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Health care workers (HCWs), and in particular anesthesia providers, often must perform aerosol-generating medical procedures (AGMPs). However, no studies have analyzed droplet distributions on the bodies of HCWs during AGMPs. Therefore, the purpose of this study was to assess and analyze droplet distributions on the bodies of HCWs during suction of oral cavities with and without oral airways and during extubations.Using a quasi-experiemental design, we assumed the HCWs perform suction and extubation on intubated patients, and we prepared an intubated mannequin mimicking a patient. This study performed the oral suction and extubation on the intubated mannequin (with or without oral airways in place) and analyzed the droplet distributions.We prepared a mannequin intubated with an 8.0 mm endotracheal tube, assuming the situation of general anesthesia. We designed the body mapping gown, and divided it into 10 areas including the head, neck, chest, abdomen, upper arms, forearms, and hands. We classified experiments into group O when suctions were performed on the mannequin with an oral airway, and into group X when the suctions were performed on the mannequin without an oral airway. An experienced board-certified anesthesiologist performed 10 oral suctions on each mannequin, and 10 extubations. We counted the droplets on the anesthesiologist's gown according to the divided areas after each procedure.The mean droplet count after suction was 6.20 ± 2.201 in group O and 13.6 ± 4.300 in group X, with a significant difference between the two groups (P.001). The right and left hands were the most contaminated areas in group O (2.8 ± 1.033 droplets and 2.0 ± 0.943 droplets, respectively). The abdomen, right hand, left forearm, and left hand showed many droplets in group X. (1.3 ± 1.337 droplets, 3.1 ± 1.792 droplets, 3.2 ± 3.910 droplets, and 4.3 ± 2.214 droplets, respectively). The chest, abdomen, and left hand presented significantly more droplets in group X than in group O. The trunk area (chest and abdomen) was exposed to more droplets during extubations than during suctions.During suctions, more droplets are splattered from mannequins without oral airways than from those with oral airways. The right and left hands were the most contaminated areas in group O. Moreover, the abdomen, right hand, left forearm, and left hand presented a lot of droplets in group X. In addition, extubations contaminate wider areas (the head, neck, chest and abdomen) of an HCW than suctions.

The aim of this study was to apply registration and three-dimensional (3D) display tools to assess the evolution of intraparenchymal hemorrhage (IPH) in patients with traumatic brain injury (TBI). We identified 109 TBI patients who had two computed tomography (CT) scans within 4 days retrospectively. The IPH was manually outlined. The registration was performed in 39 lesions from 29 patients with lesion volume 1.5 cm on both baseline and follow-up CT. The center of mass (COM) of each lesion was calculated, and the distance between baseline and follow-up CT was used to evaluate the registration effect. The mean distances of COM before registration in the XYZ, XY, and YZ coordinates were 20.5 ± 10.2 mm, 17.8 ± 9.4 mm, and 15.9 ± 9.4 mm, respectively, which decreased significantly (p 0.001) to 7.9 ± 4.9, 7.8 ± 5.0, and 6.1 ± 4.1 mm after registration. A 3D short video displaying the rendering view of all lesions in 34 randomly selected patients from baseline and follow-up scans were presented side-by-side for comparison. The detection rate of new IPH lesions increased in 3D videos (100%) as compared with axial CT slices (78.6-92.9%). A very high interrater agreement (k = 0.856) on perceiving IPH lesion progression upon viewing 3D video was noted, and the absolute volume increase was significantly higher (p 0.001) for progressive lesions (median 7.36 cc) over non-progressive lesions (median 0.01 cc). Compared to patients with spontaneous hemorrhagic stroke, evaluation of multiple small traumatic hemorrhages in TBI is more challenging. The applied image analysis and visualization methods may provide helpful tools for comparing changes between serial CT scans.

Rationale and Objectives Breast segmentation using the U-net architecture was implemented and tested in independent validation datasets to quantify fibroglandular tissue volume in breast MRI. Materials and Methods Two datasets were used. The training set was MRI of 286 patients with unilateral breast cancer. The segmentation was done on the contralateral normal breasts. The ground truth for the breast and fibroglandular tissue (FGT) was obtained by using a template-based segmentation method. The U-net deep learning algorithm was implemented to analyze the training set, and the final model was obtained using 10-fold cross-validation. The independent validation set was MRI of 28 normal volunteers acquired using four different MR scanners. Dice Similarity Coefficient (DSC), voxel-based accuracy, and Pearson's correlation were used to evaluate the performance. Results For the 10-fold cross-validation in the initial training set of 286 patients, the DSC range was 0.83–0.98 (mean 0.95 ± 0.02) for breast and 0.73–0.97 (mean 0.91 ± 0.03) for FGT; and the accuracy range was 0.92–0.99 (mean 0.98 ± 0.01) for breast and 0.87–0.99 (mean 0.97 ± 0.01) for FGT. For the entire 224 testing breasts of the 28 normal volunteers in the validation datasets, the mean DSC was 0.86 ± 0.05 for breast, 0.83 ± 0.06 for FGT; and the mean accuracy was 0.94 ± 0.03 for breast and 0.93 ± 0.04 for FGT. The testing results for MRI acquired using four different scanners were comparable. Conclusion Deep learning based on the U-net algorithm can achieve accurate segmentation results for the breast and FGT on MRI. It may provide a reliable and efficient method to process large number of MR images for quantitative analysis of breast density.

