661 results on '"Castiglioni, I"'
Search Results
2. OC03.03: Adnexal masses and risk of malignancy by radiomics: the future is now
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Chiappa, V., primary, Interlenghi, M., additional, Bascio, L. Spanò, additional, Salvatore, C., additional, Fruscio, R., additional, Ferrero, S., additional, Rosati, F., additional, De Meis, L., additional, Rolla, M., additional, Ficarelli, S., additional, Pino, I., additional, Franchi, D., additional, Mor, E., additional, Maggiore, U. Leone Roberti, additional, Bogani, G., additional, Raspagliesi, F., additional, and Castiglioni, I., additional
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- 2023
- Full Text
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3. Consequences of exposure to pollutants on respiratory health: From genetic correlations to causal relationships
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D'Antona, S, Castiglioni, I, Porro, D, Cava, C, D'Antona S., Castiglioni I., Porro D., Cava C., D'Antona, S, Castiglioni, I, Porro, D, Cava, C, D'Antona S., Castiglioni I., Porro D., and Cava C.
- Abstract
Modern society grew rapidly over the last few decades and this led to an alarming increase in air pollutants and a worsening of the human health, especially in relation to the respiratory system. Indeed, chronic respiratory diseases were the third main cause of death in 2017, with over 3 million of deaths. Furthermore, the pollution has considerable consequences both for burden medical expenses and environmental. However, the mechanisms linking pollutants to the onset of these diseases remain unclear. Thus, in this study we addressed this problem through the United Kingdom BioBank database, analyzing 170 genome-wide association studies (103 related to respiratory diseases and 67 related to pollutants). We analyzed the genetic correlations and causal relationships of these traits, leveraging the summary statistics and bioinformatics packages such as Linkage Disequilibrium Score Regression and Latent Causal Variable. We obtained 158 significant genetic correlations and subsequently we analyzed them through the Latent Causal Variable analysis, obtaining 20 significant causal relationships. The most significant were between "Workplace full of chemicals or other fumes: Sometimes" and “Condition that has ever been diagnosed by a doctor: Asthma” and between “Workplace very dusty: Sometimes” and “Condition that has ever been diagnosed by a doctor: Emphysema or chronic bronchitis”. Finally, we identified single nucleotide polymorphisms independently associated with sveral pollutants to analyze the genes and pathways that could be involved in the onset of the aforementioned respiratory system disorders and that could be useful clinical target. This study highlighted how crucial are the air condition of the working environments and the type of transport used in the onset of respiratory-related morbidity. Based on that, we also suggested some interventions, in order to improve quality life and develop new and eco-friendly society and life style, such as improving indoor air cir
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- 2022
4. From genetic correlations of Alzheimer's disease to classification with artificial neural network models
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Cava, C, D'Antona, S, Maselli, F, Castiglioni, I, Porro, D, Cava, Claudia, D'Antona, Salvatore, Maselli, Francesca, Castiglioni, Isabella, Porro, Danilo, Cava, C, D'Antona, S, Maselli, F, Castiglioni, I, Porro, D, Cava, Claudia, D'Antona, Salvatore, Maselli, Francesca, Castiglioni, Isabella, and Porro, Danilo
- Abstract
Sporadic Alzheimer’s disease (AD) is a complex neurological disorder characterized by many risk loci with potential associations with different traits and diseases. AD, characterized by a progressive loss of neuronal functions, manifests with different symptoms such as decline in memory, movement, coordination, and speech. The mechanisms underlying the onset of AD are not always fully understood, but involve a multiplicity of factors. Early diagnosis of AD plays a central role as it can offer the possibility of early treatment, which can slow disease progression. Currently, the methods of diagnosis are cognitive testing, neuroimaging, or cerebrospinal fluid analysis that can be time-consuming, expensive, invasive, and not always accurate. In the present study, we performed a genetic correlation analysis using genome-wide association statistics from a large study of AD and UK Biobank, to examine the association of AD with other human traits and disorders. In addition, since hippocampus, a part of cerebral cortex could play a central role in several traits that are associated with AD; we analyzed the gene expression profiles of hippocampus of AD patients applying 4 different artificial neural network models. We found 65 traits correlated with AD grouped into 9 clusters: medical conditions, fluid intelligence, education, anthropometric measures, employment status, activity, diet, lifestyle, and sexuality. The comparison of different 4 neural network models along with feature selection methods on 5 Alzheimer’s gene expression datasets showed that the simple basic neural network model obtains a better performance (66% of accuracy) than other more complex methods with dropout and weight regularization of the network.
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- 2023
5. Evaluation of plan complexity and dosimetric plan quality of total marrow and lymphoid irradiation using volumetric modulated arc therapy
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Universitat Rovira i Virgili, Lambri, N; Dei, D; Hernandez, V; Castiglioni, I; Clerici, E; De Philippis, C; Loiacono, D; Navarria, P; Reggiori, G; Rusconi, R; Tomatis, S; Bramanti, S; Scorsetti, M; Mancosu, P, Universitat Rovira i Virgili, and Lambri, N; Dei, D; Hernandez, V; Castiglioni, I; Clerici, E; De Philippis, C; Loiacono, D; Navarria, P; Reggiori, G; Rusconi, R; Tomatis, S; Bramanti, S; Scorsetti, M; Mancosu, P
- Abstract
PurposeTo assess the impact of the planner's experience and optimization algorithm on the plan quality and complexity of total marrow and lymphoid irradiation (TMLI) delivered by means of volumetric modulated arc therapy (VMAT) over 2010-2022 at our institute. MethodsEighty-two consecutive TMLI plans were considered. Three complexity indices were computed to characterize the plans in terms of leaf gap size, irregularity of beam apertures, and modulation complexity. Dosimetric points of the target volume (D2%) and organs at risk (OAR) (Dmean) were automatically extracted to combine them with plan complexity and obtain a global quality score (GQS). The analysis was stratified based on the different optimization algorithms used over the years, including a knowledge-based (KB) model. Patient-specific quality assurance (QA) using Portal Dosimetry was performed retrospectively, and the gamma agreement index (GAI) was investigated in conjunction with plan complexity. ResultsPlan complexity significantly reduced over the years (r = -0.50, p < 0.01). Significant differences in plan complexity and plan dosimetric quality among the different algorithms were observed. Moreover, the KB model allowed to achieve significantly better dosimetric results to the OARs. The plan quality remained similar or even improved during the years and when moving to a newer algorithm, with GQS increasing from 0.019 +/- 0.002 to 0.025 +/- 0.003 (p < 0.01). The significant correlation between GQS and time (r = 0.33, p = 0.01) indicated that the planner's experience was relevant to improve the plan quality of TMLI plans. Significant correlations between the GAI and the complexity metrics (r = -0.71, p < 0.01) were also found. ConclusionBoth the planner's experience and algorithm version are crucial to ac
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- 2023
6. Automatic planning of the lower extremities for total marrow irradiation using volumetric modulated arc therapy
