Inter-Rater Variability in the Evaluation of Lung Ultrasound in Videos Acquired from COVID-19 Patients

Artículo firmado por 42 autores. This research was partially funded by CDTI (Spanish acronym: Centre for Industrial Technological Development), funding number COI-20201153. Partially supported by the Google Cloud Research Credits program with the funding number GCP19980904, by the project RTI2018-099118-A-I00 founded by MCIU/AEI/FEDER UE and by the European Commission-NextGenerationEU, through CSIC's Global Health Platform (PTI Salud Global).Lung ultrasound (LUS) allows for the detection of a series of manifestations of COVID-19, such as B-lines and consolidations. The objective of this work was to study the inter-rater reliability (IRR) when detecting signs associated with COVID-19 in the LUS, as well as the performance of the test in a longitudinal or transverse orientation. Thirty-three physicians with advanced experience in LUS independently evaluated ultrasound videos previously acquired using the ULTRACOV system on 20 patients with confirmed COVID-19. For each patient, 24 videos of 3 s were acquired (using 12 positions with the probe in longitudinal and transverse orientations). The physicians had no information about the patients or other previous evaluations. The score assigned to each acquisition followed the convention applied in previous studies. A substantial IRR was found in the cases of normal LUS (kappa = 0.74), with only a fair IRR for the presence of individual B-lines (kappa = 0.36) and for confluent B-lines occupying 50% (kappa = 0.50). No statistically significant differences between the longitudinal and transverse scans were found. The IRR for LUS of COVID-19 patients may benefit from more standardized clinical protocols.Depto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasTRUECentre for Industrial Technological Development (CDTI)Google Cloud Research Credits programMCIU/AEI/FEDER UEEuropean Commission-NextGenerationEU, through CSIC's Global Health Platform (PTI Salud Global)pu

Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning

Funded by the Spanish Ministry of Economic Affairs and Digital Transformation (Project MIA.2021.M02.0005 TARTAGLIA, from the Recovery, Resilience, and Transformation Plan financed by the European Union through Next Generation EU funds). TARTAGLIA takes place under the R&D Missions in Artificial Intelligence program, which is part of the Spain Digital 2025 Agenda and the Spanish National Artificial Intelligence Strategy.Ultrasound (US) is a flexible imaging modality used globally as a first-line medical exam procedure in many different clinical cases. It benefits from the continued evolution of ultrasonic technologies and a well-established US-based digital health system. Nevertheless, its diagnostic performance still presents challenges due to the inherent characteristics of US imaging, such as manual operation and significant operator dependence. Artificial intelligence (AI) has proven to recognize complicated scan patterns and provide quantitative assessments for imaging data. Therefore, AI technology has the potential to help physicians get more accurate and repeatable outcomes in the US. In this article, we review the recent advances in AI-assisted US scanning. We have identified the main areas where AI is being used to facilitate US scanning, such as standard plane recognition and organ identification, the extraction of standard clinical planes from 3D US volumes, and the scanning guidance of US acquisitions performed by humans or robots. In general, the lack of standardization and reference datasets in this field makes it difficult to perform comparative studies among the different proposed methods. More open-access repositories of large US datasets with detailed information about the acquisition are needed to facilitate the development of this very active research field, which is expected to have a very positive impact on US imaging.Depto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasTRUEMinistry of Economic Affairs and Digital Transformation from the Recovery, Resilience, and Transformation PlanNext Generation EU fundspu

Photoacoustic dose monitoring in clinical high-energy photon beams

This work describes all stages of development (setup, optimization, performance, and first experimental measurements) of an acoustic sensor that can be used for range monitoring and dosimetry of clinical radiotherapy beams. The detection device consists of an ultrasonic transducer, a combination of preamplifiers and differential amplifiers with filtered outputs and a digital oscilloscope. Simulations of the experimental setup were carried out to study the optimal measurement geometry and choice of transducer. The dose distributions were calculated with the Monte Carlo code FLUKA, while the acoustic simulations were performed with the analytical wave transport code k-Wave. The temporal profiles of the dose pulses, in the order of mu s, were measured with a scintillating crystal coupled to a photomultiplier and used as input for the acoustic simulation. Measurements were performed in a Cyberknife (TM) radiosurgery beam and a TrueBeam unit. A lead block was submerged in water and placed partially or totally in the irradiation field in order to increase the acoustic signal. Photoacoustic signals were detected with both beams with the expected shape and time-delay, after the frequency response of the detection system was taken into account. The proposed setup can detect photoacoustic signals originating from the penumbra of the treatment fields after being processed with the appropriate image analysis tools

