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Eur Radiol Exp. 2019 Apr 27;3(1):18. doi: 10.1186/s41747-019-0096-3.

Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features.

Author information

1
Computational Imaging Research Laboratory, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
2
Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria.
3
Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
4
MR Center of Excellence, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria.
5
Department of Radiologic Technology, Carinthia University of Applied Sciences, Klagenfurt, Austria.
6
Department of Pathology, Medical University Vienna, 1090, Vienna, Austria.
7
Department of Surgery, Medical University Vienna, 1090, Vienna, Austria.
8
Computational Imaging Research Laboratory, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria. georg.langs@meduniwien.ac.at.

Abstract

BACKGROUND:

Multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) shows clinical potential for detection and classification of breast lesions. Yet, the contribution of features for computer-aided segmentation and diagnosis (CAD) need to be better understood. We proposed a data-driven machine learning approach for a CAD system combining dynamic contrast-enhanced (DCE)-MRI, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose (18F-FDG)-PET.

METHODS:

The CAD incorporated a random forest (RF) classifier combined with mpPET/MRI intensity-based features for lesion segmentation and shape features, kinetic and spatio-temporal texture features, for lesion classification. The CAD pipeline detected and segmented suspicious regions and classified lesions as benign or malignant. The inherent feature selection method of RF and alternatively the minimum-redundancy-maximum-relevance feature ranking method were used.

RESULTS:

In 34 patients, we report a detection rate of 10/12 (83.3%) and 22/22 (100%) for benign and malignant lesions, respectively, a Dice similarity coefficient of 0.665 for segmentation, and a classification performance with an area under the curve at receiver operating characteristics analysis of 0.978, a sensitivity of 0.946, and a specificity of 0.936. Segmentation but not classification performance of DCE-MRI improved with information from DWI and FDG-PET. Feature ranking revealed that kinetic and spatio-temporal texture features had the highest contribution for lesion classification. 18F-FDG-PET and morphologic features were less predictive.

CONCLUSION:

Our CAD enables the assessment of the relevance of mpPET/MRI features on segmentation and classification accuracy. It may aid as a novel computational tool for exploring different modalities/features and their contributions for the detection and classification of breast lesions.

KEYWORDS:

Breast neoplasms; Diagnosis (computer-assisted); Machine learning; Magnetic resonance imaging; Positron-emission tomography

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