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Background: Bone metastases are common and devastating to cancer patients. Existing treatments do not specifically target the disease sites and are therefore ineffective and systemically toxic. Here we present a new strategy to treat bone metastasis by targeting both the cancer cells (“the seed”), and their surrounding niche (“the soil”), using stem cells engineered to home to the bone metastatic niche and to maximise local delivery of multiple therapeutic factors. Methods: We used mesenchymal stem cells engineered using mRNA to simultaneously express P-selectin glycoprotein ligand-1 (PSGL-1)/Sialyl-Lewis X (SLEX) (homing factors), and modified versions of cytosine deaminase (CD) and osteoprotegerin (OPG) (therapeutic factors) to target and treat breast cancer bone metastases in two mouse models, a xenograft intratibial model and a syngeneic model of spontaneous bone metastasis. Findings: We first confirmed that MSC engineered using mRNA produced functional proteins (PSGL-1/SLEX, CD and OPG) using various in vitro assays. We then demonstrated that mRNA-engineered MSC exhibit enhanced homing to the bone metastatic niche likely through interactions between PSGL-1/SLEX and P-selectin expressed on tumour vasculature. In both the xenograft intratibial model and syngeneic model of spontaneous bone metastasis, engineered MSC can effectively kill tumour cells and preserve bone integrity. The engineered MSC also exhibited minimal toxicity in vivo, compared to its non-targeted chemotherapy counterpart (5-fluorouracil). Interpretation: Our combinatorial targeting of both the cancer cells and the niche represents a simple, safe and effective way to treat metastatic bone diseases, otherwise difficult to manage with existing strategies. It can also be applied to other cell types (e.g., T cells) and cargos (e.g., genome editing components) to treat a broad range of cancer and other complex diseases. Fund: National Institutes of Health, National Cancer Institute of the National Institutes of Health, Department of Defense, California Institute of Regenerative Medicine, National Science Foundation, Baylx Inc., and Fondation ARC pour la recherche sur le cancer. Keywords: Bone metastases, Cell therapy, Mesenchymal stem cell, mRNA engineering, Combination therapy

Objectives: To determine if radiomics analysis based on preoperative computed tomography (CT) can predict early postoperative recurrence of giant cell tumor of bone (GCTB) in the spine. Methods: In a retrospective review, 62 patients with pathologically confirmed spinal GCTB from March 2008 to February 2018, with a minimum follow-up of 24 months, were identified. The mean follow-up was 73.7 months (range, 28.7–152.1 months). The clinical information including age, gender, lesion location, multi-vertebral involvement, and surgical methods, were obtained. CT images acquired before the operation were retrieved for radiomics analysis. For each case, the tumor regions of interest (ROI) was manually outlined, and a total of 107 radiomics features were extracted. The features were selected via the sequential selection process by using the support vector machine (SVM), then used to construct classification models with Gaussian kernels. The differentiation between recurrence and non-recurrence groups was evaluated by ROC analysis, using 10-fold cross-validation. Results: Of the 62 patients, 17 had recurrence with a recurrence rate of 27.4%. None of the clinical information was significantly different between the two groups. Patients receiving curettage had a higher recurrence rate (6/16 = 37.5%) compared to patients receiving TES (6/26 = 23.1%) or intralesional spondylectomy (5/20 = 25%). The final radiomics model was built using 10 selected features, which achieved an accuracy of 89% with AUC of 0.78. Conclusions: The radiomics model developed based on pre-operative CT can achieve a high accuracy to predict the recurrence of spinal GCTB. Patients who have a high risk of early recurrence should be treated more aggressively to minimize recurrence.

Accurate segmentation of the breast region of interest (BROI) and breast density (BD) is a significant challenge during the analysis of breast MR images. Most of the existing methods for breast segmentation are semi-automatic and limited in their ability to achieve accurate results. This is because of difficulties in removing landmarks from noisy magnetic resonance images (MRI) due to similar intensity levels and the close connection to BROI. This study proposes an innovative, fully automatic and fast segmentation approach to identify and remove landmarks such as the heart and pectoral muscles. The BROI segmentation is carried out with a framework consisting of three major steps. Firstly, we use adaptive wiener filtering and k-means clustering to minimize the influence of noises, preserve edges and remove unwanted artefacts. The second step systematically excludes the heart area by utilizing active contour based level sets where initial contour points are determined by the maximum entropy thresholding and convolution method. Finally, a pectoral muscle is removed by using morphological operations and local adaptive thresholding on MR images. Prior to the elimination of the pectoral muscle, the MR image is sub divided into three sections: left, right, and central based on the geometrical information. Subsequently, a BD segmentation is achieved with 4 level fuzzy c-means (FCM) thresholding on the denoised BROI segmentation. The proposed method is validated using the 1350 breast images from 15 female subjects. The pixel-based quantitative analysis showed excellent segmentation results when compared with manually drawn BROI and BD. Furthermore, the presented results in terms of evaluation matrices: Acc, Sp, AUC, MR, P, Se and DSC demonstrate the high quality of segmentations using the proposed method. The average computational time for the segmentation of BROI and BD is 1 minute and 50 seconds.

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 three-dimensional 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.

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 < .05). CONCLUSIONS: We presented a quantitative method to divide a breast into four quadrants. The reorientation based on the nipple-centroid line improved the left to right quadrant symmetry, and this may provide a better standardized method to measure quantitative quadrant density. The cancer occurrence rates are known to vary in different sites of a breast, and our method may provide a tool for investigating its association with the quantitative breast density.

Background: Multimorbidity, individuals suffering from two or more chronic diseases, has become a major health challenge worldwide, especially in populous and prosperous cities, where studies of this phenomenon in China are limited. We examined the prevalence, trends, patterns, and associated factors of multimorbidity from 2009 to 2018 among community-dwelling adults in Guangzhou, China. Methods: We conducted serial cross-sectional surveys for chronic diseases in Guangzhou, China, in 2009, 2013, and 2018. General and stratified prevalence were standardized using demographic data. Multivariable logistic regression and hierarchical cluster analysis were applied to identify associated factors and to assess the correlations and patterns of multimorbidity, respectively. Results: This study included 23,284 adults aged 18 and over in 2009, 18,551 in 2013, and 15,727 in 2018. The standardized prevalence of multimorbidity increased substantially, with 12.69% (95% CI: 10.45–15.33) in 2009, 25.44% (95% CI: 23.47–27.52) in 2013, and 35.13% (95% CI:32.64−37.70) in 2018 (P for trend

Introduction: With rapid increase in the aging population, falls injuries have become an important public health problem. However, limited data have been reported on the associations between meteorological factors and falls injuries in the elderly. This study assessed the epidemiology of falls injuries and explored this association in the elderly in Guangzhou, China. Methods: Data on elderly falls injury cases and meteorological variables from 2014 to 2018 in Guangzhou were collected from the Guangzhou Injury Monitoring System and Guangzhou Meteorological Bureau, respectively. The monthly average data on falls injuries and meteorological factors were applied to the data analysis. These correlations were conducted using Pearson correlation analysis. A multiple linear regression model was used to estimate the effects of meteorological factors on falls injuries in the elderly in Guangzhou, China. Results: Accounting for 49.41% of causes of elderly injury were falls in the Guangzhou Injury Monitoring System from 2014 to 2018, which occupied first place for five consecutive years. The monthly number of elderly falls injury cases was lowest in April and highest in December, and had a positive correlation with monthly mean wind speed (r = 0.187, P < 0.01) and a negative correlation with monthly atmospheric pressure (r = -0.142, P < 0.05). A multiple linear regression model was constructed (F = 10.176, P < 0.01), which explained 23.7% of the variances (R2 = 0.237). Monthly mean wind speed (β = 76.85, P < 0.01) and monthly mean atmospheric pressure (β = −3.162, P < 0.01) were independent factors affecting monthly elderly falls injuries. Conclusions: Falls are the primary cause of injury among elderly people in Guangzhou, China. Meteorological factors are related to falls injuries in the elderly population. Decreasing activity during high wind and low atmospheric pressure weather may help reduce the number of elderly falls injury cases.

PURPOSE: This study investigated the association between background parenchymal enhancement (BPE) and pathologic response to neoadjuvant chemotherapy (NAC). METHODS: A total of 46 patients diagnosed with invasive breast cancer were analyzed. Each patient had three magnetic resonance imaging (MRI) studies, one pre-treatment and two follow-up (F/U) MRI studies. BPE was measured as the averaged enhancement of the whole fibroglandular tissues. The pre-treatment BPE and the changes in the F/U MRI were compared between patients achieving pathologic complete response (pCR) versus those not. Subgroup analyses based on age, estrogen receptor (ER), and human epidermal growth factor receptor 2 (HER2) status of their cancers were also performed. RESULTS: The pre-treatment BPE was higher in the pCR group than that in the non-pCR group. Compared to baseline, BPE at F/U-1 was significantly decreased in the pCR group but not in the non-pCR group. In subgroup analysis based on age, these results were seen only in the younger group (