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Multi-marker quantitative radiomics for mass characterization in dedicated breast CT imaging
- Source :
- Medical Physics, Medical Physics, 48, 313-328, Medical physics, vol 48, iss 1, Medical Physics, 48, 1, pp. 313-328
- Publication Year :
- 2020
-
Abstract
- Contains fulltext : 232918.pdf (Publisher’s version ) (Open Access) PURPOSE: To develop and evaluate the diagnostic performance of an algorithm for multi-marker radiomic-based classification of breast masses in dedicated breast computed tomography (bCT) images. METHODS: Over 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, and margin sharpness were developed and implemented. These included well-established texture and shape feature descriptors, which were supplemented with additional approaches for contour irregularity quantification, spicule and lobe detection, characterization of degree of infiltration, and differences in peritumoral compartments. All descriptors were extracted from a training set of 202 bCT masses (133 benign and 69 malignant), and their individual diagnostic performance was investigated in terms of area under the receiver operating characteristics (ROC) curve (AUC) of single-feature-based linear discriminant analysis (LDA) classifiers. Subsequently, the most relevant descriptors were selected through a multiple-step feature selection process (including stability analysis, statistical significance, evaluation of feature interaction, and dimensionality reduction), and used to develop a final LDA radiomic model for classification of benign and malignant masses, which was then tested on an independent test set of 82 cases (45 benign and 37 malignant). RESULTS: The majority of the individual radiomic descriptors showed, on the training set, an AUC value deriving from a linear decision boundary higher than 0.65, with the lower limit of the associated 95% confidence interval (C.I.) not overlapping with random chance (AUC = 0.5). The final LDA radiomic model resulted in a test set AUC of 0.90 (95% C.I. 0.80-0.96). CONCLUSIONS: The proposed multi-marker radiomic approach achieved high diagnostic accuracy in bCT mass classification, using a radiomic signature based on different feature types. While future studies with larger datasets are needed to further validate these results, quantitative radiomics applied to bCT shows potential to improve the breast cancer diagnosis pipeline.
- Subjects :
- Computer science
Computed tomography
030218 nuclear medicine & medical imaging
0302 clinical medicine
Radiomics
Margin (machine learning)
Breast
Tomography
Research Articles
Cancer
screening and diagnosis
medicine.diagnostic_test
General Medicine
Women's cancers Radboud Institute for Health Sciences [Radboudumc 17]
X-Ray Computed
Other Physical Sciences
Detection
Nuclear Medicine & Medical Imaging
medicine.anatomical_structure
Feature (computer vision)
radiomics
030220 oncology & carcinogenesis
Biomedical Imaging
Algorithms
4.2 Evaluation of markers and technologies
Research Article
precision medicine
Oncology and Carcinogenesis
Biomedical Engineering
Feature selection
Breast Neoplasms
computer‐
03 medical and health sciences
Breast cancer
breast cancer
QUANTITATIVE IMAGING AND IMAGE PROCESSING
medicine
Humans
Receiver operating characteristic
business.industry
Dimensionality reduction
Pattern recognition
breast CT
medicine.disease
Linear discriminant analysis
Lobe
aided diagnosis
computer‐aided diagnosis
Good Health and Well Being
ROC Curve
Computer-aided diagnosis
Test set
computer-aided diagnosis
Artificial intelligence
business
Tomography, X-Ray Computed
Subjects
Details
- ISSN :
- 24734209 and 00942405
- Volume :
- 48
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Medical physics
- Accession number :
- edsair.doi.dedup.....612b34a692ca213469f748499d04c6ff