Purpose To predict the neoadjuvant chemoradiation therapy (CRT) response in patients with locally advanced rectal cancer (LARC) using radiomics and deep learning based on pre-treatment MRI and a mid-radiation follow-up MRI taken 3–4 weeks after the start of CRT. Methods A total of 51 patients were included, 45 with pre-treatment, 41 with mid-radiation therapy (RT), and 35 with both MRI sets. The multi-parametric MRI protocol included T2, diffusion weighted imaging (DWI) with b-values of 0 and 800 s/mm2, and dynamic-contrast-enhanced (DCE) MRI. After completing CRT and surgery, the specimen was examined to determine the pathological response based on the tumor regression grade. The tumor ROI was manually drawn on the post-contrast image and mapped to other sequences. The total tumor volume and mean apparent diffusion coefficient (ADC) were measured. Radiomics using GLCM texture and histogram parameters, and deep learning using a convolutional neural network (CNN), were performed to differentiate pathologic complete response (pCR) vs. non-pCR, and good response (GR) vs. non-GR. Results Tumor volume decreased and ADC increased significantly in the mid-RT MRI compared to the pre-treatment MRI. For predicting pCR vs. non-pCR, combining ROI and radiomics features achieved an AUC of 0.80 for pre-treatment, 0.82 for mid-RT, and 0.86 for both MRI together. For predicting GR vs. non-GR, the AUC was 0.91 for pre-treatment, 0.92 for mid-RT, and 0.93 for both MRI together. In deep learning using CNN, combining pre-treatment and mid-RT MRI achieved a higher accuracy compared to using either dataset alone, with AUC of 0.83 for predicting pCR vs. non-pCR. Conclusion Radiomics based on pre-treatment and early follow-up multi-parametric MRI in LARC patients receiving CRT could extract comprehensive quantitative information to predict final pathologic response.

Rationale and Objectives Breast cancer occurs more frequently in the upper outer (UO) quadrant, but whether this higher cancer incidence is related to the greater amount of dense tissue is not known. Magnetic resonance imaging acquires three-dimensional volumetric images and is the most suitable among all breast imaging modalities for regional quantification of density. This study applied a magnetic resonance imaging-based method to measure quadrant percent density (QPD), and evaluated its association with the quadrant location of the developed breast cancer. Materials and Methods A total of 126 cases with pathologically confirmed breast cancer were reviewed. Only women who had unilateral breast cancer located in a clear quadrant were selected for analysis. A total of 84 women, including 47 Asian women and 37 western women, were included. An established computer-aided method was used to segment the diseased breast and the contralateral normal breast, and to separate the dense and fatty tissues. Then, a breast was further separated into four quadrants using the nipple and the centroid as anatomic landmarks. The tumor was segmented using a computer-aided method to determine its quadrant location. The distribution of cancer quadrant location, the quadrant with the highest QPD, and the proportion of cancers occurring in the highest QPD were analyzed. Results The highest incidence of cancer occurred in the UO quadrant (36 out of 84, 42.9%). The highest QPD was also noted most frequently in the UO quadrant (31 out of 84, 36.9%). When correlating the highest QPD with the quadrant location of breast cancer, only 17 women out of 84 (20.2%) had breast cancer occurring in the quadrant with the highest QPD. Conclusions The results showed that the development of breast cancer in a specific quadrant could not be explained by the density in that quadrant, and further studies are needed to find the biological reasons accounting for the higher breast cancer incidence in the UO quadrant.

Rationale and Objectives This study aimed to evaluate the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiation of four spinal lesions by using heuristic and pharmacokinetic parameters analyzed from DCE signal intensity time course. Materials and Methods DCE-MRI of 62 subjects with confirmed myeloma (n = 9), metastatic cancer (n = 22), lymphoma (n = 7), and inflammatory tuberculosis (TB) (n = 24) in the spine were analyzed retrospectively. The region of interest was placed on strongly enhanced tissues. The DCE time course was categorized as the “wash-out,” “plateau,” or “persistent enhancement” pattern. The maximum enhancement, steepest wash-in enhancement, and wash-out slope using the signal intensity at 67 seconds after contrast injection as reference were measured. The Tofts 2-compartmental pharmacokinetic model was applied to obtain Ktrans and kep. Pearson correlation between heuristic and pharmacokinetic parameters was evaluated, and receiver operating characteristic curve analysis was performed for pairwise group differentiation. Results The mean wash-out slope was −22% ± 10% for myeloma, 1% ± 0.4% for metastatic cancer, 3% ± 3% for lymphoma, and 7% ± 10% for TB, and it could significantly distinguish myeloma from metastasis (area under the curve [AUC] = 0.884), lymphoma (AUC = 1.0), and TB (AUC = 1.0) with P = .001, and distinguish metastasis from TB (AUC = 0.741) with P = .005. The kep and wash-out slope were highly correlated (r = 0.92), and they showed a similar diagnostic performance. The Ktrans was significantly correlated with the maximum enhancement (r = 0.71) and the steepest wash-in enhancement (r = 0.85), but they had inferior diagnostic performance compared to the wash-out slope. Conclusions DCE-MRI may provide additional diagnostic information, and a simple wash-out slope had the best diagnostic performance. The heuristic and pharmacokinetic parameters were highly correlated.