Machine Learning of Multi-Modal Tumor Imaging Reveals Trajectories of Response to Precision Treatment

"Funding: This work received funding from the Cancer Research for Personalized Medicine—CARPEM project (Site de Recherche Intégré sur le Cancer SIRIC), from the Plan Cancer Physicancer (grant C16025KS), and from the Région Ile-de-France. In vivo imaging was performed at the Life Imaging Facility of Université Paris Cité (Plateforme Imageries du Vivant - PIV), supported by France Life Imaging (grant ANR-11-INBS-0006) and Infrastructures Biologie-Santé (IBiSa). Nesrin Mansouri received a scholarship from the Ministère de l’Enseignement Supérieur et de la Recherche. This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement no. 101030046 of M. P.-L." "Acknowledgments: The authors thank Laure Fournier, Judith Favier, Charlotte Lussey-Lepoutre,Irène Buvat, Béatrice Berthon and J.M. Udías for rich scientific advice and discussions"The standard assessment of response to cancer treatments is based on gross tumor characteristics, such as tumor size or glycolysis, which provide very indirect information about the effect of precision treatments on the pharmacological targets of tumors. Several advanced imaging modalities allow for the visualization of targeted tumor hallmarks. Descriptors extracted from these images can help establishing new classifications of precision treatment response. We propose a machine learning (ML) framework to analyze metabolic–anatomical–vascular imaging features from positron emission tomography, ultrafast Doppler, and computed tomography in a mouse model of paraganglioma undergoing anti-angiogenic treatment with sunitinib. Imaging features from the follow-up of sunitinib-treated (n = 8, imaged once-per-week/6-weeks) and sham-treated (n = 8, imaged once-per-week/3-weeks) mice groups were dimensionally reduced and analyzed with hierarchical clustering Analysis (HCA). The classes extracted from HCA were used with 10 ML classifiers to find a generalized tumor stage prediction model, which was validated with an independent dataset of sunitinib-treated mice. HCA provided three stages of treatment response that were validated using the best-performing ML classifier. The Gaussian naive Bayes classifier showed the best performance, with a training accuracy of 98.7 and an average area under curve of 100. Our results show that metabolic–anatomical–vascular markers allow defining treatment response trajectories that reflect the efficacy of an anti-angiogenic drug on the tumor target hallmark.Depto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasTRUECancer Research for Personalized Medicine—CARPEM project (Site de Recherche Intégré sur le Cancer SIRIC)Plan Cancer PhysicancerRégion Ile-de-Francee Life Imaging Facility of Université Paris Cité (Plateforme Imageries du Vivant - PIV)France Life ImagingInfrastructures Biologie-Santé (IBiSa)Ministère de l’Enseignement Supérieur et de la RechercheEuropean Union’s Horizon 2020 research and innovation programpu

Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [F-18]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions

Simple Summary Breast cancer is a leading cause of morbidity and mortality worldwide. The metastatic disease is largely responsible for cancer patient deaths, and its treatment implies usually different therapies. In this context, the prediction of response to treatment or depiction of treatment-resistant phenotypes is essential in clinical practice, especially in the new era of precision medicine. In this study, we used several combinations of feature selection methods and machine-learning classifiers to construct predictive models of the metabolic response to the treatment of metastatic breast cancer lesions. These models were based on clinical variables and radiomic features extracted from 2-deoxy-2-[F-18]fluoro-D-glucose positron emission tomography/computed tomography ([F-18]F-FDG PET/CT) images, obtained prior to the treatment. Our main goal was to know if this prediction was feasible and to identify those combinations with better predictive performance. We found that several combinations were successful to predict the metabolic response to treatment, of which the least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM) had the best mean performance in terms of area under the curve, in both training and validation cohorts. Model performances depended largely on the selected combinations. Background: This study aimed to identify optimal combinations between feature selection methods and machine-learning classifiers for predicting the metabolic response of individual metastatic breast cancer lesions, based on clinical variables and radiomic features extracted from pretreatment [F-18]F-FDG PET/CT images. Methods: A total of 48 patients with confirmed metastatic breast cancer, who received different treatments, were included. All patients had an [F-18]F-FDG PET/CT scan before and after the treatment. From 228 metastatic lesions identified, 127 were categorized as responders (complete or partial metabolic response) and 101 as non-responders (stable or progressive metabolic response), by using the percentage changes in SULpeak (peak standardized uptake values normalized for body lean body mass). The lesion pool was divided into training (n = 182) and testing cohorts (n = 46); for each lesion, 101 image features from both PET and CT were extracted (202 features per lesion). These features, along with clinical and pathological information, allowed the prediction model's construction by using seven popular feature selection methods in cross-combination with another seven machine-learning (ML) classifiers. The performance of the different models was investigated with the receiver-operating characteristic curve (ROC) analysis, using the area under the curve (AUC) and accuracy (ACC) metrics. Results: The combinations, least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM), or random forest (RF) had the highest AUC in the cross-validation, with 0.93 +/- 0.06 and 0.92 +/- 0.03, respectively, whereas Lasso + neural network (NN) or SVM, and mutual information (MI) + RF, had the higher AUC and ACC in the validation cohort, with 0.90/0.72, 0.86/0.76, and 87/85, respectively. On average, the models with Lasso and models with SVM had the best mean performance for both AUC and ACC in both training and validation cohorts. Conclusions: Image features obtained from a pretreatment [F-18]F-FDG PET/CT along with clinical vaiables could predict the metabolic response of metastatic breast cancer lesions, by their incorporation into predictive models, whose performance depends on the selected combination between feature selection and ML classifier methods

MUBI Research Platform: Recent Advances and New Developments

The Multi-Modal Ultrasound Breast Imaging platform (MUBI) is a joint development of the Spanish National Research Council (CSIC) and the Complutense University of Madrid (UCM). It is composed of a ring of 16 transducers with 128 elements each, with a diameter of 22 cm, and working with a central frequency of 3.2 MHz. The total number of elements is 2048, and the electronic system can operate in single-element emission-reception or phased-array modes. It is intended to be a flexible platform for multi-modal ultrasound imaging research, combining reflection, transmission, and elasticity imaging, mainly oriented to breast diagnosis. In this work, research advances on the system are presented: Development of a fully operational prototype to be used with patients, design and manufacturing of calibration and anthropomorphic phantoms and design of an elastography device based on a waveform generator controlled vibrator. The work describes the design and laboratory preliminary tests of the new system components.Peer reviewe

Clinical guidelines for endoscopic mucosal resection of non-pedunculated colorectal lesions

This document summarizes the contents of the Clinical Guidelines for the Endoscopic Mucosal Resection of Non-Pedunculated Colorectal Lesions that was developed by the working group of the Spanish Society of Digestive Endoscopy (GSEED of Endoscopic Resection). This document presents recommendations for the endoscopic management of superficial colorectal neoplastic lesions

Clinical guidelines for endoscopic mucosal resection of non-pedunculated colorectal lesions.

This document summarizes the contents of the Clinical Guidelines for the Endoscopic Mucosal Resection of Non-Pedunculated Colorectal Lesions that was developed by the working group of the Spanish Society of Digestive Endoscopy (GSEED of Endoscopic Resection). This document presents recommendations for the endoscopic management of superficial colorectal neoplastic lesions

Guía clínica para la resección mucosa endoscópica de lesiones colorrectales no pediculadas.

This document summarizes the contents of the Clinical Guidelines for the Endoscopic Mucosal Resection of Non-Pedunculated Colorectal Lesions that was developed by the working group of the Spanish Society of Digestive Endoscopy (GSEED of Endoscopic Resection). This document presents recommendations for the endoscopic management of superficial colorectal neoplastic lesions

Espacios y destinos turísticos en tiempos de globalización y crisis

2 volúmenesXII Coloquio de Geografía del Turismo, Ocio y Recreación de la Asociación de Geógrafos Españoles. Colmenarejo (Madrid), del 17 al 19 de junio de 2010.Este libro ha sido editado con la colaboración económica del Ministerio de Ciencia e Innovación (ref. CS02010-10416-E)