Role of dynamic contrast enhanced MRI in evaluation of post-operative breast lesions

EL-Adalany, Mohamed Ali; Hamed, Emad

doi:10.1016/j.ejrnm.2016.02.003

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…Enhancement can be homogeneous or heterogeneous which may be associated with fat signal intensity, fat necrosis, signal voids, or resolving edema, so breath holding-DE-T1WI acquired by an MR scanner can provide superior soft tissue contrast [ 20 , 39 , 40 ]. Hence, we innovatively regard breath holding DE-T1WI as simulation scans for breast cancer patients who underwent prone RT.…”

Section: Discussionmentioning

confidence: 99%

DE-MR simulation imaging for prone radiotherapy after breast-conserving surgery: assessing its application in lumpectomy cavity delineation based on deformable image registration

Zhao

Wang

et al. 2021

Radiat Oncol

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Background The application of delayed-enhancement magnetic resonance (DE-MR) simulation imaging in lumpectomy cavity (LC) delineation for prone radiotherapy in patients with an invisible seroma or a low seroma clarity score (SCS) after breast-conserving surgery (BCS) based on deformable image registration (DIR) was assessed. Methods Twenty-six patients who were suitable for radiotherapy in prone positions after BCS were enrolled, and both computed tomography (CT) and DE-MR simulation scans were acquired. The LC delineated based on titanium surgical clips on CT images was denoted as LCCT. The LC delineated based on the signal of cavity boundaries on fat-suppressed T2-weighted imaging (T2WI) and multiphase delayed-enhancement T1-weighted imaging (DE-T1WI), which was performed at 2 min, 5 min and 10 min postinjection, were denoted as LCT2, LC2T1, LC5T1 and LC10T1, respectively. Afterwards, DIR was performed to compare the volumes and locations of the LCs with MIM software. The generalized conformity index (CIgen) of inter (intra) observer (Inter-CIgen and Intra-CIgen) was also used to explore the inter(intra) observer variation for LC delineation on each image modality. Results LCCT–LC10T1 provided the best conformal index (CI) and degree of inclusion (DI), increasing by 2.08% and 4.48% compared to LCCT–LCT2, 11.36% and 2.94% for LCCT–LC2T1, and 8.89% and 7.69% for LC5T1–LCCT, respectively. The center of mass (COM) of LCCT–LC10T1 decreased by 17.86%, 6.12% and 13.21% compared with that of LCCT–LCT2, LCCT–LC2T1 and LCCT–LC5T1, respectively. The agreement of LC delineation was strongest for 10th min DE-TIWI (coefficient of variation, COV = 2.30%, Inter-CIgen = 87.06%, Intra-CIgen = 92.64%). Conclusion For patients with a low SCS (SCS ≤ 2) after BCS, it is feasible to contour the LC based on prone DE-MR simulation images. Furthermore, the LC derived from prone DE-T1WI at 10 min was found to be most similar to that derived from prone CT simulation scans using titanium surgical clips regardless of the volume and location of the LC. Inter (intra) variability was minimal for the delineation of the LC based on 10th min DE-TIWI.

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Section: Discussionmentioning

confidence: 99%

DE-MR simulation imaging for prone radiotherapy after breast-conserving surgery: assessing its application in lumpectomy cavity delineation based on deformable image registration

Zhao

Wang

et al. 2021

Radiat Oncol

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show abstract

“…The high LC signal remained on fat-suppressed T2WI even though the longest time from surgery in our study was 198 days. Enhancement can be homogeneous or heterogeneous which may be associated with fat signal intensity, fat necrosis, signal voids, or resolving edema, so breath holding-DE-T1WI acquired by an MR scanner can provide superior soft tissue contrast [20,36,37] . Hence, we innovatively regard breath holding DE-T1WI as simulation scans for breast cancer patients who underwent prone RT.…”

Section: Discussionmentioning

confidence: 99%

Delayed-enhancement magnetic resonance simulation imaging for prone radiotherapy after breast-conserving surgery: Assessing its application in lumpectomy cavity delineation based on deformable image registration