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Universitat Rovira i Virgili, Lambri, N; Dei, D; Hernandez, V; Castiglioni, I; Clerici, E; Crespi, L; De Philippis, C; Loiacono, D; Navarria, P; Reggiori, G; Rusconi, R; Tomatis, S; Bramanti, S; Scorsetti, M; Mancosu, P, Universitat Rovira i Virgili, and Lambri, N; Dei, D; Hernandez, V; Castiglioni, I; Clerici, E; Crespi, L; De Philippis, C; Loiacono, D; Navarria, P; Reggiori, G; Rusconi, R; Tomatis, S; Bramanti, S; Scorsetti, M; Mancosu, P
- Abstract
Purpose Total marrow (and lymphoid) irradiation (TMI-TMLI) is limited by the couch travel range of modern linacs, which forces the treatment delivery to be split into two plans with opposite orientations: a head-first supine upper-body plan, and a feet-first supine lower extremities plan. A specific field junction is thus needed to obtain adequate target coverage in the overlap region of the two plans. In this study, an automatic procedure was developed for field junction creation and lower extremities plan optimization. Methods Ten patients treated with TMI-TMLI at our institution were selected retrospectively. The planning of the lower extremities was performed automatically. Target volume parameters (CTV_J-V-98% > 98%) at the junction region and several dose statistics (D-98%, D-mean, and D-2%) were compared between automatic and manual plans. The modulation complexity score (MCS) was used to assess plan complexity. Results The automatic procedure required 60-90 min, depending on the case. All automatic plans achieved clinically acceptable dosimetric results (CTV_J-V-98% > 98%), with significant differences found at the junction region, where D-mean and D-2% increased on average by 2.4% (p < 0.03) and 3.0% (p < 0.02), respectively. Similar plan complexity was observed (median MCS = 0.12). Since March 2022, the automatic procedure has been introduced in our clinic, reducing the TMI-TMLI simulation-to-delivery schedule by 2 days. Conclusion The developed procedure allowed treatment planning of TMI-TMLI to be streamlined, increasing efficiency and standardization, preventing human errors, while maintaining the dosimetric plan quality and complexity of manual plans. Automated strategies can simplify the future adoption and clinical implementation of TMI-TMLI treatments i
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- 2023
7. Machine learning classification for COVID19 patients performed on small datasets of CT scans
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Marrale Maurizio, La Fiura A, Collura G, D’Oca Maria Cristina, Lizzi F, Brero F, Cabini RF, Postuma I, Rinaldi L, Scapicchio C, Castiglioni I, Cristofalo G, Grassedonio E, Galia G M, Scichilone N, Retico A, B. Alzani, M. Bellacosa e G. Bianchi Bazzi, Marrale Maurizio, La Fiura A, Collura G, D’Oca Maria Cristina, Lizzi F, Brero F, Cabini RF, Postuma I, Rinaldi L, Scapicchio C, Castiglioni I, Cristofalo G, Grassedonio E, Galia G M, Scichilone N, and Retico A
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COVID19, CT scans, medical artificial intelligence AI ,Settore FIS/07 - Fisica Applicata(Beni Culturali, Ambientali, Biol.e Medicin) - Abstract
In this work we evaluated the possibility of carrying out classifications of the outcome of patients with COVID19 disease through machine learning (ML) techniques working on small datasets of computed tomography (CT) images. In fact, one of the most common problems for medical artificial intelligence (AI) applications is the limited availability of annotated clinical data for model training. In the framework of the artificial intelligence in medicine (AIM) project funded by INFN, we analyzed datasets of CT scans of 79 subjects combined with clinical data containing information relating to positive outcome (no need for intensive care) or poor prognosis (admission into intensive care unit and/or death). After segmentation of ground glass opacities related to this pathology, the radiomic features were subsequently extracted from the CTs, selected through various algorithms of dimension reduction or fea ture selection and used for the training various classifiers. Values of the area under the ROC curve (AUC) of 0.84 were obtained with Gradient Boosting after BORUTA feature selection. Features selected are related to disease characteristics of poor prognosis patients.
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- 2022
8. Partial Volume Correction Methods Based on Measured Lesion-to-Background Ratio in PET-CT Oncological Studies
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Gallivanone, F., Stefano, A., Gilardi, M. C., Messa, C., Canevari, C., Castiglioni, I., Magjarevic, Ratko, editor, Dössel, Olaf, editor, and Schlegel, Wolfgang C., editor
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- 2009
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9. Villapizzone, un quartiere di Milano dove l’arte intreccia il territorio
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Castiglioni, I, Giasanti, A, Castiglioni, I, and Giasanti, A
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rigenerazione urbana ,arte ,multiculturalismo ,carcere - Published
- 2022
10. Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy
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Castiglioni, I, Ippolito, D, Interlenghi, M, Monti, C, Salvatore, C, Schiaffino, S, Polidori, A, Gandola, D, Messa, C, Sardanelli, F, Castiglioni I., Ippolito D., Interlenghi M., Monti C. B., Salvatore C., Schiaffino S., Polidori A., Gandola D., Messa C., Sardanelli F., Castiglioni, I, Ippolito, D, Interlenghi, M, Monti, C, Salvatore, C, Schiaffino, S, Polidori, A, Gandola, D, Messa, C, Sardanelli, F, Castiglioni I., Ippolito D., Interlenghi M., Monti C. B., Salvatore C., Schiaffino S., Polidori A., Gandola D., Messa C., and Sardanelli F.
- Abstract
Background: We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods: We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. Results: At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85), and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73–0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in Centre 2. Conclusions: This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.
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- 2021
11. Transcriptional Profiling of Hippocampus Identifies Network Alterations in Alzheimer’s Disease
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Quarato, V, D'Antona, S, Battista, P, Zupo, R, Sardone, R, Castiglioni, I, Porro, D, Frasca, M, Cava, C, Quarato, V, D'Antona, S, Battista, P, Zupo, R, Sardone, R, Castiglioni, I, Porro, D, Frasca, M, and Cava, C
- Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease characterized by rapid brain cell degeneration affecting different areas of the brain. Hippocampus is one of the earliest involved brain regions in the disease. Modern technologies based on high-throughput data have identified transcriptional profiling of several neurological diseases, including AD, for a better comprehension of genetic mechanisms of the disease. In this study, we investigated differentially expressed genes (DEGs) from six Gene Expression Omnibus (GEO) datasets of hippocampus of AD patients. The identified DEGs were submitted to Weighted correlation network analysis (WGCNA) and ClueGo to explore genes with a higher degree centrality and to comprehend their biological role. Subsequently, MCODE was used to identify subnetworks of interconnected DEGs. Our study found 40 downregulated genes and 36 up-regulated genes as consensus DEGs. Analysis of the co-expression network revealed ACOT7, ATP8A2, CDC42, GAD1, GOT1, INA, NCALD, and WWTR1 to be genes with a higher degree centrality. ClueGO revealed the pathways that were mainly enriched, such as clathrin coat assembly, synaptic vesicle endocytosis, and DNA damage response signal transduction by p53 class mediator. In addition, we found a subnetwork of 12 interconnected genes (AMPH, CA10, CALY, NEFL, SNAP25, SNAP91, SNCB, STMN2, SV2B, SYN2, SYT1, and SYT13). Only CA10 and CALY are targets of known drugs while the others could be potential novel drug targets.
