Combination of model-free and model-based analysis of dynamic contrast enhanced MRI for breast cancer diagnosis

Eyal, Erez; Furman‐Haran, Edna; Badikhi, Daria; Kelcz, Frederick; Degani, Hadassa

doi:10.1117/12.770192

Cited by 5 publications

(7 citation statements)

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“…Such algorithms are used for characterizing intrinsic properties of the raw data, without the need to assume a model and incorporate presumed or experimental parameters. Preliminary accounts on the application of the widely used modelfree principal component analysis (PCA) (25) showed that it can be applied for computational postprocessing fat suppression (26), for segmentation of the enhancement patterns to regions with different enhancement patterns (27)(28)(29), and for extracting diagnostic information from breast DCE-MRI datasets (29). The application of the model-free independent component analysis (ICA) to decompose breast DCE datasets was demonstrated on a limited number of lesions (30,31).…”

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confidence: 99%

Principal component analysis of breast DCE‐MRI adjusted with a model‐based method

Eyal

Badikhi

Furman‐Haran

et al. 2009

Magnetic Resonance Imaging

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Purpose: To investigate a fast, objective, and standardized method for analyzing breast dynamic contrastenhanced magnetic resonance imaging (DCE-MRI) applying principal component analysis (PCA) adjusted with a model-based method.Materials and Methods: 3D gradient-echo DCE breast images of 31 malignant and 38 benign lesions, recorded on a 1.5T scanner, were retrospectively analyzed by PCA and by the model-based three-timepoints (3TP) method.Results: Intensity-scaled (IS) and enhancement-scaled (ES) datasets were reduced by PCA yielding a first IS-eigenvector that captured the signal variation between fat and fibroglandular tissue; two IS-eigenvectors and the two first ES-eigenvectors captured contrast-enhanced changes, whereas the remaining eigenvectors captured predominantly noise changes. Rotation of the two contrast-related eigenvectors led to a high congruence between the projection coefficients and the 3TP parameters. The ES-eigenvectors and the rotation angle were highly reproducible across malignant lesions, enabling calculation of a general rotated eigenvector base. Receiver operating characteristic (ROC) curve analysis of the projection coefficients of the two eigenvectors indicated high sensitivity of the first rotated eigenvector to detect lesions (area under the curve [AUC] > 0.97) and of the second rotated eigenvector to differentiate malignancy from benignancy (AUC ¼ 0.87). Conclusion:PCA adjusted with a model-based method provided a fast and objective computer-aided diagnostic tool for breast DCE-MRI.

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confidence: 99%

Principal component analysis of breast DCE‐MRI adjusted with a model‐based method

Eyal

Badikhi

Furman‐Haran

et al. 2009

Magnetic Resonance Imaging

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“…Hence, a modelfree approach to analyze the DCE-MRI data, e.g., the methods based on factor analysis, 8,9 independent component analysis (ICA), 10 and principal component analysis (PCA), [11][12][13][14] could potentially facilitate the development of the real-time decision-making supportive tools in diagnosis and therapy assessment. PCA has shown the potential to be a very robust and fast technique in analyzing the DCE-MRI data, [11][12][13][14][15][16] and especially in prostate [12][13][14] and breast 15,16 cancer. Eyal et al 12 applied PCA to dynamic intensity-scaled (IS) and enhancement-scaled (ES) datasets to develop and evaluate a method for image processing of the dynamic contrast enhanced MRI of the prostate cancer.…”

Section: Introductionmentioning

confidence: 99%

DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

Farjam¹,

Tsien²,

Lawrence³

et al. 2013

Med. Phys.

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Purpose: To develop a pharmacokinetic modelfree framework to analyze the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data for assessment of response of brain metastases to radiation therapy. Methods: Twenty patients with 45 analyzable brain metastases had MRI scans prior to whole brain radiation therapy (WBRT) and at the end of the 2-week therapy. The volumetric DCE images covering the whole brain were acquired on a 3T scanner with approximately 5 s temporal resolution and a total scan time of about 3 min. DCE curves from all voxels of the 45 brain metastases were normalized and then temporally aligned. A DCE matrix that is constructed from the aligned DCE curves of all voxels of the 45 lesions obtained prior to WBRT is processed by principal component analysis to generate the principal components (PCs). Then, the projection coefficient maps prior to and at the end of WBRT are created for each lesion. Next, a pattern recognition technique, based upon fuzzy-c-means clustering, is used to delineate the tumor subvolumes relating to the value of the significant projection coefficients. The relationship between changes in different tumor subvolumes and treatment response was evaluated to differentiate responsive from stable and progressive tumors. Performance of the PCdefined tumor subvolume was also evaluated by receiver operating characteristic (ROC) analysis in prediction of nonresponsive lesions and compared with physiological-defined tumor subvolumes. Results: The projection coefficient maps of the first three PCs contain almost all response-related information in DCE curves of brain metastases. The first projection coefficient, related to the area under DCE curves, is the major component to determine response while the third one has a complimentary role. In ROC analysis, the area under curve of 0.88 ± 0.05 and 0.86 ± 0.06 were achieved for the PC-defined and physiological-defined tumor subvolume in response assessment. Conclusions: The PC-defined subvolume of a brain metastasis could predict tumor response to therapy similar to the physiological-defined one, while the former is determined more rapidly for clinical decision-making support.

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“…Post processing fat suppression ( 24 , 25 ) and 4 . Identifying breast tumors and characterizing kinetic behavior in order to improve breast cancer detection and diagnosis ( 26 – 31 ).…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, two critical postcontrast time points were selected and the washin and washout rates were related to the model based physiological parameters using a calculated calibration map ( 9 ). Later, when it became possible to maintain high spatial resolution and enhance the temporal resolution, we developed a processing tool for breast and prostate DCE-MRI using PCA adjusted with the 3TP parameters ( 29 , 30 , 36 ). In this report we describe the actual testing of the performance of a standardization process based on a 3TP+PCA hybrid method, using different scanners at two field strengths (1.5T and 3T) and three different sequence protocols.…”

Section: Introductionmentioning

confidence: 99%

Standardization of Radiological Evaluation of Dynamic Contrast Enhanced MRI: Application in Breast Cancer Diagnosis

et al. 2013

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Dynamic contrast enhanced MRI is applied as an adjuvant tool for breast cancer detection, diagnosis, and follow-up of therapy. Despite improvements through the years in achieving higher spatial and temporal resolution, it still suffers from lack of scanning and processing standardization, and consequently, high variability in the radiological evaluation, particularly differentiating malignant from benign lesions. We describe here a hybrid method for achieving standardization of the radiological evaluation of breast dynamic contrast enhanced (DCE)-magnetic resonance imaging (MRI) protocols, based on integrating the model based three time point (3TP) method with principal component analysis (PCA). The scanning and image processing procedures consisted of three main steps: 1. 3TP standardization of the MRI acquisition parameters according to a kinetic model, 2. Applying PCA to test cases and constructing an eigenvectors' base related to the contrast-enhancement kinetics and 3. Projecting all new cases on the eigenvectors' base and evaluating the clinical outcome. Datasets of overall 96 malignant and 26 benign breast lesions were recorded on 1.5T and 3T scanners, using three different MRI acquisition parameters optimized by the 3TP method. The final radiological evaluation showed similar detection and diagnostic ability for the three different MRI acquisition parameters. The area under the curve of receiver operating characteristic analysis yielded a value of 0.88 ± 0.034 for differentiating malignant from benign lesions. This 3TP + PCA hybrid method is fast and can be readily applied as a computer aided diagnostic tool of breast cancer. The underlying principles of this method can be extended to standardize the evaluation of malignancies in other organs.

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Combination of model-free and model-based analysis of dynamic contrast enhanced MRI for breast cancer diagnosis

Cited by 5 publications

References 0 publications

Principal component analysis of breast DCE‐MRI adjusted with a model‐based method

Principal component analysis of breast DCE‐MRI adjusted with a model‐based method

DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

Standardization of Radiological Evaluation of Dynamic Contrast Enhanced MRI: Application in Breast Cancer Diagnosis

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