Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings.

We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings. A total of 256 patients with intra-axial posterior fossa tumors were identified, of whom 248 were included in machine learning analysis, with at least 6 representative subjects per each tumor pathology. The ADC histograms of solid components of tumors, structural MRI findings, and patients' age were applied to construct decision models using Classification and Regression Tree analysis. We also compared different machine learning classification algorithms (i.e., naïve Bayes, random forest, neural networks, support vector machine with linear and polynomial kernel) for dichotomized differentiation of the 5 most common tumors in our cohort: metastasis (n = 65), hemangioblastoma (n = 44), pilocytic astrocytoma (n = 43), ependymoma (n = 27), and medulloblastoma (n = 26). The decision tree model could differentiate seven tumor histopathologies with terminal nodes yielding up to 90% accurate classification rates. In receiver operating characteristics (ROC) analysis, the decision tree model achieved greater area under the curve (AUC) for differentiation of pilocytic astrocytoma (p = 0.020); and atypical teratoid/rhabdoid tumor ATRT (p = 0.001) from other types of neoplasms compared to the official clinical report. However, neuroradiologists' interpretations had greater accuracy in differentiating metastases (p = 0.001). Among different machine learning algorithms, random forest models yielded the highest accuracy in dichotomized classification of the 5 most common tumor types; and in multiclass differentiation of all tumor types random forest yielded an averaged AUC of 0.961 in training datasets, and 0.873 in validation samples. Our study demonstrates the potential application of machine learning algorithms and decision trees for accurate differentiation of brain tumors based on pretreatment MRI. Using easy to apply and understandable imaging metrics, the proposed decision tree model can help radiologists with differentiation of posterior fossa tumors, especially in tumors with similar qualitative imaging characteristics. In particular, our decision tree model provided more accurate differentiation of pilocytic astrocytomas from ATRT than by neuroradiologists in clinical reads

Evolution and implementation of radiographic response criteria in neuro-oncology

Radiographic response assessment in neuro-oncology is critical in clinical practice and trials. Conventional criteria, such as the MacDonald and response assessment in neuro-oncology (RANO) criteria, rely on bidimensional (2D) measurements of a single tumor cross-section. Although RANO criteria are established for response assessment in clinical trials, there is a critical need to address the complexity of brain tumor treatment response with multiple new approaches being proposed. These include volumetric analysis of tumor compartments, structured MRI reporting systems like the Brain Tumor Reporting and Data System, and standardized approaches to advanced imaging techniques to distinguish tumor response from treatment effects. In this review, we discuss the strengths and limitations of different neuro-oncology response criteria and summarize current research findings on the role of novel response methods in neuro-oncology clinical trials and practice

Drug capture materials based on genomic DNA-functionalized magnetic nanoparticles

Chemotherapy agents are notorious for producing severe side-effects. One approach to mitigating this off-target damage is to deliver the chemotherapy directly to a tumor via transarterial infusion, or similar procedures, and then sequestering any chemotherapeutic in the veins draining the target organ before it enters the systemic circulation. Materials capable of such drug capture are yet to be fully realized. Here, we report the covalent attachment of genomic DNA to iron-oxide nanoparticles. With these magnetic materials, we captured three common chemotherapy agents—doxorubicin, cisplatin, and epirubicin—from biological solutions. We achieved 98% capture of doxorubicin from human serum in 10 min. We further demonstrate that DNA-coated particles can rescue cultured cardiac myoblasts from lethal levels of doxorubicin. Finally, the in vivo efficacy of these materials was demonstrated in a porcine model. The efficacy of these materials demonstrates the viability of genomic DNA-coated materials as substrates for drug capture applications

Inversão sísmica bayesiana com modelagem a priori integrada com física de rocha

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro de Ciências Físicas e Matemáticas, Programa de Pós-Graduação em Física, Florianópolis, 2017.A inversão sísmica conjunta para as propriedades elásticas e petrofísicas é um problema inverso com solução não única. Existem vários fatores que afetam a precisão dos resultados como a relação estatística de física de rocha, os erros dos dados experimentais e de modelagem. Apresentamos uma metodologia para incorporar um modelo linearizado de física de rocha em uma distribuição Gaussiana multivariada. A proposta é usada para definir um modelo de mistura Gaussiana para a distribuiçãoconjunta a priori das propriedades elásticas e petrofísicas, no qual cada componente é interpretada como uma litofácies. Este processo permite introduzir uma correlação teórica entre as propriedades, com interpretação geológica específica dos parâmetros da física de rocha para cada fácies. Com base nesta modelagem a priori e no modelo convolucional, obtemos analiticamente as distribuições condicionais da amostragem de Gibbs. Em seguida, combinamos o algoritmo de amostragem com métodos de simulação geoestatística para obter a distribuição a posteriori de Bayes. Aplicamos a proposta em um conjunto de dados sísmicos reais, com três poços, para obter múltiplas realizações geoestatísticas tridimensionais das propriedades e das litofácies. A proposta é validada através de testes de poço cego e comparações com a inversão Bayesiana tradicional. Usando a probabilidade das litofácies, também calculamos a isosuperfície de probabilidade do reservatório de óleo principal do campo estudado. Além da proposta de inversão sísmica conjunta, apresentamos também uma formulação revisitada para o método de simulação geoestatística FFT-Moving Average. Nessa formulação, o filtro de correlação é derivado através de apenas um único ruído aleatório, o que permite a aplicação do método sem qualquer suposição sobre as características do ruído.Abstract : Joint seismic inversion for elastic and petrophysical properties is an inverse problem with a nonunique solution. There are several factors that affect the accuracy of the results such as the statistical rock-physics relation and observation errors. We present a general methodology to incorporate a linearized rock-physics model into a multivariate Gaussian distribution. The proposal is used to define a Gaussian mixture model for the joint prior distribution of the elastic and petrophysical properties, in which each component is interpreted as a lithofacies. This process allows to introduce a theoretical correlation between the properties with specific geological interpretation for the rock physicsparameters of each facies. Based on the prior model and on the convolutional model, we analytically obtain the conditional distributions of the Gibbs sampling. Then, we combine the sampling algorithm with geostatistical simulation methods to calculate the Bayesian posterior distribution. We applied the proposal to a real seismic data set with three wells to obtain multiple three-dimensional geostatistical simulations of the properties and the lithofacies. The proposal is validated through a blind well test and a comparison with the traditional Bayesian inversion. Using the probability of the reservoir lithofacies, we also calculated a 3D isosurface probability model of the main oil reservoir in the studied field

Repair of Perineal Hernia Following Abdominoperineal Excision with Biological Mesh: A Systematic Review.

Perineal hernia (PerH) following abdominoperineal excision (APE) procedure is a recognized complication. PerH was considered an infrequent complication of APE procedure; however, PerH rates of up to 45% have been reported in recent publications following a laparoscopic APE procedure. Various methods of repair of PerH with the use of synthetic meshes or myocutaneous flap have been described, although there is no general agreement on an optimal strategy. The use of biological meshes for different operations is growing in popularity, and these have been promoted as being superior and safer when compared to synthetic meshes. Although the use of biologics is becoming popular claims of better outcomes are largely unsupported by evidence. The aim of this systematic review is to evaluate the currently available evidence supporting the use of biologic or biosynthetic meshes for the repair of PerH that develop following an APE.This article is freely available via Open Access. Click on the Additional Link above to access the full text via the publisher's site.Published (Open Access)

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.Comment: Technical report of BraSy

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors