A Decision Support System Based on BI-RADS and Radiomic Classifiers to Reduce False Positive Breast Calcifications at Digital Breast Tomosynthesis: A Preliminary Study

Digital breast tomosynthesis (DBT) studies were introduced as a successful help for the detection of calcification, which can be a primary sign of cancer. Expert radiologists are able to detect suspicious calcifications in DBT, but a high number of calcifications with non-malignant diagnosis at biopsy have been reported (false positives, FP). In this study, a radiomic approach was developed and applied on DBT images with the aim to reduce the number of benign calcifications addressed to biopsy and to give the radiologists a helpful decision support system during their diagnostic activity. This allows personalizing patient management on the basis of personalized risk. For this purpose, 49 patients showing microcalcifications on DBT images were retrospectively included, classified by BI-RADS (Breast Imaging-Reporting and Data System) and analyzed. After segmentation of microcalcifications from DBT images, radiomic features were extracted. Features were then selected with respect to their stability within different segmentations and their repeatability in test–retest studies. Stable radiomic features were used to train, validate and test (nested 10-fold cross-validation) a preliminary machine learning radiomic classifier that, combined with BI-RADS classification, allowed a reduction in FP of a factor of 2 and an improvement in positive predictive value of 50%

Synergism of Au and Ru Nanoparticles in Low-Temperature Photoassisted CO2 Methanation

This is the peer reviewed version of the following article:Mateo-Mateo, Diego, De Masi, Deborah , Albero-Sancho, Josep, Lacroix, Lisa-Marie , Fazzini, Pier-Francesco , Chaudret, Bruno , García Gómez, Hermenegildo. (2018). Synergism of Au and Ru Nanoparticles in Low-Temperature Photoassisted CO2 Methanation.Chemistry - A European Journal, 24, 69, 18436-18443. DOI: 10.1002/chem.201803022, which has been published in final form at http://doi.org/10.1002/chem.201803022. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions[EN] Au and Ru nanoparticles have been deposited on Siralox® substrate by impregnation and chemical reduction, respectively (Au-Ru-S). The as-prepared material has demonstrated to be very active for the selective CO2 metanation to CH4 at temperatures below 250 oC. In addition, Au-Ru-S exhibits CH4 production enhancement upon UV-Vis light irradiation starting at temepratures higher than 200 oC, although the contribution of the photoassisted pathway of CH4 production decreases as temperature increases. Thus, a maximum CH4 production of 204 mmol/gRu at 250 oC upon 100 mW/cm2 irradiation was achieved. Control experiments using Ru-S and Au-S materials revealed that Ru nanoparticles are the CO2 methanation active sites, while Au NPs contribute harvesting light, mainly visible as consequence of the strong Au plasmon band centrered at 529 nm. The visible light absorbed by Au NPs plasmon could act as local heaters of neighbouring Ru NPs, increasing their temperature and enhancing CH4 production.D. M., J.A., and H.G. thank the Spanish Ministry of Economy and Competitiveness (Severo Ochoa SEV2016-0683 and CTQ2015-69563-CO2-1), Generalitat Valenciana (Prometeo 2017-083) for financial support. J.A. and D.M. also thank UPV for a postdoctoral scholarship and the Spanish Ministry of Science for a PhD Scholarship, respectively. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No GA694159 MONACAT).Mateo-Mateo, D.; De Masi, D.; Albero-Sancho, J.; Lacroix, L.; Fazzini, P.; Chaudret, B.; García Gómez, H. (2018). Synergism of Au and Ru Nanoparticles in Low-Temperature Photoassisted CO2 Methanation. Chemistry - A European Journal. 24(69):18436-18443. https://doi.org/10.1002/chem.201803022S1843618443246

AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether chest X-ray (CXR) can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. CXR is a radiological technique that compared to computed tomography (CT) it is simpler, faster, more widespread and it induces lower radiation dose. We present a dataset including data collected from 820 patients by six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. We investigate the potential of artificial intelligence to predict the prognosis of such patients, distinguishing between severe and mild cases, thus offering a baseline reference for other researchers and practitioners. To this goal, we present three approaches that use features extracted from CXR images, either handcrafted or automatically by convolutional neuronal networks, which are then integrated with the clinical data. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, implying that clinical data and images have the potential to provide useful information for the management of patients and hospital resources

AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study

open28Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether artificial intelligence working with chest X-ray (CXR) scans and clinical data can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. Indeed, further to induce lower radiation dose than computed tomography (CT), CXR is a simpler and faster radiological technique, being also more widespread. In this respect, we present three approaches that use features extracted from CXR images, either handcrafted or automatically learnt by convolutional neuronal networks, which are then integrated with the clinical data. As a further contribution, this work introduces a repository that collects data from 820 patients enrolled in six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, suggesting that clinical data and images have the potential to provide useful information for the management of patients and hospital resources.openPaolo Soda, Natascha Claudia D’Amico, Jacopo Tessadori, Giovanni Valbusa, Valerio Guarrasi, Chandra Bortolotto, Muhammad Usman Akbar, Rosa Sicilia, Ermanno Cordelli, Deborah Fazzini, Michaela Cellina, Giancarlo Oliva, Giovanni Callea, Silvia Panella, Maurizio Cariati, Diletta Cozzi, Vittorio Miele, Elvira Stellato, Gianpaolo Carrafiello, Giulia Castorani, Annalisa Simeone, Lorenzo Preda, Giulio Iannello, Alessio Del Bue, Fabio Tedoldi, Marco Alí, Diego Sona, Sergio PapaSoda, Paolo; Claudia D’Amico, Natascha; Tessadori, Jacopo; Valbusa, Giovanni; Guarrasi, Valerio; Bortolotto, Chandra; Usman Akbar, Muhammad; Sicilia, Rosa; Cordelli, Ermanno; Fazzini, Deborah; Cellina, Michaela; Oliva, Giancarlo; Callea, Giovanni; Panella, Silvia; Cariati, Maurizio; Cozzi, Diletta; Miele, Vittorio; Stellato, Elvira; Carrafiello, Gianpaolo; Castorani, Giulia; Simeone, Annalisa; Preda, Lorenzo; Iannello, Giulio; Del Bue, Alessio; Tedoldi, Fabio; Alí, Marco; Sona, Diego; Papa, Sergi