Dataset Information

Transformation of Breast Reconstruction via Additive Biomanufacturing.

ABSTRACT: Adipose tissue engineering offers a promising alternative to current breast reconstruction options. However, the conventional approach of using a scaffold in combination with adipose-derived precursor cells poses several problems in terms of scalability and hence clinical feasibility. Following the body-as-a-bioreactor approach, this study proposes a unique concept of delayed fat injection into an additive biomanufactured and custom-made scaffold. Three study groups were evaluated: Empty scaffold, Scaffold containing 4?cm(3) lipoaspirate and Empty scaffold +2-week prevascularisation period. In group 3, of prevascularisation, 4?cm(3) of lipoaspirate was injected into scaffolds after 2 weeks. Using a well-characterised additive biomanufacturing technology platform, patient-specific scaffolds made of medical-grade-polycaprolactone were designed and fabricated. Scaffolds were implanted in subglandular pockets in immunocompetent minipigs (n?=?4) for 24-weeks. Angiogenesis and adipose tissue regeneration were observed in all constructs. Histological evaluation showed that the prevascularisation?+?lipoaspirate group had the highest relative area of adipose tissue (47.32%?±?4.12) which was significantly higher than both lipoaspirate-only (39.67%?±?2.04) and empty control group (8.31%?±?8.94) and similar to native breast tissue (44.97%?±?14.12). This large preclinical animal study provides proof-of-principle that the clinically applicable prevascularisation and delayed fat-injection techniques can be used for regeneration of large volumes of adipose tissue.

SUBMITTER: Chhaya MP

PROVIDER: S-EPMC4908382 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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Transformation of Breast Reconstruction via Additive Biomanufacturing.

Chhaya Mohit P MP Balmayor Elizabeth R ER Hutmacher Dietmar W DW Schantz Jan-Thorsten JT

Scientific reports 20160615

Adipose tissue engineering offers a promising alternative to current breast reconstruction options. However, the conventional approach of using a scaffold in combination with adipose-derived precursor cells poses several problems in terms of scalability and hence clinical feasibility. Following the body-as-a-bioreactor approach, this study proposes a unique concept of delayed fat injection into an additive biomanufactured and custom-made scaffold. Three study groups were evaluated: Empty scaffol ...[more]

PMID: 27301425

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Project description:Network representations of biological systems are widespread and reconstructing unknown networks from data is a focal problem for computational biologists. For example, the series of biochemical reactions in a metabolic pathway can be represented as a network, with nodes corresponding to metabolites and edges linking reactants to products. In a different context, regulatory relationships among genes are commonly represented as directed networks with edges pointing from influential genes to their targets. Reconstructing such networks from data is a challenging problem receiving much attention in the literature. There is a particular need for approaches tailored to time-series data and not reliant on direct intervention experiments, as the former are often more readily available. In this paper, we introduce an approach to reconstructing directed networks based on dynamic systems models. Our approach generalizes commonly used ODE models based on linear or nonlinear dynamics by extending the functional class for the functions involved from parametric to nonparametric models. Concomitantly we limit the complexity by imposing an additive structure on the estimated slope functions. Thus the submodel associated with each node is a sum of univariate functions. These univariate component functions form the basis for a novel coupling metric that we define in order to quantify the strength of proposed relationships and hence rank potential edges. We show the utility of the method by reconstructing networks using simulated data from computational models for the glycolytic pathway of Lactocaccus Lactis and a gene network regulating the pluripotency of mouse embryonic stem cells. For purposes of comparison, we also assess reconstruction performance using gene networks from the DREAM challenges. We compare our method to those that similarly rely on dynamic systems models and use the results to attempt to disentangle the distinct roles of linearity, sparsity, and derivative estimation.

Project description:Background Previous studies have suggested that breast parenchymal texture features may reflect the biologic risk factors associated with breast cancer development. Therefore, combining the characteristics of normal parenchyma from the contralateral breast with radiomic features of breast tumors may improve the accuracy of digital mammography in the diagnosis of breast cancer. Purpose To determine whether the addition of radiomic analysis of contralateral breast parenchyma to the characterization of breast lesions with digital mammography improves lesion classification over that with radiomic tumor features alone. Materials and Methods This HIPAA-compliant, retrospective study included 182 patients (age range, 25-90 years; mean age, 55.9 years ± 14.9) who underwent mammography between June 2002 and July 2009. There were 106 malignant and 76 benign lesions. Automatic lesion segmentation and radiomic analysis were performed for each breast lesion. Radiomic texture analysis was applied in the normal regions of interest in the contralateral breast parenchyma to assess the mammographic parenchymal patterns. The classification performance of both individual features and the output from a Bayesian artificial neural network classifier was evaluated with the leave-one-patient-out method by using the area under the receiver operating characteristic curve (AUC) as the figure of merit in the task of differentiating between malignant and benign lesions. Results The performance of the combined lesion and parenchyma classifier in the differentiation between malignant and benign mammographic lesions was better than that with the lesion features alone (AUC = 0.84 ± 0.03 vs 0.79 ± 0.03, respectively; P = .047). Overall, six radiomic features-spiculation, margin sharpness, size, circularity from the tumor feature set, and skewness and power law beta from the parenchymal feature set-were selected more than 50% of the time during the feature selection process on the combined feature set. Conclusion Combining quantitative radiomic data from tumors with contralateral parenchyma characterizations may improve diagnostic accuracy for breast cancer. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Shaffer in this issue.

Dataset Information

Transformation of Breast Reconstruction via Additive Biomanufacturing.

Publications

Transformation of Breast Reconstruction via Additive Biomanufacturing.

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