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- 2022
12. Il ruolo della sostenibilità nella transizione post-industriale: il caso del Nord Milano
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Matteo Colleoni, Castiglioni, I, Colleoni, M, Spanu, S, Ida Castiglioni, Sara Spanu, Matteo Colleoni, Castiglioni, I, Colleoni, M, Spanu, S, Ida Castiglioni, and Sara Spanu
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- 2022
13. La dimensione locale e culturale dello sviluppo sostenibile
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Ida Castiglioni, Matteo Colleoni, Sara Spanu, Matteo Colleoni, Castiglioni, I, Colleoni, M, Spanu, S, Ida Castiglioni, Sara Spanu, Ida Castiglioni, Matteo Colleoni, Sara Spanu, Matteo Colleoni, Castiglioni, I, Colleoni, M, Spanu, S, Ida Castiglioni, and Sara Spanu
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- 2022
14. LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data
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Ramazzotti, D, Angaroni, F, Maspero, D, Ascolani, G, Castiglioni, I, Piazza, R, Antoniotti, M, Graudenzi, A, Ramazzotti, Daniele, Angaroni, Fabrizio, Maspero, Davide, Ascolani, Gianluca, Castiglioni, Isabella, Piazza, Rocco, Antoniotti, Marco, Graudenzi, Alex, Ramazzotti, D, Angaroni, F, Maspero, D, Ascolani, G, Castiglioni, I, Piazza, R, Antoniotti, M, Graudenzi, A, Ramazzotti, Daniele, Angaroni, Fabrizio, Maspero, Davide, Ascolani, Gianluca, Castiglioni, Isabella, Piazza, Rocco, Antoniotti, Marco, and Graudenzi, Alex
- Abstract
The rise of longitudinal single-cell sequencing experiments on patient-derived cell cultures, xenografts and organoids is opening new opportunities to track cancer evolution, assess the efficacy of therapies and identify resistant subclones. We introduce LACE, the first algorithmic framework that processes single-cell mutational profiles from samples collected at different time points to reconstruct longitudinal models of cancer evolution. The approach maximizes a weighted likelihood function computed on longitudinal data points to solve a Boolean matrix factorization problem, via Markov chain Monte Carlo sampling. On simulations, LACE outperforms state-of-the-art methods for both bulk and single-cell sequencing data with respect to the reconstruction of the ground-truth clonal phylogeny and dynamics, also in conditions of unbalanced datasets, significant rates of sequencing errors and sampling limitations. As the results are robust with respect to data-specific errors, LACE is effective with mutational profiles generated by calling variants from (full-length) scRNA-seq data, and this allows one to investigate the relation between genomic and phenotypic evolution of tumors at the single-cell level. Here, we apply LACE to a longitudinal scRNA-seq dataset of patient-derived xenografts of BRAFV600E/K mutant melanomas, dissecting the impact of BRAF/MEK-inhibition on clonal evolution, also in terms of clone-specific gene expression dynamics. Furthermore, the analysis of breast cancer PDXs from longitudinal targeted scDNA-sequencing experiments delivers a high-resolution temporal characterization of intra-tumor heterogeneity.
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- 2022
15. An innovative protocol for the study of painting materials involving the combined use of MA-XRF maps and hyperspectral images
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Galli, A, Caccia, M, Caglio, S, Bonizzoni, L, Castiglioni, I, Gironda, M, Alberti, R, Martini, M, Galli, Anna, Caccia, Michele, Caglio, Simone, Bonizzoni, Letizia, Castiglioni, Isabella, Gironda, Michele, Alberti, Roberto, Martini, Marco, Galli, A, Caccia, M, Caglio, S, Bonizzoni, L, Castiglioni, I, Gironda, M, Alberti, R, Martini, M, Galli, Anna, Caccia, Michele, Caglio, Simone, Bonizzoni, Letizia, Castiglioni, Isabella, Gironda, Michele, Alberti, Roberto, and Martini, Marco
- Abstract
X-ray fluorescence (XRF) and reflectance spectroscopy (RS) are commonly used for the characterization of painting materials. It is well known that the former provides the chemical fingerprint of the pictorial layers, while the latter returns the molecular description of the pigments constituting the uppermost layers. Even if these two techniques cannot unveil the stratigraphy, their synergetic application well describes the materials employed for realizing the panels and represents a key turn for non-invasive scientific analysis of works of art. However, the potential of the cross-comparison between XRF and RS is not fully exploited yet. The measurement points often barely match, and they are usually few isolated spots spread over the whole surface of the painting; these facts limit the mutual exchange of information between the data sets and can lead to losing details. In this scenario, XRF mapping (MA-XRF) and hyperspectral reflectance imaging (HRI) provide a connection channel that promises to be a decisive tool to strengthen the relationship between X-ray fluorescence and reflectance spectroscopy and, therefore, to deepen the knowledge about the case studies. Due to the spatial localization of the information they contain, the maps provide not only a straightforward reference for comparing the data but also a three-dimensional collection of elemental and molecular images. By applying computer vision and statistical methods such as spectral angle mapper (SAM), it is possible to implement an innovative approach that exploits the elemental features, obtained from XRF spectra, to improve the comprehension of the molecular aspects given by RS, and vice versa. Once we discussed the main issues behind our approach, we applied it to analyze the painting Chariot Race by Giorgio De Chirico (1928–1929, oil on canvas, Pinacoteca di Brera, Milan, Italy). The results reflect the complexity of the painting, and even if only some of the spectra identified by the method as pecul
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- 2022
16. Variant calling from scRNA-seq data allows the assessment of cellular identity in patient-derived cell lines
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Ramazzotti, D, Angaroni, F, Maspero, D, Ascolani, G, Castiglioni, I, Piazza, R, Antoniotti, M, Graudenzi, A, Ramazzotti, Daniele, Angaroni, Fabrizio, Maspero, Davide, Ascolani, Gianluca, Castiglioni, Isabella, Piazza, Rocco, Antoniotti, Marco, Graudenzi, Alex, Ramazzotti, D, Angaroni, F, Maspero, D, Ascolani, G, Castiglioni, I, Piazza, R, Antoniotti, M, Graudenzi, A, Ramazzotti, Daniele, Angaroni, Fabrizio, Maspero, Davide, Ascolani, Gianluca, Castiglioni, Isabella, Piazza, Rocco, Antoniotti, Marco, and Graudenzi, Alex
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- 2022
17. Documenting Cultural Heritage in very hostile fruition contexts: the synoptic visualization of Giottesque frescoes by Multispectral and 3D Close-range Imaging
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Grifoni, E, primary, Gargano, M, additional, Melada, J, additional, Interlenghi, M, additional, Castiglioni, I, additional, Romano Gosetti di Sturmeck, S, additional, and Ludwig, N, additional
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- 2022
- Full Text
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18. Clinical Guidelines on the Use of Assisted Reproductive Technology During Covid-19 Pandemic: A Minireview of the Current Literature
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Sabetian S., Jahromi B.N., Feiz F., Castiglioni I., Cava C., and Vakili S.
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Assisted ,Assisted reproductive technology ,COVID-19 ,Infertility ,Pandemic ,Reproductive Techniques - Abstract
Background: The coronavirus disease-2019 (COVID-19), caused by a the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is now spread worldwide. Therefore, informative and reliable data related to the exact effects of COVID-19 on fertility and pregnancy is still of great interest until the pandemic is declared over. General guidelines regarding the protection and management of COVID-19 have been published and new information will continue to be updated daily. Methods: In this review, we summarized clinical health guidelines for reproductive and infertility centers to improve quality management in assisted reproductive technology and minimize the potentially harmful consequences of COVID-19 on pregnancy and fertility. Results: As specified in the literature, protocols consist of five categories, including protocols for couples, protocols for women, protocols for men, labor and delivery, and postpartum and breastfeeding. Conclusion: General protocols for patients and staff may vary depending on specific conditions. However, this review provides some rules to ensure their safety against the disease during the pandemic.
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- 2022
19. Variant calling from scRNA-seq data allows the assessment of cellular identity in patient-derived cell lines
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Ramazzotti D., Angaroni F., Maspero D., Ascolani G., Castiglioni I., Piazza R., Antoniotti M., and Graudenzi A.
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MATTERS ARISING
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- 2022
20. Tissue-equivalent trimodal anthropomorphic phantom for radiomic studies
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Gallivanone, F., primary, D’Ambrosio, D., additional, Carne, I., additional, D’Arcangelo, M., additional, Montagna, P., additional, Giroletti, E., additional, Poggi, P., additional, Vellani, C., additional, Moro, L., additional, and Castiglioni, I., additional
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- 2021
- Full Text
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21. PET quantification: strategies for partial volume correction
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Bettinardi, V., Castiglioni, I., De Bernardi, E., and Gilardi, M. C.
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- 2014
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22. Predictive value of pre-therapy 18F-FDG PET/CT for the outcome of 18F-FDG PET-guided radiotherapy in patients with head and neck cancer
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Picchio, M., Kirienko, M., Mapelli, P., Dell’Oca, I., Villa, E., Gallivanone, F., Gianolli, L., Messa, C., and Castiglioni, I.
