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Breast Cancer Mass Detection in DCE–MRI Using Deep-Learning Features Followed by Discrimination of Infiltrative vs. In Situ Carcinoma through a Machine-Learning Approach
- Source :
- Applied Sciences, Volume 10, Issue 17, Applied Sciences, Vol 10, Iss 6109, p 6109 (2020)
- Publication Year :
- 2020
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2020.
-
Abstract
- Breast cancer is the leading cause of cancer deaths worldwide in women. This aggressive tumor can be categorized into two main groups&mdash<br />in situ and infiltrative, with the latter being the most common malignant lesions. The current use of magnetic resonance imaging (MRI) was shown to provide the highest sensitivity in the detection and discrimination between benign vs. malignant lesions, when interpreted by expert radiologists. In this article, we present the prototype of a computer-aided detection/diagnosis (CAD) system that could provide valuable assistance to radiologists for discrimination between in situ and infiltrating tumors. The system consists of two main processing levels&mdash<br />(1) localization of possibly tumoral regions of interest (ROIs) through an iterative procedure based on intensity values (ROI Hunter), followed by a deep-feature extraction and classification method for false-positive rejection<br />and (2) characterization of the selected ROIs and discrimination between in situ and invasive tumor, consisting of Radiomics feature extraction and classification through a machine-learning algorithm. The CAD system was developed and evaluated using a DCE&ndash<br />MRI image database, containing at least one confirmed mass per image, as diagnosed by an expert radiologist. When evaluating the accuracy of the ROI Hunter procedure with respect to the radiologist-drawn boundaries, sensitivity to mass detection was found to be 75%. The AUC of the ROC curve for discrimination between in situ and infiltrative tumors was 0.70.
- Subjects :
- In situ
infiltrative breast cancer
02 engineering and technology
lcsh:Technology
030218 nuclear medicine & medical imaging
lcsh:Chemistry
0302 clinical medicine
Breast cancer
Segmentation
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
Instrumentation
lcsh:QH301-705.5
oncology_oncogenics
Fluid Flow and Transfer Processes
medicine.diagnostic_test
General Engineering
food and beverages
lcsh:QC1-999
Computer Science Applications
machine learning
020201 artificial intelligence & image processing
Radiology
Radiomic
In situ breast cancer
medicine.medical_specialty
Feature extraction
Infiltrative breast cancer
03 medical and health sciences
breast cancer
Machine learning
Carcinoma
medicine
Radiomics
business.industry
lcsh:T
Process Chemistry and Technology
Deep learning
fungi
segmentation
Cancer
deep learning
Magnetic resonance imaging
medicine.disease
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
Artificial intelligence
business
lcsh:Engineering (General). Civil engineering (General)
in situ breast cancer
lcsh:Physics
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Database :
- OpenAIRE
- Journal :
- Applied Sciences
- Accession number :
- edsair.doi.dedup.....263ed03b362f0c62a7b5f9dc271f4348
- Full Text :
- https://doi.org/10.3390/app10176109