Rationale and Objectives Low-dose chest computed tomography (LDCT), increasingly being used for screening of lung cancer, may also be used to measure breast density, which is proven as a risk factor for breast cancer. In this study, we developed a segmentation method to measure quantitative breast density on CT images and correlated with magnetic resonance density. Materials and Methods Forty healthy women receiving both LDCT and breast magnetic resonance imaging (MRI) were studied. A semiautomatic method was applied to quantify the breast density on LDCT images. The intra- and interoperator reproducibility was evaluated. The volumetric density on MRI was obtained by using a well-established automatic template-based segmentation method. The breast volume (BV), fibroglandular tissue volume (FV), and percent breast density (PD) measured on LDCT and MRI were compared. Results The measurements of BV, FV, and PD on LDCT images yield highly consistent results, with the intraclass correlation coefficient of 0.999 for BV, 0.977 for FV, and 0.966 for PD for intraoperator reproducibility, and intraclass correlation coefficient of 0.953 for BV, 0.974 for FV, and 0.973 for PD for interoperator reproducibility. The BV, FV, and PD measured on LDCT and MRI were well correlated (all r ≥ 0.90). Bland-Altman plots showed that a larger BV and FV were measured on LDCT than on MRI. Conclusions The preliminary results showed that quantitative breast density can be measured from LDCT, and that our segmentation method could yield a high reproducibility on the measured volume and PD. The results measured on LDCT and MRI were highly correlated. Our results showed that LDCT may provide valuable information about breast density for evaluating breast cancer risk.

© 2015 The Authors. Published by Elsevier Inc. Mammographic density has been proven as an independent risk factor for breast cancer. Women with dense breast tissue visible on a mammogram have a much higher cancer risk than women with little density. A great research effort has been devoted to incorporate breast density into risk prediction models to better estimate each individual’s cancer risk. In recent years, the passage of breast density notification legislation in many states in USA requires that every mammography report should provide information regarding the patient’s breast density. Accurate definition and measurement of breast density are thus important, which may allow all the potential clinical applications of breast density to be implemented. Because the two-dimensional mammography-based measurement is subject to tissue overlapping and thus not able to provide volumetric information, there is an urgent need to develop reliable quantitative measurements of breast density. Various new imaging technologies are being developed. Among these new modalities, volumetric mammographic density methods and threedimensional magnetic resonance imaging are the most well studied. Besides, emerging modalities, including different x-ray–based, optical imaging, and ultrasound-based methods, have also been investigated. All these modalities may either overcome some fundamental problems related to mammographic density or provide additional density and/or compositional information. The present review article aimed to summarize the current established and emerging imaging techniques for the measurement of breast density and the evidence of the clinical use of these density methods from the literature.

A three-dimensional cell-to-tissue multiscale model for quantitative evaluation of the influences of vascular network on the treatment outcome of combined cancer therapy of cryosurgery and hyperthermia was developed, where the bioheat transfer equation was used to predict the thermal history in both the tumor and normal tissues, the Navier–Stokes equations were used to calculate the temperature and flow fields in the vascular network generated using fractal theory based on MRI images, and the thermal and cryo damage functions together with the injury caused by intracellular ice formation were used for evaluating the killing effect. The effects of the vascular tree on both the thermal history and intracellular ice formation in the tissues were investigated. For a tumor located at the first level of vascular network, the vascular network is significant only when the distance between the tumor and blood vessel is less than three times of the radius (R) of the tumor and this critical distance decreases to 2R for the second level of the vascular tree. For a fixed distance between tumor and blood vessel, the first level of vascular network was found to be thermally significant while the third or lower levels of vascular tree are not.

© 2015 The Authors. PURPOSE: This study presented a three-dimensional magnetic resonance (MR)–based method to separate a breast into four quadrants for quantitative measurements of the quadrant breast volume (BV) and density. METHODS: Breast MR images from 58 healthy women were studied. The breast and the fibroglandular tissue were segmented by using a computer-based algorithm. A breast was divided into four quadrants using two perpendicular planes intersecting at the nipple or the nipple-centroid line. After the separation, the BV, the fibroglandular tissue volume, and the percent density (PD) were calculated. The symmetry of the quadrant BV in the left and right breasts separated by using the nipple alone, or the nipple-centroid line, was compared. RESULTS: The quadrant separation made on the basis of the nipple-centroid line showed closer BVs in four quadrants than using the nipple alone. The correlation and agreement for the BV in corresponding quadrants of the left and the right breasts were improved after the nipple-centroid reorientation. Among the four quadrants, PD was the highest in the lower outer and the lowest in the upper outer (significant than the other three) quadrants (P

Background: We measured breast density (BD) on MRI and correlated with endogenous hormonal levels. Patients and methods: Twenty-four premenopausal women received four weekly breast MRI. A blood sample was collected on the same day of MRI. BD was measured using a computer-based algorithm. The generalized estimation equation method was applied to model mean fibroglandular tissue volume (FV) and mean percent density (PD) from predictor variables including estradiol, progesterone, and week during a cycle. Results: In week 3, a borderline significant correlation between estradiol and PD (r = 0.43, P = 0.04), estradiol and FV (r = 0.40, P = 0.05) and between progesterone and FV (r = 0.42, P = 0.04) was noted. The FV and PD measured in weeks 4 and 1 were higher than in weeks 2 and 3, adjusted for variation in endogenous estradiol and progesterone, indicating that the hormone change could not account for the changes in density. No lag effect of endogenous hormone on the change of FV or PD was noted (all P-values > 0.05). Conclusions: Our results showed that BD is not strongly associated with the endogenous hormone. Their association with breast cancer risk was likely coming from different mechanisms, and they should be considered as independent risk factors. © The Author 2013. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved.

The normal breast tissue responds to the fluctuation of endogenous hormones during a menstrual cycle (MC) and shows changes in breast density. The changes between left and right breasts of the same women were compared to evaluate the symmetrical response. Twenty-four healthy women were recruited in this study. Four weekly magnetic resonance imaging (MRI) studies were performed during one MC. A computer algorithm was used to segment the breast and the fibroglandular tissue to measure the fibroglandular tissue volume (FV) and three morphological parameters: circularity, convexity and irregularity. The coefficient of variation (CV) for each parameter measured among four MRI studies was calculated; also, the maximal percent change between two MRI studies that show the highest and the lowest FV was calculated. These parameters measured from left and right breasts were compared using Pearson correlation. For the FV, the CV measured between left and right breasts of 24 subjects was highly correlated, with r=0.91; the maximal percent difference was also highly correlated, with r=0.93. Overall, the mean left-to-right difference in the measured FV was small: 1.2%±1.1% for CV and 2.6%±2.3% for the maximal percent difference. For the three morphological parameters, the mean left-to-right percentage difference was similar to the differences seen in FV; however, these morphological parameters do not reveal a high functional symmetry between left and right breasts. The results showed that the measured FV from left and right breasts of the same woman revealed a high functional symmetry. Since endogenous hormone plays an important role in the development of breast cancer, it would be interesting to investigate whether the functional asymmetry of response in some patients is associated with the risk of developing unilateral breast cancer.

This study aimed to evaluate the influence of hormone receptor (HR) and Ki-67 proliferation markers in predicting the accuracy of magnetic resonance imaging (MRI) for measuring residual tumor size in patients with HER2-negative (HER2(-)) breast cancer receiving neoadjuvant chemotherapy (NAC).Fifty-four women were studied. Patients received AC (doxorubicin (Adriamycin)/cyclophosphamide) and/or taxane-based regimens. The accuracy of MR-determined clinical complete response (CCR) was compared to pathological complete response (pCR). The size of detectable residual tumor on MRI was correlated with pathologically diagnosed tumor size using the Pearson correlation.MRI correctly diagnosed 16 of the 17 cases of pCR. There were 8 false-negative diagnoses: 7 HR(+) and 1 HR(-). The overall sensitivity, specificity, and accuracy of MRI were 78%, 94%, and 83%, respectively. The positive predictive value was 97% and the negative predictive value was 67%. For MRI vs. pathologically determined tumor size correlation, HR(-) cancers showed a higher correlation (R = 0.79) than did HR(+) cancers (R = 0.58). A worse MRI/pathology size discrepancy was found in HR(+) cancer than in HR(-)cancer (1.6 ± 2.8 cm vs. 0.56 ± 0.9 cm; P = .05). Tumors with low Ki-67 proliferation (40%) showed a larger size discrepancy than did those with high Ki-67 proliferation (≥ 40%) (1.2 ± 2.0 cm vs. 0.4 ± 0.8 cm; P = .05).The results showed that the diagnostic performance of MRI for patients with breast cancer undergoing NAC is associated with a molecular biomarker profile. Among HER2(-)tumors, the accuracy of MRI was worse in HR(+)cancers than in HR(-)cancers and was also worse in low-proliferation tumors than in high-proliferation tumors. These findings may help in surgical planning.

Rationale and Objectives Dynamic contrast-enhanced magnetic resonance imaging is a clinical imaging modality for the detection and diagnosis of breast lesions. Analytic methods were compared for diagnostic feature selection and the performance of lesion classification to differentiate between malignant and benign lesions in patients. Materials and Methods The study included 43 malignant and 28 benign histologically proved lesions. Eight morphologic parameters, 10 gray-level co-occurrence matrix texture features, and 14 Laws texture features were obtained using automated lesion segmentation and quantitative feature extraction. Artificial neural network (ANN) and logistic regression analysis were compared for the selection of the best predictors of malignant lesions among the normalized features. Results Using ANN, the final four selected features were compactness, energy, homogeneity, and Law_LS, with an area under the receiver-operating characteristic curve (AUC) of 0.82 and accuracy of 0.76. The diagnostic performance of these four features computed on the basis of logistic regression yielded an AUC of 0.80 (95% confidence interval [CI], 0.688–0.905), similar to that of ANN. The analysis also showed that the odds of a malignant lesion decreased by 48% (95% CI, 25%–92%) for every increase of 1 standard deviation in the Law_LS feature, adjusted for differences in compactness, energy, and homogeneity. Using logistic regression with z-score transformation, a model composed of compactness, normalized radial length entropy, and gray-level sum average was selected, and it had the highest overall accuracy, 0.75, among all models, with an AUC of 0.77 (95% CI, 0.660–0.880). When logistic modeling of transformations using the Box-Cox method was performed, the most parsimonious model with predictors compactness and Law_LS had an AUC of 0.79 (95% CI, 0.672–0.898). Conclusion The diagnostic performance of models selected by ANN and logistic regression was similar. The analytic methods were found to be roughly equivalent in terms of predictive ability when a small number of variables were chosen. The robust ANN methodology uses a sophisticated nonlinear model, while logistic regression analysis provides insightful information to enhance the interpretation of the model features.

