Further Signs in the Evaluation of Magnetic Resonance Mammography

Fischer, Dorothee; Wurdinger, Susanne; Boettcher, Joachim; Malich, Ansgar; Kaiser, Werner A.

doi:10.1097/01.rli.0000167138.52283.aa

Cited by 52 publications

(40 citation statements)

References 37 publications

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“…However, using the MIPT also has limitations. It affects the number of FPs negatively, as it is very sensitive to artifacts and noise, and may lead to overestimation of lesion size due to the ''blooming sign'' effect (31)(32)(33). Second, our system has a higher number of FP findings if compared with other academic software and to commercially available solutions (34).…”

Section: Discussionmentioning

confidence: 99%

Performance of a fully automatic lesion detection system for breast DCE‐MRI

Vignati

Giannini

Luca

et al. 2011

Magnetic Resonance Imaging

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Purpose: To describe and test a new fully automatic lesion detection system for breast DCE-MRI.Materials and Methods: Studies were collected from two institutions adopting different DCE-MRI sequences, one with and the other one without fat-saturation. The detection pipeline consists of (i) breast segmentation, to identify breast size and location; (ii) registration, to correct for patient movements; (iii) lesion detection, to extract contrast-enhanced regions using a new normalization technique based on the contrast-uptake of mammary vessels; (iv) false positive (FP) reduction, to exclude contrastenhanced regions other than lesions. Detection rate (number of system-detected malignant and benign lesions over the total number of lesions) and sensitivity (systemdetected malignant lesions over the total number of malignant lesions) were assessed. The number of FPs was also assessed.Results: Forty-eight studies with 12 benign and 53 malignant lesions were evaluated. Median lesion diameter was 6 mm (range, 5-15 mm) for benign and 26 mm (range, 5-75 mm) for malignant lesions. Detection rate was 58/65 (89%; 95% confidence interval [CI] 79%-95%) and sensitivity was 52/53 (98%; 95% CI 90%-99%). Mammary median FPs per breast was 4 (1st-3rd quartiles 3-7.25). Conclusion:The system showed promising results on MR datasets obtained from different scanners producing fatsat or non-fat-sat images with variable temporal and spatial resolution and could potentially be used for early diagnosis and staging of breast cancer to reduce reading time and to improve lesion detection. Further evaluation is needed before it may be used in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Performance of a fully automatic lesion detection system for breast DCE‐MRI

Vignati

Giannini

Luca

et al. 2011

Magnetic Resonance Imaging

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“…However, numerous other studies indicate that the high signal intensity in lesions on T2-w MRI is distinctive in the discrimination between benign and malignant lesions [67,68,69,70,71,72]. Few other studies conclude that T2-w features are promising diagnostic features for the classification of benign and malignant lesions [73,74,75]. Above all, it is standard clinical practice to use T2-w MRI for the diagnosis of cancer in breast MRI.…”

Section: Breast Mri Databasementioning

confidence: 99%

Multiple Instance Learning for Breast Cancer Magnetic Resonance Imaging

Maken

Gal

McClymont

et al. 2014

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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In this thesis we evaluate the efficacy of multiple instance learning (MIL) as a 'pure' machine learning approach for the diagnosis of breast cancer in magnetic resonance images (MRI). The traditional approach for the diagnosis of breast cancer is based on region-of-interest (ROI) based single instance learning (SIL). In the ROI-based SIL, the classification of benign and malignant lesions depends on the features, which are extracted from segmented ROIs. But, an accurate segmentation of a ROI is a challenging task due to poor signal-to-noise-ratio and faint edges due to partial volume effects. Therefore, variations in the segmentation of ROIs can affect the diagnostic outcome. MIL is a relatively new method in the supervised learning, where each sample is represented as a bag of instances. An image in the context of MIL can be considered as a bag of pixels, tiles, or ROIs, which correspond to instances. Here we apply tile-based MIL, where the diagnosis of breast cancer is based on tile-based features which do not require the segmentation of ROIs. Therefore, tile-based MIL has the potential to provide the classification of breast cancer without segmenting the ROIs. This is the motivation for the main objective of this thesis: to estimate the efficacy of tile-based MIL in the detection and the diagnosis of breast cancer MRI.We initially evaluate the potential of MIL for the detection and the diagnosis of breast cancer in anatomical T2-weighted MRI. In particular, we compare the performance of a MIL-based learner, i.e., citation-kNN (CkNN) against conventional kNN and a Random Forest classifier. We utilise both (generic) tile-based spatial features and (domain specific) ROI-based features. We perform experiments on two datasets consisting of 77 mass-like lesions and 129 both mass-like and non-mass-like lesions. The performance of CkNN as both a diagnostic and screening tool is evaluated using the area under the receiver operating characteristic curve (AUC), estimated over 10-fold cross validation. Results demonstrate that the tile-based CkNN has equivalent performance to the ROI-based classification. However, tile-based MIL has an advantage that it does not require the domain specific ROI-based features typically used in breast MRI. This not only has the potential to make the tile-based classification robust to inaccuracies in the delineation of suspicious lesions, but also makes it suitable for the detection of suspicious lesions prior to segmentation.Next, we investigate the performance of CkNN for the diagnosis of breast cancer using dynamic MRI. Specifically we use generic tile-based spatio-temporal features derived from T2-weighted MRI and T1-weighted dynamic contrast enhanced MRI. We utilise a discrete cosine transform and contrast enhancement models as feature extraction techniques. We compare the III performance of CkNN and kNN against a traditional approach based on bespoke ROI-based features using the 77 mass-like lesions. Empirical results show the equivalent classification performance of both ti...

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“…Static T 2 -weighted images provide complementary information for discriminating benign fibroadenoma from malignant cancers, as both expose similar enhancement patterns in the DCE T 1 -weighted images. Furthermore, several secondary signs such as edemas, which frequently accompany pathological tissue growth, are accentuated in T 2 -weighted images [2].…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis in Breast MRI: Do Adjunct Features Derived from T 2-weighted Images Improve Classification of Breast Masses?

et al. 2008

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Abstract. In the field of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer, current research efforts in computer-aided diagnosis (CADx) are mainly focused on the temporal series of T1-weighted images acquired during uptake of a contrast agent, processing morphological and kinetic information. Although static T2-weighted images are usually part of DCE-MRI protocols, they are seldom used in CADx systems. The aim of this work was to evaluate to what extent T2-weighted images provide complementary information to a CADx system, improving its performance for the task of discriminating benign breast masses from life-threatening carcinomas. In a preliminary study considering 64 masses, inclusion of lesion features derived from T2-weighted images increased the classification performance from Az=0.94 to Az=0.99.

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Further Signs in the Evaluation of Magnetic Resonance Mammography

Cited by 52 publications

References 37 publications

Performance of a fully automatic lesion detection system for breast DCE‐MRI

Performance of a fully automatic lesion detection system for breast DCE‐MRI

Multiple Instance Learning for Breast Cancer Magnetic Resonance Imaging

Computer-Aided Diagnosis in Breast MRI: Do Adjunct Features Derived from T 2-weighted Images Improve Classification of Breast Masses?

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