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A bioimage informatics based reconstruction of breast tumor microvasculature with computational blood flow predictions.

ABSTRACT: Induction of tumor angiogenesis is among the hallmarks of cancer and a driver of metastatic cascade initiation. Recent advances in high-resolution imaging enable highly detailed three-dimensional geometrical representation of the whole-tumor microvascular architecture. This enormous increase in complexity of image-based data necessitates the application of informatics methods for the analysis, mining and reconstruction of these spatial graph data structures. We present a novel methodology that combines ex-vivo high-resolution micro-computed tomography imaging data with a bioimage informatics algorithm to track and reconstruct the whole-tumor vasculature of a human breast cancer model. The reconstructed tumor vascular network is used as an input of a computational model that estimates blood flow in each segment of the tumor microvascular network. This formulation involves a well-established biophysical model and an optimization algorithm that ensures mass balance and detailed monitoring of all the vessels that feed and drain blood from the tumor microvascular network. Perfusion maps for the whole-tumor microvascular network are computed. Morphological and hemodynamic indices from different regions are compared to infer their role in overall tumor perfusion.

SUBMITTER: Stamatelos SK

PROVIDER: S-EPMC3977934 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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A bioimage informatics based reconstruction of breast tumor microvasculature with computational blood flow predictions.

Stamatelos Spyros K SK Kim Eugene E Pathak Arvind P AP Popel Aleksander S AS

Microvascular research 20131214

Induction of tumor angiogenesis is among the hallmarks of cancer and a driver of metastatic cascade initiation. Recent advances in high-resolution imaging enable highly detailed three-dimensional geometrical representation of the whole-tumor microvascular architecture. This enormous increase in complexity of image-based data necessitates the application of informatics methods for the analysis, mining and reconstruction of these spatial graph data structures. We present a novel methodology that c ...[more]

PMID: 24342178

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Project description:BackgroundMetamorphosis in insects transforms the larval into an adult body plan and comprises the destruction and remodeling of larval and the generation of adult tissues. The remodeling of larval into adult muscles promises to be a genetic model for human atrophy since it is associated with dramatic alteration in cell size. Furthermore, muscle development is amenable to 3D in vivo microscopy at high cellular resolution. However, multi-dimensional image acquisition leads to sizeable amounts of data that demand novel approaches in image processing and analysis.ResultsTo handle, visualize and quantify time-lapse datasets recorded in multiple locations, we designed a workflow comprising three major modules. First, the previously introduced TLM-converter concatenates stacks of single time-points. The second module, TLM-2D-Explorer, creates maximum intensity projections for rapid inspection and allows the temporal alignment of multiple datasets. The transition between prepupal and pupal stage serves as reference point to compare datasets of different genotypes or treatments. We demonstrate how the temporal alignment can reveal novel insights into the east gene which is involved in muscle remodeling. The third module, TLM-3D-Segmenter, performs semi-automated segmentation of selected muscle fibers over multiple frames. 3D image segmentation consists of 3 stages. First, the user places a seed into a muscle of a key frame and performs surface detection based on level-set evolution. Second, the surface is propagated to subsequent frames. Third, automated segmentation detects nuclei inside the muscle fiber. The detected surfaces can be used to visualize and quantify the dynamics of cellular remodeling. To estimate the accuracy of our segmentation method, we performed a comparison with a manually created ground truth. Key and predicted frames achieved a performance of 84% and 80%, respectively.ConclusionsWe describe an analysis pipeline for the efficient handling and analysis of time-series microscopy data that enhances productivity and facilitates the phenotypic characterization of genetic perturbations. Our methodology can easily be scaled up for genome-wide genetic screens using readily available resources for RNAi based gene silencing in Drosophila and other animal models.

Project description:Importance:Making a good decision about breast reconstruction requires predicting how one would feel after the procedure, but people tend to overestimate the impact of events on future well-being. Objective:To assess how well patients predict future well-being after mastectomy, with or without immediate reconstruction, with the following a priori hypotheses: Patients will overestimate the negative impact of mastectomy and positive impact of reconstruction, and prediction accuracy will be associated with decision satisfaction and decision regret. Design, Setting, and Participants:This prospective cohort survey study was conducted at a single, multidisciplinary academic oncology clinic from July 2012 to February 2014. Adult women undergoing mastectomy for stage 1, 2, or 3 invasive ductal or lobular breast cancer, ductal carcinoma in situ, or prophylaxis were invited to participate. Data analysis was conducted from September 2015 to October 2017. Exposures:Mastectomy only or mastectomy with immediate reconstruction. Main Outcomes and Measures:Preoperative measures predicted were 12-month happiness (Cantril Ladder) and quality of life, predicted satisfaction with breasts, sexual attractiveness, breast numbness, and pain (measured with BreastQ single items). Measures at 12 months postoperative added the Decision Regret Scale and Satisfaction With Decisions Scale. Results:Of 214 eligible patients, 182 consecutive patients were approached, and 145 enrolled (80%). Of these 145 patients, 131 returned surveys (72%) and 111 of these remained at 12 months (88%). Fifteen who had delayed reconstruction were excluded from analysis, leaving a final cohort of 96 women; 54 had not had reconstruction and 42 had had reconstruction. The mean (SD) age of the cohort was 53.9 (12.1) years; 73 (76%) were white; 50 (52%) were college graduates; 54 (56%) were privately insured; 69 (72%) had disease at stages 0, 1, or 2; and 31 (32%) received adjuvant radiation. Patients having mastectomy without reconstruction underestimated future well-being in all domains. Differences were significant for quality of life scores (mean predicted, 68 vs mean actual, 74; t50, -2.47; P?=?.02) and satisfaction with breasts-clothed (mean predicted, 2.4 vs mean actual, 2.8; t49, -2.11; P?=?.04). Patients undergoing mastectomy with reconstruction overestimated future well-being in all but 1 domain. Differences were significant for satisfaction with breasts-unclothed (mean predicted, 3.1 vs mean actual, 2.6; t41, 2.70; P?=?.01); sexual attractiveness-clothed (mean predicted, 3.7 vs mean actual, 3.3; t39, 2.29; P?=?.03); sexual attractiveness-unclothed (mean predicted, 3.3 vs mean actual, 2.3; t40, 5.57; P?<?.001). Both groups experienced more numbness than predicted (mean predicted, 2.79 and 2.72 for mastectomy only and mastectomy with reconstruction groups, respectively; mean actual, 3.52 and 3.56, respectively; t47, -3.4 and t38, -2.9, respectively; P?<?.01). Patients who were less happy (??=?6.3; P?=?.02) or had greater pain (??=?8.7; P?<?.001) than predicted had greater regret. Conclusions and Relevance:Patients underestimated future well-being after mastectomy and overestimated well-being after reconstruction. Misprediction was associated with regret. Decision support for breast reconstruction should address expectations about well-being.

Dataset Information

A bioimage informatics based reconstruction of breast tumor microvasculature with computational blood flow predictions.

Publications

A bioimage informatics based reconstruction of breast tumor microvasculature with computational blood flow predictions.

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