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- 2014
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23. The Giotto's workshop in the XXI century: looking inside the “God the Father with Angels” gable
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Gargano, M, Galli, A, Bonizzoni, L, Alberti, R, Aresi, N, Caccia, M, Castiglioni, I, Interlenghi, M, Salvatore, C, Ludwig, N, Martini, M, Gargano M., Galli A., Bonizzoni L., Alberti R., Aresi N., Caccia M., Castiglioni I., Interlenghi M., Salvatore C., Ludwig N., Martini M., Gargano, M, Galli, A, Bonizzoni, L, Alberti, R, Aresi, N, Caccia, M, Castiglioni, I, Interlenghi, M, Salvatore, C, Ludwig, N, Martini, M, Gargano M., Galli A., Bonizzoni L., Alberti R., Aresi N., Caccia M., Castiglioni I., Interlenghi M., Salvatore C., Ludwig N., and Martini M.
- Abstract
God the Father with Angels (about 1330, tempera on panel) by Giotto is the Gable of the altarpiece of Baroncelli Chapel in the church of Santa Croce in Florence. Very little is known about its history since the separation from the so-called Baroncelli Polyptych. Now at the San Diego Museum of Art, the Gable had never been studied by means of scientific methods before our team took the opportunity to during the exhibition “Giotto, l'Italia” held in Milan. Exploiting the integration of different knowledge, technologies and resources of our team, we were able to provide data for understanding the organizational model of Giotto's workshop performing non-invasive analyses with portable instruments during closing hours of exhibition (four diagnostic campaigns, six hours of work/campaign, no interruption of the exhibition). The achieved results confirm the painting technique based on different layers of pigments, a technique already used by Giotto. Combining the effectiveness of scanning MA-XRF with the responsive of IR reflectography and IR false colour, we moved step by step toward the discovery of Giotto's palette for the flesh tones in God the Father with Angels. FORS and XRF single point analyses were performed on some selected areas too. The IR reflectography results support the hypothesis of a detailed underdrawing with both thin and flat brushstrokes. By applying image-processing algorithms to the collected reflectograms, we obtained quantitative objective measures supporting the hypothesis that a guide could have been used in the realization of human figures; this means the use of sketches for the face of “God the Father” and for the faces of angels.
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- 2019
24. Leonardeschi oltre il visibile
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Letizia Bonizzoni, Silvia Bruni, Isabella Castiglioni, Anna Galli, Marco Gargano, Matteo Interlenghi, Margherita Longoni, Marco Martini, Arianna Passaretti, Christian Salvatore, Bonizzoni, L, Bruni, S, Castiglioni, I, Galli, A, Gargano, M, Interlenghi, M, Longoni, M, Martini, M, Passaretti, A, Salvatore, C, Bonizzoni L, Bruni S, Castiglioni I, Galli A, Gargano M, Interlenghi M, Longoni M, Martini M, Passaretti A, Salvatore C, Letizia Bonizzoni, Silvia Bruni, Isabella Castiglioni, Anna Galli, Marco Gargano, Matteo Interlenghi, Margherita Longoni, Marco Martini, Arianna Passaretti, Christian Salvatore, Bonizzoni, L, Bruni, S, Castiglioni, I, Galli, A, Gargano, M, Interlenghi, M, Longoni, M, Martini, M, Passaretti, A, Salvatore, C, Bonizzoni L, Bruni S, Castiglioni I, Galli A, Gargano M, Interlenghi M, Longoni M, Martini M, Passaretti A, and Salvatore C
- Published
- 2019
25. La dialettica della bellezza nella Social Justice
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Castiglioni, I., Giasanti, A., Natali, L., Castiglioni, I, Castiglioni, I., Giasanti, A., Natali, L., and Castiglioni, I
- Published
- 2019
26. 364 RadiOmics and molecular classification in endometrial cancer (the ROME study): a step forward to a simplified precision medicine
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Bogani, G, primary, Castiglioni, I, additional, Chiappa, V, additional, and Raspagliesi, F, additional
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- 2021
- Full Text
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27. Imaging in radiotherapy
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Calandrino, R., Del Maschio, A., Cattaneo, G.M., and Castiglioni, I.
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- 2009
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28. The SRA protein UHRF1 promotes epigenetic crosstalks and is involved in prostate cancer progression
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Babbio, F, Pistore, C, Curti, L, Castiglioni, I, Kunderfranco, P, Brino, L, Oudet, P, Seiler, R, Thalman, G N, Roggero, E, Sarti, M, Pinton, S, Mello-Grand, M, Chiorino, G, Catapano, C V, Carbone, G M, and Bonapace, I M
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- 2012
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29. Nel Quarto Stato: indagine interdisciplinare sull'opera di Giuseppe Pellizza da Volpedo
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Addari, A, Alberti, R, Bolzacchini, E, Bracco, B, Bigogno, A, Bonizzoni, L, Caccia, M, Caglio, S, Castiglioni, I, Cefalì, AM, Capurro, R, Caramenti, M, De Nicola, A, Edallo, E, Facchinetti, F, Ferrero, L, Galli, A, Gargano, M, Germagnoli, F, Giacon, D, Grifoni, E, Interlenghi, M, Lantini, R, Ludwig, N, Martini, M, Melada, J, Montaldo, AM, Nascimbene, R, Nuvolati, G, Rota, M, Pernigotti, P, Perticucci, I, Palifori, A, Reale, R, Schiavi, A, Scotti Tosini, A, Tacci, M, Taccola, G, Tariffi, F, and Zuccoli, F
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In questo libro, a cura di Rita Capurro, Anna Galli e Gregorio Taccola, è presentato un inedito studio interdisciplinare dedicato a Quarto Stato, il capolavoro di Giuseppe Pellizza da Volpedo esposto al Museo del Novecento di Milano. L'intervento su quest'Opera si inserisce come caso studio in un importante progetto multidisciplinare finanziato da Regione Lombardia nell'ambito di un ampio intervento nel quadro dei Fondi Europei di Sviluppo Regionale, FESR, previsto nel Programma Operativo Nazionale (PON). Il progetto propone lo sviluppo, la sperimentazione e l'adozione di una piattaforma tecnologica mobile- la piattaforma MOBARTECH - che integra competenze e capacità culturali, sociali e creative con tecnologie abilitanti, quali Information Technology, tecnologie fisiche diagnostiche non invasive, dispositivi e metodi di acquisizione ed elaborazione delle immagini, tecnologie e metodologie per la conservazione e il restauro, sistemi di logistica intelligente, tecnologie di public interaction e di infotainment (information + entertainment), per l'erogazione di servizi ad elevato valore aggiunto applicati ai Beni storico-artistici. Al progetto prendono parte quattro dipartimenti dell'Università di Milano-Bicocca (Dipartimento di Scienza dei Materiali, Dipartimento di Sociologia e Ricerca Sociale, Dipartimento di Scienza dell'Ambiente e della Terra e il Dipartimento di Scienze Umane per la Formazione "Riccardo Massa") in partnership con altre importanti istituzioni italiane di ricerca scientifica quali l'Università di Milano, l'Università Cattolica, l'Istituto IBFM del Consiglio Nazionale delle Ricerche, CNR, l'Istituto Eucentre, e con la società ARTERIA , che è capofila del progetto, l'azienda XGLab del gruppo Bruker e la società Space.
