Breast Contrast Enhanced MR Imaging: Semi-Automatic Detection of Vascular Map and Predominant Feeding Vessel

Kaiser score diagnosis of breast MRI lesions: Factors associated with false-negative and false-positive results

PURPOSE

We sought factors associated with false-negative and false-positive results in the diagnosis of breast lesions using the Kaiser score (KS) on breast magnetic resonance imaging (MRI).

METHODS

We retrospectively analyzed 1058 patients with 1058 breast lesions who underwent preoperative breast MRI with successful histopathologic results. Two radiologists assessed each lesion according to KS criteria, and clinicopathologic features and MRI findings were analyzed. Multivariate regression analysis was conducted to identify factors associated with false-negative and false-positive KS results.

RESULTS

Of the 1058 lesions, 859 were malignant and 199 were benign. Particularly high misdiagnosis rates were observed for intraductal papilloma, inflammatory lesion, and mucinous carcinoma. For breast cancer, KS yielded 821 (95.6 %) true-positive and 38 (4.4 %) false-negative results. Multivariate analysis showed that smaller lesion size (≤1 cm) (OR, 3.698; 95 %CI, 1.430-9.567; p = 0.007), absence of ipsilateral breast hypervascularity (OR, 3.029; 95 %CI, 1.370-6.693; p = 0.006), and presence of hyperintensity on T2WI (OR, 2.405; 95 %CI, 1.121-5.162; p = 0.024) were significantly associated with false-negative breast cancer results. For benign lesions, KS yielded 141 (70.9 %) true-negative and 58 (29.1 %) false-positive results. Multivariate regression analysis revealed that non-mass enhancement lesions (OR, 4.660; 95 %CI, 2.018-10.762; p＜0.001), moderate/high background parenchymal enhancement (OR, 2.402; 95 %CI, 1.180-4.892; p = 0.016), and the presence of hyperintensity on T2WI (OR, 2.986; 95 %CI, 1.386-6.433; p = 0.005) were significantly associated with false-positive KS results.

CONCLUSION

Several clinicopathologic and MRI features influence the accuracy of KS diagnosis. Understanding these factors may facilitate appropriate use of KS and guide alternative diagnostic approaches, ultimately improving diagnostic accuracy in the evaluation of breast lesions.

Detection of internal mammary chain infiltration in breast cancer patients by [18F]FDG PET/MRI. Therapy implications

OBJECTIVE

To evaluate the detection rate and therapeutic implication of the infiltration of the internal mammary chain (IMCI) by [18F]FDG PET/MRI for staging of patients with breast cancer.

METHODS

Prospective study including 41 women with breast cancer (stage ≥ IIB) staged by [18F]FDG PET/MR. Two-phase exam: breast imaging (prone), whole-body (supine). TNM stage assessed by peer consensus with Nuclear Medicine and Radiology specialists. Study of the afferent vessel (AV) to IMC by breast MRI. IMCI was correlated with age, AV-IMC, T stage, breast quadrants, axillary and distant infiltration. Therapeutic re-evaluation by a multidisciplinary committee.

RESULTS

IMCI detection rate of 34% (14/41), with 8/14 patients under 55 years of age. All 14 patients with IMCI showed AV-IMC, 6 of them (43.9%) without VA-axillary. Of 27/41 patients without IMCI, in 13 (48.1%) only AV-axillary was found, in the remaining 14 (51.9%), AV-axillary and AV-IMC was found. In 57% (8/14) tumours were multicentric and 42% (6/14) focal, in inner quadrants in 4/6 (66.7%). In 1/14 patient (7.1%) only IMCI was found, in 9/14 (64.3%) axillary and IMC, in 4/14 patients (28.6%) distant lesions were detected. Committee re-evaluation: no further treatment in 27/41 patients (65.8%), thoracic radiotherapy in 10/41 patients (24.4%), systemic therapy in 4/41 patients (9.7%).

CONCLUSION

Our detection rate of IMCI in breast cancer staging by [18F]FDG PET/MR was 34%. Related factors were age, multicentric tumours, inner quadrants, detection of AV-IMC, NM staging. The evidence of IMCI allowed tailored therapy, with thoracic radiotherapy implementation in 24.4% of patients.

Automated vascular analysis of breast thermograms with interpretable features

Purpose: Vascular changes are observed from initial stages of breast cancer, and monitoring of vessel structures helps in early detection of malignancies. In recent years, thermal imaging is being evaluated as a low-cost imaging modality to visualize and analyze early vascularity. However, visual inspection of thermal vascularity is challenging and subjective. Therefore, there is a need for automated techniques to assist physicians in visualization and interpretation of vascularity by marking the vessel structures and by providing quantified qualitative parameters that helps in malignancy classification Approach: In the literature, there are very few approaches for vascular analysis and classification of breast thermal images using interpretable vascular features. One major challenge is the automated detection of breast vascularity due to diffused vessel boundaries. We first propose a deep learning-based semantic segmentation approach that generates heatmaps of vessel structures from two-dimensional breast thermal images for quantitative assessment of breast vascularity. Second, we extract interpretable vascular parameters and propose a classifier to predict likelihood of breast cancer purely from the extracted vascular parameters. Results: The results of the cancer classifier were validated using an independent clinical dataset consisting of 258 participants. The results were encouraging as the proposed approach segmented vessels well and gave a good classification performance with area under receiver operating characteristic curve of 0.85 with the proposed vascularity parameters. Conclusions: The detected vasculature and its associated high classification performance show the utility of the proposed approach in interpretation of breast vascularity.

Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging

Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.

Assessment of breast arteries and lymph nodes by 3D MR angiography enhancement imaging: feasibility and pilot clinical results

Background

Conventional dynamic contrast enhanced (DCE) magnetic resonance (MR) hardly achieves a good imaging performance of arteries and lymph nodes in the breast area. Therefore, a new imaging method is needed for the assessment of breast arteries and lymph nodes.

Methods

We performed prospective research. The research included 52 patients aged from 25 to 64 between June 2019 and April 2020. The isotropic e-THRIVE sequence scanned in the coronal direction after DCE-THRIVE. Reconstructed images obtained by DCE-THRIVE and the coronal e-THRIVE were compared mainly in terms of the completeness of the lateral thoracic artery, thoracodorsal artery, and lymph nodes. We proposed a criterion for evaluating image quality. According to the criterion, images were assigned a score from 1 to 5 according to the grade from low to high. Two board-certified doctors evaluated images individually, and their average score was taken as the final result. The chi-square test was used to assess the difference.

Results

The coronal e-THRIVE score is 4.60, which is higher than the DCE-THRIVE score of 3.48, there are significant differences between the images obtained by two sequences (P = 1.2712e−8). According to the score of images, 44 patients (84.61%) had high-quality images on the bilateral breast. Only 3 patients’ (5.77%) images were not ideal on both sides. The improved method is effective for most patients to get better images.

Conclusions

The proposed coronal e-THRIVE scan can get higher quality reconstruction images than the conventional method to visualize the course of arteries and the distribution of lymph nodes in most patients, which will be helpful for the clinical follow-up treatment.

Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification

To compare diagnostic performance of contrast-enhanced dual-energy digital mammography (CEDM) and digital breast tomosynthesis (DBT) alone and in combination compared to 2D digital mammography (MX) and dynamic contrast-enhanced MRI (DCE-MRI) in women with breast lesions. We enrolled 100 consecutive patients with breast lesions (BIRADS 3-5 at imaging or clinically suspicious). CEDM, DBT, and DCE-MRI 2D were acquired. Synthetized MX was obtained by DBT. A total of 134 lesions were investigated on 111 breasts of 100 enrolled patients: 53 were histopathologically proven as benign and 81 as malignant. Nonparametric statistics and receiver operating characteristic (ROC) curve were performed. Two-dimensional synthetized MX showed an area under ROC curve (AUC) of 0.764 (sensitivity 65%, specificity 80%), while AUC was of 0.845 (sensitivity 80%, specificity 82%) for DBT, of 0.879 (sensitivity 82%, specificity 80%) for CEDM, and of 0.892 (sensitivity 91%, specificity 84%) for CE-MRI. DCE-MRI determined an AUC of 0.934 (sensitivity 96%, specificity 88%). Combined CEDM with DBT findings, we obtained an AUC of 0.890 (sensitivity 89%, specificity 74%). A difference statistically significant was observed only between DCE-MRI and CEDM (P = .03). DBT, CEDM, CEDM combined to tomosynthesis, and DCE-MRI had a high ability to identify multifocal and bilateral lesions with a detection rate of 77%, 85%, 91%, and 95% respectively, while 2D synthetized MX had a detection rate for multifocal lesions of 56%. DBT and CEDM have superior diagnostic accuracy of 2D synthetized MX to identify and classify breast lesions, and CEDM combined with DBT has better diagnostic performance compared with DBT alone. The best results in terms of diagnostic performance were obtained by DCE-MRI. Dynamic information obtained by time-intensity curve including entire phase of contrast agent uptake allows a better detection and classification of breast lesions.

Quantitative analysis of vascular properties derived from ultrafast DCE-MRI to discriminate malignant and benign breast tumors

PURPOSE

We propose a novel methodology to integrate morphological and functional information of tumor-associated vessels to assist in the diagnosis of suspicious breast lesions.

THEORY AND METHODS

Ultrafast, fast, and high spatial resolution DCE-MRI data were acquired on 15 patients with suspicious breast lesions. Segmentation of the vasculature from the surrounding tissue was performed by applying a Hessian filter to the enhanced image to generate a map of the probability for each voxel to belong to a vessel. Summary measures were generated for vascular morphology, as well as the inputs and outputs of vessels physically connected to the tumor. The ultrafast DCE-MRI data was analyzed by a modified Tofts model to estimate the bolus arrival time, K^trans (volume transfer coefficient), and v_p (plasma volume fraction). The measures were compared between malignant and benign lesions via the Wilcoxon test, and then incorporated into a logistic ridge regression model to assess their combined diagnostic ability.

RESULTS

A total of 24 lesions were included in the study (13 malignant and 11 benign). The vessel count, K^trans , and v_p showed significant difference between malignant and benign lesions (P = 0.009, 0.034, and 0.010, area under curve [AUC] = 0.76, 0.63, and 0.70, respectively). The best multivariate logistic regression model for differentiation included the vessel count and bolus arrival time (AUC = 0.91).

CONCLUSION

This study provides preliminary evidence that combining quantitative characterization of morphological and functional features of breast vasculature may provide an accurate means to diagnose breast cancer.