Purpose Studies specifically reporting MRI of fibrocystic change (FCC) of the breast are very few and its MRI features are not clearly known. The purpose of this study was to analyze the MRI features of FCC of the breast. Materials and Methods Thirty-one patients with pathologically proven FCC of the breast were retrospectively reviewed. The MRI study was performed using a 1.5-T MR scanner with standard bilateral breast coil. The imaging protocol consisted of pre-contrast T1-weighed imaging and dynamic contrast-enhanced axial T1-weighed imaging. The MRI features were interpreted based on the morphologic and enhancement kinetic descriptors defined on ACR BIRADS-MRI lexicon. Results FCC of the breast had a wide spectrum of morphologic and kinetic features on MRI. Two types of FCC were found, including a more diffuse type of nonmass lesion (12/31, 39%) showing benign enhancement kinetic pattern with medium wash-in in early phase (9/10, 90%) and a focal mass-type lesion (11/31, 35%) with enhancement kinetic usually showing rapid up-slope mimicking a breast cancer (8/11, 73%). Conclusion MRI is able to elaborate the diverse imaging features of FCC of the breast. Our result showed that FCC presenting as a focal mass-type lesion was usually overdiagnosed as malignancy. Understanding MRI of FCC is important to determine which cohort of patients should be followed up alone or receive aggressive management.

Proton magnetic resonance spectroscopy (1H MRS) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) provide functional information, including vascular volume, vascular permeability and choline (Cho) metabolism. In this study, we applied these two imaging modalities to quantitatively characterize 36 malignant breast lesions in 32 patients and analyzed the correlation between them. Cho concentration was quantified by single-voxel 1H MRS using water as an internal reference. The measured Cho levels ranged from 0.32 to 10.47 mmol/kg, consistent with previously reported values. In 25 mass-type lesions, the Cho concentration was significantly correlated with tumor size (r=.69, P

Longitudinal changes in cortical atrophy, ventricular enlargement, and lesion development in serial MRI scans collected from 47 healthy dogs from 1999 (8-11 years old) to 2002 (11-14 years old) were studied. The first method involved manual region of interest volumetric analysis to examine changes in cerebral and ventricular volume during the three years. No change in cerebral volume was detected but ventricular volume increased significantly each year in 2000, 2001, and 2002. Increased ventricular volume parallels early studies of age-dependent ventricular enlargement in the brain of aging beagle dogs. The second method involved a visual analysis of co-registered serial MRIs for each subject. Consistent with the volumetric results, there was no visible change in cortical thickness indicating no cerebral atrophy, but a significant increase in ventricular size was noted. Visual examination also revealed a significant increase in number of dogs who developed aging lesions over the last 2 years in 2001 and 2002. Additionally, a disproportionate number of lesions were recorded in the frontal cortex and caudate nucleus compared to other brain regions. These lesion findings are consistent with other studies in the aging dog that suggest that the frontal lobes may be particularly vulnerable to age-related changes.

Clinical images emphasizing on magnetic resonance imaging (MRI) characteristics of silicone-injected breasts with microscopic correlation were presented. A total of 16 patients with a history of silicone injection breast augmentation received MRI examinations due to palpable masses were reviewed. MRI enables the demonstration of the characteristic MR intensities in silicone-injected breasts. Four breast cancers were accurately detected by the enhancement technique including the cancers admixing within the fibrosis and silicone granuloma. This article can document the excellent ability of MRI with high microscopic correlation in examining these clinical, mammographic and ultrasonographic difficult cases. Understanding the MRI features is helpful to approach these patients. On the other hand, these illustrations indicated the importance of enhancement technique in detecting the cancers and in differentiating the angiogenic lesions from nonenhanced silicone granulomas.

Adenocarcinomas in rats and humans frequently contain perivascular, degranulating mast cells that release heparin. Protamine is a low-molecular weight, cationic polypeptide that binds avidly to heparin and neutralizes its anticoagulant properties. We hypothesized that mast-cell heparin functions as a localized anticoagulant that modulates hemostasis and blood perfusion in tumors. Consequently, systemically administered protamine should be able to neutralize the endogenous heparin within tumors, thereby inducing selective thrombosis of blood vessels within tumors. Here we demonstrate with magnetic resonance imaging (MRI) that an intravenous dose of protamine labeled with gadolinium accumulated within the parenchyma of subcutaneous implants of a mammary adenocarcinoma in Fischer 344 rats. Moreover, we show with dynamic contrast enhanced MRI that sequential intravenous doses of protamine in 12 tumor-bearing rats resulted in significantly decreased signal enhancement kinetics (blood perfusion) of the tumor. This functional impairment of MRI signal enhancement was accompanied by histological evidence of thrombosis in the blood vessels within the tumor. There was no histological evidence of thrombosis within normal liver, kidney, lung, spleen, or adjacent muscle of tumor-bearing animals that received protamine treatment or in the tumors of animals that had not been pretreated with protamine. On the basis of these results, we conclude that protamine accumulates within adenocarcinoma implants and induces selective thrombosis of blood vessels within the tumor, probably by neutralizing the endogenous heparin within tumors.

