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A hierarchical classifier based on human blood plasma fluorescence for non-invasive colorectal cancer screening
- Source :
- Artificial Intelligence in Medicine. 82:1-10
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- Colorectal cancer (CRC) a leading cause of death by cancer, and screening programs for its early identification are at the heart of the increasing survival rates. To motivate population participation, non-invasive, accurate, scalable and cost-effective diagnosis methods are required. Blood fluorescence spectroscopy provides rich information that can be used for cancer identification. The main challenges in analyzing blood fluorescence data for CRC classification are related to its high dimensionality and inherent variability, especially when analyzing a small number of samples. In this paper, we present a hierarchical classification method based on plasma fluorescence to identify not only CRC, but also adenomas and other non-malignant colorectal findings that may require further medical investigation. A feature selection algorithm is proposed to deal with the high dimensionality and select discriminant fluorescence wavelengths. These are used to train a binary support vector machine (SVM) in the first level to identify the CRC samples. The remaining samples are then presented to a one-class SVM trained on healthy subjects to detect deviant samples, and thus non-malignant findings. This hierarchical design, together with the one class-SVM, aims to reduce the effects of small samples and high variability. Using a dataset analyzed in previous studies comprised of 12,341 wavelengths, we achieved much superior results. Sensitivity and specificity are 0.87 and 0.95 for CRC detection, and 0.60 and 0.79 for non-malignant findings, respectively. Compared to related work, the proposed method presented a better accuracy, required fewer features, and provides a unified approach that expands CRC detection to non-malignant findings.
- Subjects :
- 0301 basic medicine
Support Vector Machine
Computer science
Colorectal cancer
Population
Colonic Polyps
Medicine (miscellaneous)
Feature selection
Bioinformatics
Hierarchical classifier
Adenomatous Polyps
03 medical and health sciences
0302 clinical medicine
Predictive Value of Tests
Artificial Intelligence
Biomarkers, Tumor
medicine
Humans
education
Early Detection of Cancer
education.field_of_study
business.industry
Reproducibility of Results
Cancer
Pattern recognition
medicine.disease
Support vector machine
Identification (information)
Spectrometry, Fluorescence
030104 developmental biology
Colorectal cancer screening
Case-Control Studies
030211 gastroenterology & hepatology
Artificial intelligence
Colorectal Neoplasms
business
Subjects
Details
- ISSN :
- 09333657
- Volume :
- 82
- Database :
- OpenAIRE
- Journal :
- Artificial Intelligence in Medicine
- Accession number :
- edsair.doi.dedup.....17727c7d89f9db21a68e85e89368c753