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- 2021
30. Variant calling from scRNA-seq data allows the assessment of cellular identity in patient-derived cell lines
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Ramazzotti, D, Ramazzotti, D, Angaroni, F, Maspero, D, Ascolani, G, Castiglioni, I, Piazza, R, Antoniotti, M, Graudenzi, A, Ramazzotti, D, Ramazzotti, D, Angaroni, F, Maspero, D, Ascolani, G, Castiglioni, I, Piazza, R, Antoniotti, M, and Graudenzi, A
- Abstract
Matters Arising from: Sharma, A., Cao, E.Y., Kumar, V. et al. Longitudinal single-cell RNA sequencing of patient-derived primary cells reveals drug-induced infidelity in stem cell hierarchy. Nat Commun 9, 4931 (2018). https://doi.org/10.1038/s41467-018-07261-3. In Sharma, A. et al. Nat Commun 9, 4931 (2018) the authors employ longitudinal single-cell transcriptomic data from patient-derived primary and metastatic oral squamous cell carcinomas cell lines, to investigate possible divergent modes of chemo-resistance in tumor cell subpopulations. We integrated the analyses presented in the manuscript, by performing variant calling from scRNA-seq data via GATK Best Practices. As a main result, we show that an extremely high number of single-nucleotide variants representative of the identity of a specific patient is unexpectedly found in the scRNA-seq data of the cell line derived from a second patient, and vice versa. This finding likely suggests the existence of a sample swap, thus jeopardizing some of the translational conclusions of the article. Our results prove the efficacy of a joint analysis of the genotypic and transcriptomic identity of single-cells.
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- 2021
31. A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules
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Rundo, L, Ledda, R, di Noia, C, Sala, E, Mauri, G, Milanese, G, Sverzellati, N, Apolone, G, Gilardi, M, Messa, M, Castiglioni, I, Pastorino, U, Rundo, Leonardo, Ledda, Roberta Eufrasia, di Noia, Christian, Sala, Evis, Mauri, Giancarlo, Milanese, Gianluca, Sverzellati, Nicola, Apolone, Giovanni, Gilardi, Maria Carla, Messa, Maria Cristina, Castiglioni, Isabella, Pastorino, Ugo, Rundo, L, Ledda, R, di Noia, C, Sala, E, Mauri, G, Milanese, G, Sverzellati, N, Apolone, G, Gilardi, M, Messa, M, Castiglioni, I, Pastorino, U, Rundo, Leonardo, Ledda, Roberta Eufrasia, di Noia, Christian, Sala, Evis, Mauri, Giancarlo, Milanese, Gianluca, Sverzellati, Nicola, Apolone, Giovanni, Gilardi, Maria Carla, Messa, Maria Cristina, Castiglioni, Isabella, and Pastorino, Ugo
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Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based r
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- 2021
32. Minor Allele Frequencies and Molecular Pathways Differences for SNPs Associated with Amyotrophic Lateral Sclerosis in Subjects Participating in the UKBB and 1000 Genomes Project
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D'Antona, S, Bertoli, G, Castiglioni, I, Cava, C, D'Antona, S, Bertoli, G, Castiglioni, I, and Cava, C
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Amyotrophic lateral sclerosis (ALS) is a complex disease with a late onset and is characterized by the progressive loss of muscular and respiratory functions. Although recent studies have partially elucidated ALS’s mechanisms, many questions remain such as what the most important molecular pathways involved in ALS are and why there is such a large difference in ALS onset among different populations. In this study, we addressed this issue with a bioinformatics approach, using the United Kingdom Biobank (UKBB) and the European 1000 Genomes Project (1KG) in order to analyze the most ALS-representative single nucleotide polymorphisms (SNPs) that differ for minor allele frequency (MAF) between the United Kingdom population and some European populations including Finnish in Finland, Iberian population in Spain, and Tuscans in Italy. We found 84 SNPs associated with 46 genes that are involved in different pathways including: “Ca2+ activated K+ channels”, “cGMP effects”, ”Nitric oxide stimulates guanylate cyclase”, “Proton/oligopeptide co-transporters”, and “Signaling by MAPK mutants”. In addition, we revealed that 83% of the 84 SNPs can alter transcription factor-motives binding sites of 224 genes implicated in “Regulation of beta-cell development”, “Transcription-al regulation by RUNX3”, “Transcriptional regulation of pluripotent stem cells”, and “FOXO-mediated transcription of cell death genes”. In conclusion, the genes and pathways analyzed could explain the cause of the difference of ALS onset.
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- 2021
33. Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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Caccia, M, Caglio, S, Galli, A, Interlenghi, M, Castiglioni, I, Martini, M, Michele Caccia, Simone Caglio, Anna Galli, Matteo Interlenghi, Isabella Castiglioni, Marco Martini, Caccia, M, Caglio, S, Galli, A, Interlenghi, M, Castiglioni, I, Martini, M, Michele Caccia, Simone Caglio, Anna Galli, Matteo Interlenghi, Isabella Castiglioni, and Marco Martini
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Reflectance Spectroscopy (RS) and Fiber Optics Reflectance Spectroscopy (FORS) are well-established techniques for the investigation of works of art with particular attention to paintings. Most modern museums put at the disposal of their research groups portable equipment that, together with the intrinsic non-invasiveness of RS and FORS, makes possible the in situ collection of reflectance spectra from the surface of artefacts. The comparison, performed by experts in pigments and painting materials, of the experimental data with databases of reference spectra drives the characterization of the palettes and of the techniques used by the artists. However, this approach requires specific skills and it is time consuming especially if the number of the spectra to be investigated becomes large as is the case of Hyperspectral Reflectance Imaging (HRI) datasets. The HRI experimental setups are multi-dimensional cameras that associate the spectral information, given by the reflectance spectra, with the spatial localization of the spectra over the painted surface. The resulting datasets are 3D-cubes (called hypercubes or data-cubes) where the first two dimensions locate the spectrum over the painting and the third is the spectrum itself (i.e., the reflectance of that point of the painted surface versus the wavelength in the operative range of the detector). The capability of the detector to simultaneously collect a great number of spectra (typically much more than 10,000 for each hypercube) makes the HRI datasets large reservoirs of information and justifies the need for the development of robust and, possibly, automated protocols to analyze the data. After the description of the procedure designed for the data acquisition, we present an analysis method that systematically exploits the potential of the hypercubes. Based on Spectral Angle Mapper (SAM) and on the manipulation of the collected spectra, the algorithm handles and analyzes thousands of spectra while at the same tim
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- 2021
34. Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia
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Salvatore, C, Interlenghi, M, Monti, C, Ippolito, D, Capra, D, Cozzi, A, Schiaffino, S, Polidori, A, Gandola, D, Alì, M, Castiglioni, I, Messa, C, Sardanelli, F, Salvatore, Christian, Interlenghi, Matteo, Monti, Caterina B, Ippolito, Davide, Capra, Davide, Cozzi, Andrea, Schiaffino, Simone, Polidori, Annalisa, Gandola, Davide, Alì, Marco, Castiglioni, Isabella, Messa, Cristina, Sardanelli, Francesco, Salvatore, C, Interlenghi, M, Monti, C, Ippolito, D, Capra, D, Cozzi, A, Schiaffino, S, Polidori, A, Gandola, D, Alì, M, Castiglioni, I, Messa, C, Sardanelli, F, Salvatore, Christian, Interlenghi, Matteo, Monti, Caterina B, Ippolito, Davide, Capra, Davide, Cozzi, Andrea, Schiaffino, Simone, Polidori, Annalisa, Gandola, Davide, Alì, Marco, Castiglioni, Isabella, Messa, Cristina, and Sardanelli, Francesco
- Abstract
We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88 community-acquired pneumonia (CAP) patients, and 98 subjects without either COVID-19 or CAP, collected in two Italian hospitals. The system was tested on two independent cohorts, namely, 148 patients (COVID-19, CAP, or negative) collected by one of the two hospitals (independent testing I) and 820 COVID-19 patients collected by a multicenter study (independent testing II). On the training and cross-validation dataset, sensitivity, specificity, and area under the curve (AUC) were 0.91, 0.87, and 0.93 for COVID-19 versus negative subjects, 0.85, 0.82, and 0.94 for COVID-19 versus CAP. On the independent testing I, sensitivity, specificity, and AUC were 0.98, 0.88, and 0.98 for COVID-19 versus negative subjects, 0.97, 0.96, and 0.98 for COVID-19 versus CAP. On the independent testing II, the system correctly diagnosed 652 COVID-19 patients versus negative subjects (0.80 sensitivity) and correctly differentiated 674 COVID-19 versus CAP patients (0.82 sensitivity). This system appears promising for the diagnosis and differential diagnosis of COVID-19, showing its potential as a second opinion tool in conditions of the variable prevalence of different types of infectious pneumonia.
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- 2021
35. Imaging and spectroscopic data combined to disclose the painting techniques and materials in the fifteenth century Leonardo atelier in Milan
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Galli, A, Gargano, M, Bonizzoni, L, Bruni, S, Interlenghi, M, Longoni, M, Passaretti, A, Caccia, M, Salvatore, C, Castiglioni, I, Martini, M, Galli, Anna, Gargano, Marco, Bonizzoni, Letizia, Bruni, Silvia, Interlenghi, Matteo, Longoni, Margherita, Passaretti, Arianna, Caccia, Michele, Salvatore, Christian, Castiglioni, Isabella, Martini, Marco, Galli, A, Gargano, M, Bonizzoni, L, Bruni, S, Interlenghi, M, Longoni, M, Passaretti, A, Caccia, M, Salvatore, C, Castiglioni, I, Martini, M, Galli, Anna, Gargano, Marco, Bonizzoni, Letizia, Bruni, Silvia, Interlenghi, Matteo, Longoni, Margherita, Passaretti, Arianna, Caccia, Michele, Salvatore, Christian, Castiglioni, Isabella, and Martini, Marco
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The project “Leonardesque Artists beyond the Visible” was planned on the 500th anniversary of the death of Leonardo da Vinci, with the occasion of an exhibition held in Milan in 2019, where more than twenty works made by Leonardesque masters have been put on display. Among them, five representative paintings made by his closest pupils were selected for a comprehensive and multidisciplinary project. Portable non-invasive imaging and spectroscopy techniques were applied to supply useful information to scholars but also to the wider public: description of the material composition of the pigments, of the preparation and of the binders, existence or absence of underdrawing, and identification of the painters’ technique and style. Particular attention was paid also to the image processing techniques, mostly for hyperspectral and radiographic data, to get the most from both innovative and traditional techniques. Results highlighted for each author a peculiar painting technique showing hidden features such as pentimenti and the panel preparation methods, pigments, binders and varnishes.
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- 2021
36. Radiation-induced gene expression changes in high and low grade breast cancer cell types
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Bravata, V, Cava, C, Minafra, L, Cammarata, F, Russo, G, Gilardi, M, Castiglioni, I, Forte, G, Bravata V., Cava C., Minafra L., Cammarata F. P., Russo G., Gilardi M. C., Castiglioni I., Forte G. I., Bravata, V, Cava, C, Minafra, L, Cammarata, F, Russo, G, Gilardi, M, Castiglioni, I, Forte, G, Bravata V., Cava C., Minafra L., Cammarata F. P., Russo G., Gilardi M. C., Castiglioni I., and Forte G. I.
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Background: There is extensive scientific evidence that radiation therapy (RT) is a crucial treatment, either alone or in combination with other treatment modalities, for many types of cancer, including breast cancer (BC). BC is a heterogeneous disease at both clinical and molecular levels, presenting distinct subtypes linked to the hormone receptor (HR) status and associated with different clinical outcomes. The aim of this study was to assess the molecular changes induced by high doses of ionizing radiation (IR) on immortalized and primary BC cell lines grouped according to Human epidermal growth factor receptor (HER2), estrogen, and progesterone receptors, to study how HR status influences the radiation response. Our genomic approach using in vitro and ex-vivo models (e.g., primary cells) is a necessary first step for a translational study to describe the common driven radio-resistance features associated with HR status. This information will eventually allow clinicians to prescribe more personalized total doses or associated targeted therapies for specific tumor subtypes, thus enhancing cancer radio-sensitivity. Methods: Nontumorigenic (MCF10A) and BC (MCF7 and MDA-MB-231) immortalized cell lines, as well as healthy (HMEC) and BC (BCpc7 and BCpcEMT) primary cultures, were divided into low grade, high grade, and healthy groups according to their HR status. At 24 h post-treatment, the gene expression profiles induced by two doses of IR treatment with 9 and 23 Gy were analyzed by cDNA microarray technology to select and compare the differential gene and pathway expressions among the experimental groups. Results: We present a descriptive report of the substantial alterations in gene expression levels and pathways after IR treatment in both immortalized and primary cell cultures. Overall, the IR-induced gene expression profiles and pathways appear to be cell-line dependent. The data suggest that some specific gene and pathway signatures seem to be linked to HR status
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- 2018
37. Radiomics: is it time to compose the puzzle?
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Castiglioni, I, Gilardi, M, Castiglioni I., Gilardi M. C., Castiglioni, I, Gilardi, M, Castiglioni I., and Gilardi M. C.
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- 2018
38. MRI Characterizes the Progressive Course of AD and Predicts Conversion to Alzheimer's Dementia 24 Months Before Probable Diagnosis
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Salvatore, C, Cerasa, A, Castiglioni, I, Salvatore C, Cerasa A, CASTIGLIONI I, Salvatore, C, Cerasa, A, Castiglioni, I, Salvatore C, Cerasa A, and CASTIGLIONI I
- Abstract
There is no disease-modifying treatment currently available for AD, one of the more impacting neurodegenerative diseases affecting more than 47.5 million people worldwide. The definition of new approaches for the design of proper clinical trials is highly demanded in order to achieve non-confounding results and assess more effective treatment. In this study, a cohort of 200 subjects was obtained from the Alzheimer's Disease Neuroimaging Initiative. Subjects were followed-up for 24 months, and classified as AD (50), progressive-MCI to AD (50), stable-MCI (50), and cognitively normal (50). Structural T1-weighted MRI brain studies and neuropsychological measures of these subjects were used to train and optimize an artificial-intelligence classifier to distinguish mild-AD patients who need treatment (AD + pMCI) from subjects who do not need treatment (sMCI + CN). The classifier was able to distinguish between the two groups 24 months before AD definite diagnosis using a combination of MRI brain studies and specific neuropsychological measures, with 85% accuracy, 83% sensitivity, and 87% specificity. The combined-approach model outperformed the classification using MRI data alone (72% classification accuracy, 69% sensitivity, and 75% specificity). The patterns of morphological abnormalities localized in the temporal pole and medial-temporal cortex might be considered as biomarkers of clinical progression and evolution. These regions can be already observed 24 months before AD definite diagnosis. The best neuropsychological predictors mainly included measures of functional abilities, memory and learning, working memory, language, visuoconstructional reasoning, and complex attention, with a particular focus on some of the sub-scores of the FAQ and AVLT tests
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- 2018
39. Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis
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Cava, C, Bertoli, G, Colaprico, A, Olsen, C, Bontempi, G, Castiglioni, I, Cava C, Bertoli G, Colaprico A, Olsen C, Bontempi G, CASTIGLIONI I, Cava, C, Bertoli, G, Colaprico, A, Olsen, C, Bontempi, G, Castiglioni, I, Cava C, Bertoli G, Colaprico A, Olsen C, Bontempi G, and CASTIGLIONI I
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Background: Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network. Results: We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study. Conclusions: Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways
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- 2018
40. Parameters Influencing PET Imaging Features: A Phantom Study with Irregular and Heterogeneous Synthetic Lesions
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Gallivanone, F, Interlenghi, M, D'Ambrosio, D, Trifirò, G, Castiglioni, I, Gallivanone F, Interlenghi M, D'Ambrosio D, Trifirò G, CASTIGLIONI I, Gallivanone, F, Interlenghi, M, D'Ambrosio, D, Trifirò, G, Castiglioni, I, Gallivanone F, Interlenghi M, D'Ambrosio D, Trifirò G, and CASTIGLIONI I
- Abstract
Aim. To evaluate reproducibility and stability of radiomic features as effects of the use of different volume segmentation methods and reconstruction settings. The potential of radiomics in really capturing the presence of heterogeneous tumor uptake and irregular shape was also investigated. Materials and Methods. An anthropomorphic phantom miming real clinical situations including synthetic lesions with irregular shape and nonuniform radiotracer uptake was used. 18F-FDG PET/CT measurements of the phantom were performed including 38 lesions of different shape, size, lesion-to-background ratio, and radiotracer uptake distribution. Different reconstruction parameters and segmentation methods were considered. COVs were calculated to quantify feature variations over the different reconstruction settings. Friedman test was applied to the values of the radiomic features obtained for the considered segmentation approaches. Two sets of test-retest measurement were acquired and the pairwise intraclass correlation coefficient was calculated. Fifty-eight morphological and statistical features were extracted from the segmented lesion volumes. A Mann-Whitney test was used to evaluate significant differences among each feature when calculated from heterogeneous versus homogeneous uptake. The significance of each radiomic feature in terms of capturing heterogeneity was evaluated also by testing correlation with gold standard indexes of heterogeneity and sphericity. Results. The choice of the segmentation method has a strong impact on the stability of radiomic features (less than 20% can be considered stable features). Reconstruction affects the estimate of radiomic features (only 26% are stable). Thirty-one radiomic features (53%) resulted to be reproducible, 11 of them are able to discriminate heterogeneity. Among these, we found a subset of 3 radiomic features strongly correlated with GS heterogeneity index that can be suggested as good features for retrospective evaluations
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- 2018
41. Machine-learning neuroimaging challenge for automated diagnosis of mild cognitive impairment: Lessons learnt
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Castiglioni, I, Salvatore, C, Ramírez, J, Górriz, J, CASTIGLIONI I, Salvatore C, Ramírez J, Górriz JM., Castiglioni, I, Salvatore, C, Ramírez, J, Górriz, J, CASTIGLIONI I, Salvatore C, Ramírez J, and Górriz JM.