Estrogen receptor (ER) status has been used in the clinical management of breast cancer both as a predictive factor for treatment and as a prognostic factor for survival [1]. Compared with ER-positive cancer, ER-negative cancer has a poorer clinical outcome and shorter median survival [2–3]. ER-negative cancer was more aggressive, with bigger tumor size, more prominent tumor infiltration showing non-mass type enhancements on magnetic resonance imaging (MRI) features [4]. ER-negative tumors showed higher intratumoral microvessel density than did ER-positive tumors [5]. ER-negative breast carcinoma was also associated with an increased choline kinase (ChoK) activity [6]. The ChoK and its product, phosphocholine (PCho), have been implicated in human carcinogenesis. Elevated level of total choline-containing compounds (tCho) is a tissue proliferative marker for malignant tumor [7]. In this study, we reported a quantitative proton magnetic resonance spectroscopy (1H-MRS) study to further investigate if the tCho level shows difference between ER-positive and ER-negative breast cancers. The aim of our study was to determine whether in vivo 1H-MRS can provide useful information for characterizing ER status in breast cancer. Forty-seven breast cancer patients, who were scanned with the MRI/proton magnetic resonance spectroscopy (MRS) protocol from June 2004 to December 2006, were included in this study. The inclusion criteria were patients with biopsy confirmed diagnosis of malignant lesions that measured ≥1.8 cm on magnetic resonance (MR) images. All 47 patients had histopathologically an invasive ductal carcinoma. The ER status was examined by pathologists at hospital and was considered negative if immunoperoxidase staining of tumor cell nuclei in the biopsy specimen was

Mammographic density is a known risk factor for breast cancer. Studies have shown that women with increased mammographic density have higher risk of developing breast cancer compared with women with lower mammographic density [1, 2]. Both endogenous and exogenous estrogen may influence mammographic density. Mammographic density decreases after menopause when ovarian function declines. Hormonal replacement therapy, with combination of estrogen and progesterone, increases mammographic density [2], while tamoxifen, which has antiestrogenic effect, decreases mammographic density [3]. Mammographic density therefore can be regarded as a marker of the effect of estrogen on the breast tissue. To what extent mammographic density is a predictor for both hormone receptor-positive and hormone receptor-negative tumors is still unclear. Postmenopausal hormonal replacement therapy is associated with an increased risk of developing an estrogen receptor (ER)-positive tumor [4]. In symptomatic breast cancers, a significant positive association was found between ER and progesterone receptor (PgR) status of the tumors and percent density [5]. If an increased risk of ER-positive breast cancer is associated with breast density, it may be possible to find a higher density in patients who develop ER-positive cancer than ER-negative cancer. In this study, we investigated the breast density using three-dimensional (3D) magnetic resonance imaging (MRI)-based method [6] in two groups of patients, with ER-positive and ER-negative cancer. The evaluation of breast density based on mammogram may not accurately analyze the density due to projection imaging with the tissue-overlapping problem. MRI, however, provides strong soft tissue contrast distinguishing between fibroglandular and fatty tissues and a 3D view of breast tissues without compression. In a retrospective review of breast cancer patients who received breast MRI at our institution from 2004 to 2006, 80 pathologically proven patients with unilateral invasive ductal carcinoma and with complete information of ER and PgR status were included and studied (12 Asian women and 68 white women; 45 ER/PgR positive and 35 ER/PgR negative). These 80 subjects came from a sub-cohort of women in our another study focusing on the comparison of breast density between invasive ductal carcinoma and ductal carcinoma in situ. The breast density was analyzed from the contralateral normal breast of each subject, assuming symmetric bilateral breast density. It was noted that density measurements of the two breasts in women are highly correlated [7]. This study was approved by the institutional review board and was Health Insurance Portability and Accountability compliant. All patients gave written informed consent for receiving the MRI study. The breast MRI was carried out in a 1.5-T magnetic resonance (MR) with a 4-channel phased-array bilateral breast coil (Philips Medical Systems, Cleveland, OH). The imaging protocol consisted of pre-contrast T1-weighted imaging and bilateral dynamic contrast-enhanced imaging using a 3D spoiled gradient recalled radiofrequency-spoiled (RF)-fast acquisition at steady rate (FAST) pulse sequence. Thirty-two axial images covering both breasts were acquired. Noncontrast 3D SPGR (RF-FAST) T1-weighted images without fat suppression (repetition time = 8.1 ms, echo time = 4.0 ms, flip angle = 20°, matrix size = 256 × 128, field of view = 38 cm, slice thickness = 3–4 mm) were used to calculate the breast density. In this study, since tumor volume will affect the measurement of the breast density, the contralateral breast not harboring tumor was selected for density measurement. Breast density measurement was carried out based on our recently developed MR method [6]. With this method, the breast and the fibroglandular tissue were segmented using computer-assisted algorithms. After the breast was segmented out, the total breast volume was calculated. The adaptive Fuzzy C-Means was applied for segmentation of the fibroglandular tissue from the surrounding fatty tissue. After completing the segmentation from all 2D imaging slices, the volume of fibroglandular tissue was calculated, and the percent density was obtained by normalizing to the total breast volume. The mean age was 54 years in the ER-positive group and 47 years in the ER-negative group (significantly younger, P

