Papers by Luis Martí-Bonmatí
Radiología, 2015
ABSTRACT Objective To evaluate the diagnostic performance of acoustic radiation force impulse ima... more ABSTRACT Objective To evaluate the diagnostic performance of acoustic radiation force impulse imaging (ARFI) in detecting significant hepatic fibrosis in children. Material and methods Our hospital's ethics committee approved the study and all patients or their representatives provided informed written consent. We included 96 children (50 boys, 46 girls; mean age, 8 y). We also studied 16 volunteers without liver disease as controls and 80 patients with diseases that can lead to fibrosis and cirrhosis of the liver. The final sample included 31 patients with biopsies and the 16 controls. All patients underwent abdominal ultrasonography including Doppler imaging and elastography with ARFI. The ARFI value, expressed as velocity (m/s) of shear wave propagation through the tissue, was calculated by averaging 16 measurements in both liver lobes. We used one-way analysis of variance to compare means between groups; we set statistical significance at P<.05. We used Student's t-tests and chi-square tests for categorical data. Results The ARFI value in children with fibrosis ≥ F2 was higher (1.80 ± 0.45 m/s) than in controls and higher than in patients with F0-F1 (1.38 ± 0.22 m/s). The difference was significant (P<.001) for detecting F ≥ 2. Steatosis was not related with the ARFI value (Student's t-test, P>.84). Necroinflammatory activity was strongly associated with the ARFI value (Student's t-test, P<.01). Fibrosis and necroinflammatory activity were strongly associated with each other (chi-square test, P<.0001). Conclusion The speed of shear wave propagation is significantly associated with the degree of hepatic fibrosis in children.
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Nanomedicine: Nanotechnology, Biology and Medicine, 2021
Mesenchymal stem cell therapy after stroke is a promising option investigated in animal models an... more Mesenchymal stem cell therapy after stroke is a promising option investigated in animal models and clinical trials. The intravenous route is commonly used in clinical settings guarantying an adequate safety profile although low yields of engraftment. In this report, rats subjected to ischemic stroke were injected with adipose-derived stem cells (ADSCs) labeled with superparamagnetic iron oxide nanoparticles (SPIONs) applying an external magnetic field in the skull to retain the cells. Although most published studies demonstrate viability of ADSCs, only a few have used ultrastructural techniques. In our study, the application of a local magnetic force resulted in a tendency for higher yields of SPION-ADSCs targeting the brain. However, grafted cells displayed morphological signs of death, one day after administration, and correlative microscopy showed active microglia and astrocytes associated in the process of scavenging. Thus, we conclude that, although successfully targeted within the brain, SPION-ADSCs viability was rapidly compromised.
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Journal of Ultrasound in Medicine, 2020
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Cancers
Tumor segmentation is one of the key steps in imaging processing. The goals of this study were to... more Tumor segmentation is one of the key steps in imaging processing. The goals of this study were to assess the inter-observer variability in manual segmentation of neuroblastic tumors and to analyze whether the state-of-the-art deep learning architecture nnU-Net can provide a robust solution to detect and segment tumors on MR images. A retrospective multicenter study of 132 patients with neuroblastic tumors was performed. Dice Similarity Coefficient (DSC) and Area Under the Receiver Operating Characteristic Curve (AUC ROC) were used to compare segmentation sets. Two more metrics were elaborated to understand the direction of the errors: the modified version of False Positive (FPRm) and False Negative (FNR) rates. Two radiologists manually segmented 46 tumors and a comparative study was performed. nnU-Net was trained-tuned with 106 cases divided into five balanced folds to perform cross-validation. The five resulting models were used as an ensemble solution to measure training (n = 106...
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Entropy
Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is t... more Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state fre...
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European Radiology Experimental
Background Estimating the required sample size is crucial when developing and validating clinical... more Background Estimating the required sample size is crucial when developing and validating clinical prediction models. However, there is no consensus about how to determine the sample size in such a setting. Here, the goal was to compare available methods to define a practical solution to sample size estimation for clinical predictive models, as applied to Horizon 2020 PRIMAGE as a case study. Methods Three different methods (Riley’s; “rule of thumb” with 10 and 5 events per predictor) were employed to calculate the sample size required to develop predictive models to analyse the variation in sample size as a function of different parameters. Subsequently, the sample size for model validation was also estimated. Results To develop reliable predictive models, 1397 neuroblastoma patients are required, 1060 high-risk neuroblastoma patients and 1345 diffuse intrinsic pontine glioma (DIPG) patients. This sample size can be lowered by reducing the number of variables included in the model, ...
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EJNMMI Physics, 2021
Background Radiomics analysis usually involves, especially in multicenter and large hospital stud... more Background Radiomics analysis usually involves, especially in multicenter and large hospital studies, different imaging protocols for acquisition, reconstruction, and processing of data. Differences in protocols can lead to differences in the quantification of the biomarker distribution, leading to radiomic feature variability. The aim of our study was to identify those radiomic features robust to the different degrading factors in positron emission tomography (PET) studies. We proposed the use of the standardized measurements of the European Association Research Ltd. (EARL) accreditation to retrospectively identify the radiomic features having low variability to the different systems and reconstruction protocols. In addition, we presented a reproducible procedure to identify PET radiomic features robust to PET/CT imaging metal artifacts. In 27 heterogeneous homemade phantoms for which ground truth was accurately defined by CT segmentation, we evaluated the segmentation accuracy and...
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Poster: "ECR 2010 / C-2461 / Thalamus voxel-based morphometry shows differences between schi... more Poster: "ECR 2010 / C-2461 / Thalamus voxel-based morphometry shows differences between schizophrenic patients and control subjects" by: "G. Garcia Marti, L. Marti-Bonmati, O. Brotons, A. Alberich-Bayarri, R. Sanz Requena, J. Sanjuan; Valencia/ES"
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This work defines a research on data strategy focused on medical imaging and derived image biomar... more This work defines a research on data strategy focused on medical imaging and derived image biomarkers to critically assess the concept of causal inference and uncertainties. Computational observational studies will be valued to generate casual inference from real world data. Our main goal is to propose a scientific methodology that allows to estimate causalities from observational studies through quality control of large databases, definition of plausible hypotheses, using computational estimated models and artificial intelligence tools. The computational approach of radiology to precision medicine by using epidemiological strategies is based on causal inference studies relies on real-world data observational, longitudinal, case-control analysis designed (being case the presence, and control the absence of the event to be estimated). In this new research setting, we consider disease in classical epidemiology as phenotyping, response to treatment and final prognosis; and exposure equ...
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Revista española de cardiología, 2002
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Journal of the American College of Radiology, 2014
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Insights into Imaging, 2013
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Radiology, 2007
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Medical Image Analysis, 2008
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Papers by Luis Martí-Bonmatí