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- 2018
42. A wrapped multi-label classifier for the automatic diagnosis and prognosis of Alzheimer's disease
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Salvatore, C, Castiglioni, I, Salvatore C, CASTIGLIONI I, Salvatore, C, Castiglioni, I, Salvatore C, and CASTIGLIONI I
- Abstract
Background: AD is the most frequent neurodegenerative disease, severely impacting our society. Early diagnosis and prognosis are challenging tasks in the management of AD patients. New method: We implemented a machine-learning classifier for the automatic early diagnosis and prognosis of AD by means of features extracted, selected and optimized from structural MRI brain images. The classifier was designed to perform multi-label automatic classification into the following four classes: HC, ncMCI, cMCI, and AD. Results: From our analyses, it emerged that MMSE and hippocampus-related measures must be included as primary measures in automatic-classification systems for both the early diagnosis and the prognosis of AD. The voting scheme mainly based on the binary-classification performances on the different four groups is the best choice to model the multi-label decision function for AD, when compared with a simple majority-vote scheme or with a scheme aimed at discriminating patients with high vs low risk of conversion to AD and therapy addressing. Comparison with existing method(s): The accuracies of our binary classifications were higher than or comparable to previously published methods. An improvement is needed on the approach we used to combine binary-classification outputs to obtain the final multi-label classification. Conclusions: The performance of multi-label automatic-classification systems strongly depends on the choice of the voting scheme used for combining binary-classification labels.
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- 2018
43. Le case e i traslochi di Quarto Stato. Appunti per una storia espositiva e museale
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Addari, A, Alberti, R, Bolzacchini, E, Bracco, B, Bigogno, A, Bonizzoni, L, Caccia, M, Caglio, S, Castiglioni, I, Cefalì, AM, Capurro, R, Caramenti, M, De Nicola, A, Edallo, E, Facchinetti, F, Ferrero, L, Galli, A, Gargano, M, Germagnoli, F, Giacon, D, Grifoni, E, Interlenghi, M, Lantini, R, Ludwig, N, Martini, M, Melada, J, Montaldo, AM, Nascimbene, R, Nuvolati, G, Rota, M, Pernigotti, P, Perticucci, I, Palifori, A, Reale, R, Schiavi, A, Scotti Tosini, A, Tacci, M, Taccola, G, Tariffi, F, Zuccoli, F, Taccola G, Addari, A, Alberti, R, Bolzacchini, E, Bracco, B, Bigogno, A, Bonizzoni, L, Caccia, M, Caglio, S, Castiglioni, I, Cefalì, AM, Capurro, R, Caramenti, M, De Nicola, A, Edallo, E, Facchinetti, F, Ferrero, L, Galli, A, Gargano, M, Germagnoli, F, Giacon, D, Grifoni, E, Interlenghi, M, Lantini, R, Ludwig, N, Martini, M, Melada, J, Montaldo, AM, Nascimbene, R, Nuvolati, G, Rota, M, Pernigotti, P, Perticucci, I, Palifori, A, Reale, R, Schiavi, A, Scotti Tosini, A, Tacci, M, Taccola, G, Tariffi, F, Zuccoli, F, and Taccola G
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- 2020
44. Longitudinal cancer evolution from single cells
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Ramazzotti, Daniele, Ramazzotti, D, Angaroni, F, Maspero, D, Ascolani, G, Castiglioni, I, Piazza, R, Antoniotti, M, Graudenzi, A, Ramazzotti, Daniele, Angaroni, Fabrizio, Maspero, Davide, Ascolani, Gianluca, Castiglioni, Isabella, Piazza, Rocco, Antoniotti, Marco, Graudenzi, Alex, Ramazzotti, Daniele, Ramazzotti, D, Angaroni, F, Maspero, D, Ascolani, G, Castiglioni, I, Piazza, R, Antoniotti, M, Graudenzi, A, Ramazzotti, Daniele, Angaroni, Fabrizio, Maspero, Davide, Ascolani, Gianluca, Castiglioni, Isabella, Piazza, Rocco, Antoniotti, Marco, and Graudenzi, Alex
- Abstract
The rise of longitudinal single-cell sequencing experiments on patient-derived cell cultures, xenografts and organoids is opening new opportunities to track cancer evolution in single tumors and to investigate intra-tumor heterogeneity. This is particularly relevant when assessing the efficacy of therapies over time on the clonal composition of a tumor and in the identification of resistant subclones. We here introduce LACE (Longitudinal Analysis of Cancer Evolution), the first algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. Our approach solves a Boolean matrix factorization problem with phylogenetic constraints, by maximizing a weighted likelihood function computed on multiple time points, and we show with simulations that it outperforms state-of-the-art methods for both bulk and single-cell sequencing data. Remarkably, as the results are robust with respect to high levels of data-specific errors, LACE can be employed to process single-cell mutational profiles as generated by calling variants from the increasingly available scRNA-seq data, thus obviating the need of relying on rarer and more expensive genome sequencing experiments. This also allows to investigate the relation between genomic clonal evolution and phenotype at the single-cell level. To illustrate the capabilities of LACE, we show its application to a longitudinal scRNA-seq dataset of patient-derived xenografts of BRAFV600E/K mutant melanomas, in which we characterize the impact of concurrent BRAF/MEK-inhibition on clonal evolution, also by showing that distinct genetic clones reveal different sensitivity to the therapy.