Dogs exhibit both neuroanatomical and cognitive changes as a function of age that parallel those seen in aging humans. This study describes in vivo changes in neuroanatomical and cerebrovascular characteristics of the canine brain as a function of age in a group of dogs ranging from 4 to 15 years old. Dynamic contrast-enhanced magnetic resonance imaging (MRI) was used to measure the kinetics of contrast agents in the brain. Measures of vascular volume and blood-brain barrier (BBB) permeability were derived from a pharmacokinetic analysis. Cortical atrophy and ventricular enlargement were characteristic features of the aged canine brain. Vascular volume did not vary as a function of age and BBB permeability exhibited a nonsignificant increasing trend with age. However, BBB dysfunction was detected in one middle-aged dog that in addition to having unusually large ventricles, demonstrated an early onset of diffuse senile plaques at postmortem. These findings indicate that BBB dysfunction detected by magnetic resonance imaging may be useful for predicting and potentially diagnosing early pathological conditions.

Mammographic density has been proven as an independent risk factor for breast cancer [1, 2]. Recently, a large, population-based longitudinal study has confirmed that breast density was also associated with risk of developing ductal carcinoma in situ (DCIS) [3]. The literature report, or the biological basis for cancer development, did not indicate that there is a difference in the association between breast density and the risk of developing invasive ductal cancer (IDC) versus DCIS. However, there are no data to confirm this either. In this study, we measured and compared breast density in patients who had confirmed IDC versus DCIS, using a quantitative three-dimensional (3D) magnetic resonance (MR)-based density analysis method [4]. Magnetic resonance imaging (MRI) provides strong soft tissue contrast distinguishing between fibroglandular and fatty tissues in the entire breast without compression and has been shown capable of measuring density with a small variability. In a retrospective review of patients who participated in a breast MRI study at our institution from 2004 to 2006, a total of 141 patients with unilateral pathologically proven breast cancer were identified, including 100 patients with IDC and 41 patients with DCIS. The density was measured from the contralateral normal breast of each subject. It was commonly agreed that density of the two breasts of the same women are highly correlated [5], so the density in the normal breast of patients who had IDC versus DCIS were compared to investigate whether the density was associated with development of invasive versus in situ cancer. This study was approved by the institutional review board and was Health Insurance Portability and Accountability compliant. All patients gave written informed consent for receiving the MRI study. The MRI study was carried out using a 1.5-T MR scanner with a 4-channel phased-array bilateral breast coil (Philips Medical Systems, Cleveland, OH). The bilateral dynamic contrast-enhanced imaging was carried out using a 3D spoiled gradient recalled (radiofrequency-spoiled-fast acquisition at steady rate) pulse sequence. Thirty-two axial images with 4-mm thickness were prescribed to cover both breasts. The density was analyzed on pre-contrast T1-weighted images without fat suppression (repetition time = 8.1 ms, echo time = 4.0 ms, flip angle = 20°, matrix size = 256 × 128, field of view = 38 cm). Breast density measurement was carried out based on our published method [4]. The breast and the fibroglandular tissues were segmented using computer-assisted algorithms. The key procedures of the breast segmentation consisted of the following: (i) an initial V-shape cutting using three body landmarks to determine the posterior boundary of the breast, (ii) a fuzzy c-means (FCM)-based segmentation algorithm with the b-spline curve fitting to obtain the whole breast area, and (iii) dynamic searching to exclude the skin. For fibroglandular tissue segmentation, the adaptive FCM was applied for bias field correction to remove image intensity nonuniformities and for segmentation of the fibroglandular tissue from the surrounding fatty tissue. The volumes of the total fibroglandular tissues and the entire breast were calculated, and the ratio × 100% was defined as the percent density. The mean age was 53 years in the IDC group and 58 years in the DCIS group. Overall, the measured breast density did not show significant difference between the two groups of patients (10.9% ± 8.4% for the IDC group versus 10.4% ± 6.2% for the DCIS group, P = 0.73). Figure 1 shows the scattered plot between the percent density and the age of all patients. It was noted that there was no difference among patients with these two cancer types. However, an age dependence was observed. A decreased density was clearly noted in patients >55 years old. A logistic model was applied to analyze the difference in density, controlling for age, and the results show no significant difference between IDC versus DCIS. Figure 2 shows two case examples with IDC and DCIS. Figure 1. Scatter plot of percent breast density versus age in patients with IDC and DCIS. Decreased density in patients over age 55 is noted. IDC, invasive ductal cancer; DCIS, ductal carcinoma in situ. Figure 2. (A) A 50-year-old Asian woman with IDC; (B) a 50-year-old Caucasian woman with DCIS. IDC and DCIS lesions are indicated by arrows. The segmented fibroglandular density in the normal breast is shown. The measured percent density is 17.3% for the IDC patient ... In summary, this study showed that the breast density between patients who had IDC and DCIS did not have significant differences. Although breast density is a well-established risk factor for breast cancer, it does not predict whether a woman is more likely to develop invasive cancer or in situ cancer. Other genetic or molecular markers need to be investigated.

