Breast magnetic resonance imaging interpretation using computer-aided detection

US-Elastography for Breast Lesion Characterization: Prospective Comparison of US BIRADS, Strain Elastography and Shear wave Elastography

PURPOSE

 To evaluate the diagnostic performance of strain elastography (SE) and 2 D shear wave elastography (SWE) and SE/SWE combination in comparison with conventional multiparametric ultrasound (US) with respect to improving BI-RADS classification results and differentiating benign and malignant breast lesions using a qualitative and quantitative assessment.

MATERIALS AND METHODS

 In this prospective study, 130 histologically proven breast masses were evaluated with baseline US, color Doppler ultrasound (CDUS), SE and SWE (Toshiba Aplio 500 with a 7-15 MHz wide-band linear transducer). Each lesion was classified according to the BIRADS lexicon by evaluating the size, the B-mode and color Doppler features, the SE qualitative (point color scale) and SE semi-quantitative (strain ratio) methods, and quantitative SWE. Histological results were compared with BIRADS, strain ratio (SR) and shear wave elastography (SWE) all performed by one investigator blinded to the clinical examination and mammographic results at the time of the US examination. The area under the ROC curve (AUC) was calculated to evaluate the diagnostic performance of B-mode US, SE, SWE, and their combination.

RESULTS

 Histological examination revealed 47 benign and 83 malignant breast lesions. The accuracy of SR was statistically significantly higher than SWE (sensitivity, specificity and AUC were 89.2 %, 76.6 % and 0.83 for SR and 72.3 %, 66.0 % and 0.69 for SWE, respectively, p = 0.003) but not higher than B-mode US (B-mode US sensitivity, specificity and AUC were 85.5 %, 78.8 %, 0.821, respectively, p = 1.000).

CONCLUSION

 Our experience suggests that conventional US in combination with both SE and SWE is a valid tool that can be useful in the clinical setting, can improve BIRADS category assessment and may help in the differentiation of benign from malignant breast lesions, with SE having higher accuracy than SWE.

3.0T MR-CAD: Clinical Value in Diagnosis of Breast Tumor Compared with Conventional MRI

Purpose: to explore the clinical value of 3.0T magnetic resonance (MR) imaging compared with computer-aided MR diagnosis (MR-CAD) in differential diagnosis of benign and malignant breast tumors.

Materials and Methods: MRI method and MR-CAD method was used in the diagnosis of a total of 93 breast lesions of 78 patients, based on the morphological and time-intensity-curve (TIC) analysis. The accuracy of the two modalities in differentiating malignant and benign breast tumor was compared.

Results: MR-CAD method yielded a statistically better accuracy than MRI method. For 51 mass-like lesions, MRI and MR-CAD had no difference in diagnosing accuracy, but MR-CAD had better accuracy in 42 non-mass-like lesions.

Conclusion: MR-CAD had a notable advantage over MRI in differential diagnosis of benign and malignant breast tumors, especially non-mass-like tumor.

Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images

PURPOSE

Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Computerized analysis is increasingly used to quantify breast MRI features in applications such as computer-aided lesion detection and fibroglandular tissue estimation for breast cancer risk assessment. Automated segmentation of the whole-breast as an organ from the other parts imaged is an important step in aiding lesion localization and fibroglandular tissue quantification. For this task, identifying the chest wall line (CWL) is most challenging due to image contrast variations, intensity discontinuity, and bias field.

METHODS

In this work, the authors develop and validate a fully automated image processing algorithm for accurate delineation of the CWL in sagittal breast MRI. The CWL detection is based on an integrated scheme of edge extraction and CWL candidate evaluation. The edge extraction consists of applying edge-enhancing filters and an edge linking algorithm. Increased accuracy is achieved by the synergistic use of multiple image inputs for edge extraction, where multiple CWL candidates are evaluated by the dynamic time warping algorithm coupled with the construction of a CWL reference. Their method is quantitatively validated by a dataset of 60 3D bilateral sagittal breast MRI scans (in total 3360 2D MR slices) that span the full American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) breast density range. Agreement with manual segmentation obtained by an experienced breast imaging radiologist is assessed by both volumetric and boundary-based metrics, including four quantitative measures.

RESULTS

In terms of breast volume agreement with manual segmentation, the overlay percentage expressed by the Dice's similarity coefficient is 95.0% and the difference percentage is 10.1%. More specifically, for the segmentation accuracy of the CWL boundary, the CWL overlay percentage is 92.7% and averaged deviation distance is 2.3 mm. Their method requires ≈ 4.5 min for segmenting each 3D breast MRI scan (56 slices) in comparison to ≈ 35 min required for manual segmentation. Further analysis indicates that the segmentation performance of their method is relatively stable across the different BI-RADS density categories and breast volume, and also robust with respect to a varying range of the major parameters of the algorithm.

CONCLUSIONS

Their fully automated method achieves high segmentation accuracy in a time-efficient manner. It could support large scale quantitative breast MRI analysis and holds the potential to become integrated into the clinical workflow for breast cancer clinical applications in the future.