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- 2020
45. Short-Term Classification Learning Promotes Rapid Global Improvements of Information Processing in Human Brain Functional Connectome
- Author
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Zippo, A. G., Castiglioni, I., Lin, Jianyi, Borsa, V. M., Valente, M., Biella, G. E. M., Lin J. (ORCID:0000-0002-3299-448X), Zippo, A. G., Castiglioni, I., Lin, Jianyi, Borsa, V. M., Valente, M., Biella, G. E. M., and Lin J. (ORCID:0000-0002-3299-448X)
- Abstract
Classification learning is a preeminent human ability within the animal kingdom but the key mechanisms of brain networks regulating learning remain mostly elusive. Recent neuroimaging advancements have depicted human brain as a complex graph machinery where brain regions are nodes and coherent activities among them represent the functional connections. While long-term motor memories have been found to alter functional connectivity in the resting human brain, a graph topological investigation of the short-time effects of learning are still not widely investigated. For instance, classification learning is known to orchestrate rapid modulation of diverse memory systems like short-term and visual working memories but how the brain functional connectome accommodates such modulations is unclear. We used publicly available repositories (openfmri.org) selecting three experiments, two focused on short-term classification learning along two consecutive runs where learning was promoted by trial-by-trial feedback errors, while a further experiment was used as supplementary control. We analyzed the functional connectivity extracted from BOLD fMRI signals, and estimated the graph information processing in the cerebral networks. The information processing capability, characterized by complex network statistics, significantly improved over runs, together with the subject classification accuracy. Instead, null-learning experiments, where feedbacks came with poor consistency, did not provoke any significant change in the functional connectivity over runs. We propose that learning induces fast modifications in the overall brain network dynamics, definitely ameliorating the short-term potential of the brain to process and integrate information, a dynamic consistently orchestrated by modulations of the functional connections among specific brain regions.
- Published
- 2020
46. Artificial intelligence and neuropsychological measures: The case of Alzheimer's disease
- Author
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Petronilla Battista a, 1, Christian Salvatore b, c, Manuela Berlingeri d, e, f, Antonio Cerasa g, h, Isabella Castiglioni i, and j
- Subjects
neuropsychological measures ,dementia [Neurodegenerative diseases] ,Cognitive Neuroscience ,Bivariate analysis ,Neuropsychological Tests ,Machine Learning ,03 medical and health sciences ,Behavioral Neuroscience ,0302 clinical medicine ,Neuroimaging ,Alzheimer Disease ,Artificial Intelligence ,Machine learning ,medicine ,Humans ,0501 psychology and cognitive sciences ,Cognitive Dysfunction ,050102 behavioral science & comparative psychology ,Neuropsychological assessment ,medicine.diagnostic_test ,Artificial neural network ,business.industry ,05 social sciences ,Neuropsychology ,Mild cognitive impairment ,AD ,Neuropsychological test ,medicine.disease ,MCI ,Support vector machine ,Cognitive measures ,dementia ,Neuropsychological tests [AD ,Automatic classification ,Biomarkers ,Neurodegenerative diseases] ,Neuropsychology and Physiological Psychology ,Mild Cognitive impairment ,automatic classification ,biomarkers ,cognitive measures ,machine learning ,neurodegenerative diseases: dementia ,neuropsychological tests ,Neuropsychological tests ,Alzheimer ,Disease Progression ,Artificial intelligence ,Alzheimer's disease ,business ,030217 neurology & neurosurgery - Abstract
One of the current challenges in the field of Alzheimer's disease (AD) is to identify patients with mild cognitive impairment (MCI) that will convert to AD. Artificial intelligence, in particular machine learning (ML), has established as one of more powerful approach to extract reliable predictors and to automatically classify different AD phenotypes. It is time to accelerate the translation of this knowledge in clinical practice, mainly by using low-cost features originating from the neuropsychological assessment. We performed a meta-analysis to assess the contribution of ML and neuropsychological measures for the automated classification of MCI patients and the prediction of their conversion to AD. The pooled sensitivity and specificity of patients' classifications was obtained by means of a quantitative bivariate random-effect meta-analytic approach. Although a high heterogeneity was observed, the results of meta-analysis show that ML applied to neuropsychological measures can lead to a successful automatic classification, being more specific as screening rather than prognosis tool. Relevant categories of neuropsychological tests can be extracted by ML that maximize the classification accuracy.
- Published
- 2019
47. A publicly accessible Monte Carlo database for validation purposes in emission tomography
- Author
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Castiglioni, I., Buvat, I., Rizzo, G., Gilardi, M. C., Feuardent, J., and Fazio, F.
- Published
- 2005
- Full Text
- View/download PDF
48. Multi-Modal Medical Image Integration to Optimize Radiotherapy Planning in Lung Cancer Treatment
- Author
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Rizzo, G., Cattaneo, G. M., Castellone, P., Castiglioni, I., Ceresoli, G. L., Messa, C., Landoni, C., Gilardi, M. C., Arienti, R., Cerutti, S., and Fazio, F.
- Published
- 2004
- Full Text
- View/download PDF
49. Shared cortical anatomy for motor awareness and motor control
- Author
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Berti, A., Bottini, G., Gandola, M., Pia, L., Smania, N., Stracciari, A., Castiglioni, I., Vallar, G., and Paulesu, E.
- Subjects
Brain damage -- Physiological aspects -- Psychological aspects -- Research ,Motor ability -- Research -- Physiological aspects -- Psychological aspects -- Analysis ,Movement disorders -- Analysis -- Physiological aspects -- Research ,Human mechanics -- Research -- Psychological aspects -- Analysis -- Physiological aspects ,Hemiplegia -- Analysis -- Research ,Science and technology - Abstract
In everyday life, the successful monitoring of behavior requires continuous updating of the effectiveness of motor acts; one crucial step is becoming aware of the movements one is performing. We studied the anatomical distribution of Lesions in right-brain--damaged hemiplegic patients, who obstinately denied their motor impairment, claiming that they could move their paralyzed limbs. Denial was associated with lesions in areas related to the programming of motor acts, particularly Brodmann's premotor areas 6 and 44, motor area 4, and the somatosensory cortex. This association suggests that monitoring systems may be implemented within the same cortical network that is responsible for the primary function that has to be monitored., Although much of the functioning of the body's motor systems occurs without awareness, humans are aware that they are moving (or not moving) different parts of their body, even when [...]
- Published
- 2005
50. Lesion detectability and quantification in PET/CT oncological studies by Monte Carlo simulations
- Author
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Castiglioni, I., Rizzo, G., Gilardi, M.C., Bettinardi, V., Savi, A., and Fazio, F.
- Subjects
Algorithms -- Usage ,Cancer -- Research ,Oncology, Experimental ,Monte Carlo method ,Algorithm ,Business ,Electronics ,Electronics and electrical industries - Abstract
The aim of this work was to assess lesion detectability and quantification in whole body oncological [sup.18]F-FDG studies performed by a state-of-the-art integrated Positron Emission Tomograph/computed tomography (PET/CT) system. Lesion detectability and quantification were assessed by a Monte Carlo (MC) simulation approach as a function of different physical factors (e.g., attenuation and scatter), image counting statistics, lesion size and position, lesion-to-background radioactivity concentration ratio (L/B), and reconstruction algorithms. The results of this work brought to a number of conclusions. * The MC code PET-electron gamma shower (EGS) was accurate in simulating the physical response of the considered PET/CT scanner (> 90%). * PET-EGS and patient-derived phantoms can be used in [simulating.sup.18] F-FDG PET oncological studies. * Counting statistics is a dominant factor in lesion detectability. * Correction for scatter (from both inside and outside the field of view) is needed to improve lesion detectability. * Iterative reconstruction and attenuation correction must be used to interpret clinical images. * Re-binning algorithms are appropriate for whole-body oncological data. * A MC-based method for correction of partial volume effect is feasible. For the considered PET/CT system, limits in lesion detectability were determined in situations comparable to those of real oncological studies: at a L/B = 3 for lesions of 12 mm diameter and at a L/B = 4 for lesions of 8 mm diameter. Index Terms--Lesion detectability, Monte Carlo, PET/CT.
- Published
- 2005
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