Information of hormonal receptors, including estrogen receptor (ER) and progesterone receptor (PR), is essential in management of breast cancer patients. In a recent study by Chen et al., it was found that ER positive and negative breast cancers demonstrated different imaging features on MRI [1]. However, since the status of PR was consistent with that of ER in most patients (ER+/PR+ or ER−/PR−) in that study, the impact of PR was not analyzed. Previous studies have shown that lack of PR expression was associated with aggressive tumors [2, 3]. In this work the association between PR status and MR imaging features was investigated. In a retrospective review of our breast MRI database from 2001 to 2007, 25 patients with ER+/PR− invasive breast cancer were found, including 21 invasive ductal cancers and 4 invasive lobular cancers. A group of 29 consecutive patients with ER+/PR+ invasive ductal cancer was taken from the previously published study [1] for comparison. The age range was 34−70 years old (mean 53) for the ER+/PR+ patients, and 34−81 years old (mean 59) for the ER+/PR− patients (P = 0.05). The MRI features between these 2 groups were compared. This study was approved by the institutional review board (IRB) and was HIPPA-compliant. All patients gave informed consent. The MRI study was performed using a 1.5 T MR scanner with a standard bilateral breast coil. The imaging protocol consisted of high-resolution pre-contrast imaging and bilateral axial dynamic contrast-enhanced imaging. For the dynamic imaging acquisition, a 3D SPGR (RFFAST) pulse sequence was prescribed. The sequence was repeated 16 times, four pre-contrast, and 12 post-contrast sets. The subtraction images at 1-min after contrast injection from 32 slices were generated, then used to form the maximum intensity projection (MIP). Tumor size was measured as the longest tumor dimension on the MIP. The enhancement kinetics was analyzed from manually-drawn region of interest on each subtraction imaging slice at 1-min post injection containing the lesion. The enhancement time course was calculated by subtracting the mean pre-contrast signal intensity from each of the subsequent 12 post-contrast signal intensities. The tumor morphology and kinetic enhancement features were analyzed based on ACR Breast Imaging Reporting and Data System (BI-RADS) MRI lexicon [4]. The axillary lymph node status was evaluated on pre-contrast sagittal view images. Most of the patients in both groups, including all ER+/PR+ cancers (29/29) and 21 of 25 ER+/PR− cancers presented as mass type lesion (P < 0.05). Four ER+/PR− breast cancers presented as non-mass type lesions, including 3 regional enhancements and one linear enhancement. The mean tumor size was significantly bigger in the ER+/PR− cancers than the ER+/PR+ cancers (2.4 ± 1.3 cm vs. 1.7 ± 1.0 cm, P < 0.05). For lesions multiplicity, 5 ER+/PR− patients (5/25, 20%) and 8 ER+/PR+ patients (8/29, 27.5%) showed multiple lesions (P=0.54). Axillary lymph nodes were detected by MRI in 9 ER+/PR− patients (9/25, 36%) and in 6 ER+/PR+ patients (6/29, 21%, P= 0.23). Regarding kinetic enhancement curve, malignant type enhancement occurred in 33 of 36 measured ER+/PR− lesions (29 washout and 4 plateau), and in 31 of 34 measured ER+/PR+ lesions (27 washout and 4 plateau). Three lesions in both groups showed the benign type continuous enhancement curve (P= 1). Table 1 summarized the imaging findings. Table 1 MR Imaging Features of ER+/PR+ and ER+/PR− Patients PR status can provide strong prognostic information on the risk of recurrence in endocrine-treated breast cancer patients [5]. ER+/PR− breast cancers are less sensitive to tamoxifen than are ER+/PR+ tumors [6-8]. In post-menopausal women, the ER+/PR− group had a worse disease free survival and overall survival compared to ER+/PR+ group [8]. It has been shown that the PR status is a better prognostic predictor than the ER status [9]. Correlation of PR with imaging features is rarely studied. In a few studies of PET uptake with prognostic factors of breast cancer, controversial results were found regarding impact of PR on maximal standardized uptake value [10-12]. In this study we found that ER+/PR− cancer has larger tumor size compared to ER+/PR+ cancer, and it is more likely to present non-mass type lesions. The finding is consistent with that of Arpino G et al. [2]. Another study by Ponzone R et al. also showed that PR negative tumors were characterized by larger size and higher tumor grade [3]. According to the ACR BI-RADS MRI lexicon [4], non-mass type lesion indicates that enhancement occurs in an area of the fibroglandular tissue without space-occupying effect. It represents a breast lesion with infiltrative process. In malignant cancer, the infiltrative process might be regarded as an aggressive tumor behavior. However, interpretation of this data should be cautious since there were 4 patients of lobular cancer included in the ER+/PR− cohort and 2 of them showed non-mass type enhancement. If these 4 patients were excluded, the difference is not significant (2/21 vs. 0/29, P= 0.17). Other imaging features, including multiple lesions, kinetic enhancement curve, and axillary lymph nodes metastasis, were not significantly different. In conclusion, in this study we compared the MR imaging features between ER+/PR+ and ER+/PR− cancers, and found that PR negative cancer had significantly bigger tumor size and more non-mass type lesions. Our study provides imaging evidence that PR in invasive breast cancer does impact imaging features and PR negative invasive breast cancer is more aggressive, consistent with findings of several previous studies showing that missing PR in ER positive breast cancer is more aggressive than its counterpart of ER+/PR+ cancer.