Zhao

Wang

et al. 2021

Preprint

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Background The application of delayed-enhancement magnetic resonance (DE-MR) simulation imaging in lumpectomy cavity (LC) delineation for prone radiotherapy in patients with an invisible seroma or a low seroma clarity score (SCS) after breast-conserving surgery (BCS) based on deformable image registration (DIR) was assessed. Methods Twenty-six patients who were suitable for radiotherapy in prone positions after BCS were enrolled, and both computed tomography (CT) and DE-MR simulation scans were acquired. The LC delineated based on titanium surgical clips on CT images was denoted as LCCT. The LC delineated based on the signal of cavity boundaries on fat-suppressed T2-weighted imaging (T2WI) and multiphase delayed-enhancement T1-weighted imaging (DE-T1WI), which was performed at 2 min, 5 min and 10 min postinjection, were denoted as LCT2, LC2T1, LC5T1 and LC10T1, respectively. Afterwards, DIR was performed to compare the volumes and locations of the LCs with MIM software. The generalized conformity index (CIgen) of inter (intra) observer (Inter-CIgen and Intra-CIgen) was also used to explore the inter(intra) observer variation for LC delineation on each image modality. Results LCCT-LC10T1 provided the best conformal index (CI) and degree of inclusion (DI), increasing by 2.08% and 4.48% compared to LCCT-LCT2, 11.36% and 2.94% for LCCT-LC2T1, and 8.89% and 7.69% for LC5T1-LCCT, respectively. The center of mass (COM) of LCCT-LC10T1 decreased by 17.86%, 6.12% and 13.21% compared with that of LCCT-LCT2, LCCT-LC2T1 and LCCT-LC5T1, respectively. The agreement of LC delineation was strongest for 10th min DE-TIWI (coefficient of variation, COV = 2.30%, Inter-CIgen = 87.06%, Intra-CIgen = 92.64%). Conclusion For patients with a low SCS (SCS ≤ 2) after BCS, it is feasible to contour the LC based on prone DE-MR simulation images. Furthermore, the LC derived from prone DE-T1WI at 10 min was found to be most similar to that derived from prone CT simulation scans using titanium surgical clips regardless of the volume and location of the LC. Inter(intra)variability was minimal for the delineation of the LC based on 10th min DE-TIWI.

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“…Early discovery of abnormalities will not only improve survival rates but also enhance therapeutic efficacy. Several imaging examinations, such as breast magnetic resonance imaging (MRI), breast elastography and B-mode ultrasound imaging, and breast mammography, can be used for breast tumor detection [3][4][5][6][7][8][9][10][11]:…”

Section: Introductionmentioning

confidence: 99%

“…Sample cases from MIAS (mammographic image analysis society) images for benign (B) and malignant (M) breast tumors in lateral right and left breasts. The approximate radius of a circle enclosing the abnormality is 4.53-19.39 mm sensitivity, good contrast for detecting small breast lesions, and suitability for use in patients undergoing surgical treatment [4,5]. However, prior to MRI imaging, patients must receive an intravenous injection of a contrast agent to facilitate breast tumor detection.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Breast Mammography to Rapid Screen Abnormalities Using 2D Spatial Fractional‐Order Feature Extraction and Multilayer Machine Vision Classifier

Chen

Lin

et al. 2021

IEEJ Transactions Elec Engng

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While breast tumor symptoms [benign (B) or malignant (M) tumors] or lymph node metastasis can be detected at an early stage, the timely discovery of abnormalities could simultaneously improve survival rates and enhance therapeutic efficacy. During firstline mammography examination, upon finding any solid-white region, clinicians or radiologists can manually select the region of interest (ROI) from lateral-view images with a specific bounding box (BB). This study used two-dimensional (2D) fractionalorder convolution (FOC) operations with fractional-order parameters (v = 0.3 − 0.4) to sharpen and retain the structure of possible focus, inhibit the features of background tissue, and remove unwanted noise. The gray-scale values of breast mammography can be readjusted to enhance the image contrast using a nonlinear intensity transformation function. Abnormal contour levels can automatically be searched on the basis of the edge detection of normalized gradients to interpret the main ROI region using a contour algorithm. Hence, the main ROI can be rapidly identified on each breast mammography. The central region of the main ROI can then be divided into five sub ROIs, which are fed to a multilayer machine vision classifier. We used a general regression neural network (GRNN)-based classifier to separate normal (no tumor) images from abnormal (B or M tumor) ones and obtain an automatic screening support tool that can decisively confirm suspicious lesions and achieve accurate detection. Using images from the Mammographic Image Analysis Society (MIAS) digital breast mammogram database, we applied the proposed GRNNbased classifier with K-fold cross-validation to emphasize the possible focus and provide additional confidence during imaging examination. Results showed an average recall (%), an average precision (%), an average accuracy (%), and an average F1 score of 87.31%, 90.50%, 88.67%, and 0.8883%, respectively, for the rapid screening of B and M tumors on digital breast X-ray images. The GRNN-based classifier is superior to traditional techniques in terms of adaptive learning schemes, computational time consumption, and design cycle.

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Role of dynamic contrast enhanced MRI in evaluation of post-operative breast lesions

Cited by 4 publications

References 18 publications

DE-MR simulation imaging for prone radiotherapy after breast-conserving surgery: assessing its application in lumpectomy cavity delineation based on deformable image registration

DE-MR simulation imaging for prone radiotherapy after breast-conserving surgery: assessing its application in lumpectomy cavity delineation based on deformable image registration

Delayed-enhancement magnetic resonance simulation imaging for prone radiotherapy after breast-conserving surgery: Assessing its application in lumpectomy cavity delineation based on deformable image registration

Enhancement of Breast Mammography to Rapid Screen Abnormalities Using 2D Spatial Fractional‐Order Feature Extraction and Multilayer Machine Vision Classifier

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