Optimising magnetic sentinel lymph node biopsy in an in vivo porcine model

The magnetic technique for sentinel lymph node biopsy (SLNB) has been evaluated in several clinical trials. An in vivo porcine model was developed to optimise the magnetic technique by evaluating the effect of differing volume, concentration and time of injection of magnetic tracer. A total of 60 sentinel node procedures were undertaken. There was a significant correlation between magnetometer counts and iron content of excised sentinel lymph nodes (SLNs) (r = 0.82; P < 0.001). Total number of SLNs increased with increasing volumes of magnetic tracer (P < 0.001). Transcutaneous magnetometer counts increased with increasing time from injection of magnetic tracer (P < 0.0001), plateauing within 60 min. Increasing concentration resulted in higher iron content of SLNs (P = 0.006). Increasing magnetic tracer volume and injecting prior to surgery improve transcutaneous ‘hotspot’ identification but very high volumes, increase the number of nodes excised. From the Clinical Editor Sentinel lymph node biopsy (SLNB) is the standard of care for axillary staging of breast cancer patients. Although the current gold standard technique is the combined injection of technetium-labelled nanocolloid and blue dye into the breast, the magnetic technique, using superparamagnetic carboxydextran-coated iron oxide (SPIO), has also been demonstrated as a feasible alternative. In this article, the authors set up to study factors in order to optimize the magnetic tracers. Graphical abstract Variable volumes and concentrations of a magnetic tracer were injected into the third inguinal mammary gland bilaterally in an in vivo porcine model (1) allowing the performance of magnetic sentinel lymph node biopsy of draining inguinal nodes (2). The harvested nodes were ‘darkly stained’ for iron uptake and ‘hot’ for magnetometer counts (3). The iron was deposited within the cortex and subcapsular space – visible as blue using PERL’s staining – on histopathology (4) and was quantified using quantitative magnetometry and a validated iron-grading scale

Breast MRI for screening: evaluation of clinical practice and future perspectives

Contains fulltext : 194302.pdf (publisher's version ) (Open Access)Radboud University, 27 september 2018Promotor : Karssemeijer, N. Co-promotores : Mann, R.M., Gubern Merida, A.vii, 218 p

Is Ultrafast or Abbreviated Breast MRI Ready for Prime Time?

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A computer-aided diagnosis system for breast DCE-MRI at high spatiotemporal resolution

PURPOSE: With novel MRI sequences, high spatiotemporal resolution has become available in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. Since benign structures in the breast can show enhancement similar to malignancies in DCE-MRI, characterization of detected lesions is an important problem. The purpose of this study is to develop a computer-aided diagnosis (CADx) system for characterization of breast lesions imaged with high spatiotemporal resolution DCE-MRI. METHODS: The developed CADx system is composed of four main parts: semiautomated lesion segmentation, automated computation of morphological and dynamic features, aorta detection, and classification between benign and malignant categories. Lesion segmentation is performed by using a "multiseed smart opening" algorithm. Five morphological features were computed based on the segmentation of the lesion. For each voxel, contrast enhancement curve was fitted to an exponential model and dynamic features were computed based on this fitted curve. Average and standard deviations of the dynamic features were computed over the entire segmented area, in addition to the average value in an automatically selected smaller "most suspicious region." To compute the dynamic features for an enhancement curve, information of aortic enhancement is also needed. To keep the system fully automated, the authors developed a component which automatically detects the aorta and computes the aortic enhancement time. The authors used random forests algorithm to classify benign lesions from malignant. The authors evaluated this system in a dataset of breast MRI scans of 325 patients with 223 malignant and 172 benign lesions and compared its performance to an existing approach. The authors also evaluated the classification performances for ductal carcinoma in situ (DCIS), invasive ductal carcinoma (IDC), and invasive lobular carcinoma (ILC) lesions separately. The classification performances were measured by receiver operating characteristic (ROC) analysis in a leave-one-out cross validation scheme. RESULTS: The area under the ROC curve (AUC) obtained by the proposed CADx system was 0.8543, which was significantly higher (p = 0.007) than the performance obtained by the previous CADx system (0.8172) on the same dataset. The AUC values for DCIS, IDC, and ILC lesions were 0.7924, 0.8688, and 0.8650, respectively. CONCLUSIONS: The authors developed a CADx system for high spatiotemporal resolution DCE-MRI of the breast. This system outperforms a previously proposed system in classifying benign and malignant lesions, while it requires less user interactions

The frequency of missed breast cancers in women participating in a high-risk MRI screening program

Contains fulltext : 191352.pdf (publisher's version ) (Open Access)PURPOSE: To evaluate the frequency of missed cancers on breast MRI in women participating in a high-risk screening program. METHODS: Patient files from women who participated in an increased risk mammography and MRI screening program (2003-2014) were coupled to the Dutch National Cancer Registry. For each cancer detected, we determined whether an MRI scan was available (0-24 months before cancer detection), which was reported to be negative. These negative MRI scans were in consensus re-evaluated by two dedicated breast radiologists, with knowledge of the cancer location. Cancers were scored as invisible, minimal sign, or visible. Additionally, BI-RADS scores, background parenchymal enhancement, and image quality (IQ; perfect, sufficient, bad) were determined. Results were stratified by detection mode (mammography, MRI, interval cancers, or cancers in prophylactic mastectomies) and patient characteristics (presence of BRCA mutation, age, menopausal state). RESULTS: Negative prior MRI scans were available for 131 breast cancers. Overall 31% of cancers were visible at the initially negative MRI scan and 34% of cancers showed a minimal sign. The presence of a BRCA mutation strongly reduced the likelihood of visible findings in the last negative MRI (19 vs. 46%, P < 0.001). Less than perfect IQ increased the likelihood of visible findings and minimal signs in the negative MRI (P = 0.021). CONCLUSION: This study shows that almost one-third of cancers detected in a high-risk screening program are already visible at the last negative MRI scan, and even more in women without BRCA mutations. Regular auditing and double reading for breast MRI screening is warranted

Fully automated detection of breast cancer in screening MRI using convolutional neural networks

Contains fulltext : 191309.pdf (publisher's version ) (Open Access)Current computer-aided detection (CADe) systems for contrast-enhanced breast MRI rely on both spatial information obtained from the early-phase and temporal information obtained from the late-phase of the contrast enhancement. However, late-phase information might not be available in a screening setting, such as in abbreviated MRI protocols, where acquisition is limited to early-phase scans. We used deep learning to develop a CADe system that exploits the spatial information obtained from the early-phase scans. This system uses three-dimensional (3-D) morphological information in the candidate locations and the symmetry information arising from the enhancement differences of the two breasts. We compared the proposed system to a previously developed system, which uses the full dynamic breast MRI protocol. For training and testing, we used 385 MRI scans, containing 161 malignant lesions. Performance was measured by averaging the sensitivity values between 1/8-eight false positives. In our experiments, the proposed system obtained a significantly ([Formula: see text]) higher average sensitivity ([Formula: see text]) compared with that of the previous CADe system ([Formula: see text]). In conclusion, we developed a CADe system that is able to exploit the spatial information obtained from the early-phase scans and can be used in screening programs where abbreviated MRI protocols are used

Amount of fibroglandular tissue FGT and background parenchymal enhancement BPE in relation to breast cancer risk and false positives in a breast MRI screening program A retrospective cohort study

Contains fulltext : 206247.pdf (publisher's version ) (Open Access

Artificial Intelligence-Based Classification of Breast Lesions Imaged With a Multiparametric Breast MRI Protocol With Ultrafast DCE-MRI, T2, and DWI

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