectal cancer: A bibliometric analysis.Frontiers in oncology · 2022Pooled it
- Review
- Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review.Diagnostics (Basel, Switzerland) · 2024Review
- Review
- Detection of colorectal adenomas using artificial intelligence models in patients with chronic hepatitis C.World journal of hepatology · 2023Article
- Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect.Frontiers in medicine · 2023Review
- Article
- Review
- Review
- Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification.Frontiers in oncology · 2022Review
- Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era.Current oncology (Toronto, Ont.) · 2021Review
- An artificial neural network model predicting pathologic nodal metastases in clinical stage I-II esophageal squamous cell carcinoma patients.Journal of thoracic disease · 2020Article
- Application of artificial intelligence to the diagnosis and therapy of colorectal cancer.American journal of cancer research · 2020Review
- Advances in Near-Infrared Spectroscopy and Related Computational Methods.Molecules (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes a sensitive, sample preparation-free, rapid, and low-cost method for the detection of the B-rapidly accelerated fibrosarcoma (BRAF) gene mutation involving a substitution of valine to glutamic acid at codon 600 (V600E) in colorectal cancer (CRC) by near-infrared (NIR) spectroscopy in conjunction with counter propagation artificial neural network (CP-ANN). The NIR spectral data from 104 paraffin-embedded CRC tissue samples consisting of an equal number of the BRAF V600E mutant and wild-type ones calibrated and validated the CP-ANN model. As a result, the CP-ANN model had the classification accuracy of calibration (CAC) 98.0%, cross-validation (CACV) 95.0% and validation (CAV) 94.4%. When used to detect the BRAF V600E mutation in CRC, the model showed a diagnostic sensitivity of 100.0%, a diagnostic specificity of 87.5%, and a diagnostic accuracy of 93.8%. Moreover, this method was proven to distinguish the BRAF V600E mutant from the wild type based on intrinsic differences by using a total of 312 CRC tissue samples paraffin-embedded, deparaffinized, and stained. The novel method can be used for the auxiliary diagnosis of the BRAF V600E mutation in CRC. This work can expand the application of NIR spectroscopy in the auxiliary diagnosis of gene mutation in human cancer.
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