Imperial College London

DrPaoloInglese

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Data Science Research Associate
 
 
 
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p.inglese14 Website

 
 
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Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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27 results found

Kazanc E, Karali E, Wu V, Inglese P, McKenzie J, Tripp A, Koundouros N, Tsalikis T, Kudo H, Poulogiannis G, Takats Zet al., 2020, A multimodal analysis in breast cancer: Revealing metabolic heterogeneity using DESI-MS imaging with Laser-microdissection coupled transcriptome approach., AACR Virtual Special Conference on Tumor Heterogeneity - From Single Cells to Clinical Impact, Publisher: AMER ASSOC CANCER RESEARCH, ISSN: 0008-5472

Conference paper

Dannhorn A, Kazanc E, Ling S, Nikula C, Karali E, Serra MP, Vorng J-L, Inglese P, Maglennon G, Hamm G, Swales J, Strittmatter N, Barry ST, Sansom OJ, Poulogiannis G, Bunch J, Goodwin RJA, Takats Zet al., 2020, Universal Sample Preparation Unlocking Multimodal Molecular Tissue Imaging, ANALYTICAL CHEMISTRY, Vol: 92, Pages: 11080-11088, ISSN: 0003-2700

Journal article

Koundouros N, Karali E, Tripp A, Valle A, Inglese P, Perry NJS, Magee DJ, Virmouni SA, Elder GA, Tyson AL, Doria ML, van Weverwijk A, Soares RF, Isacke CM, Nicholson JK, Glen RC, Takats Z, Poulogiannis Get al., 2020, Metabolic fingerprinting links oncogenic PIK3CA with enhanced arachidonic acid-derived eicosanoids, Cell, Vol: 181, Pages: 1596-1611.e27, ISSN: 0092-8674

Oncogenic transformation is associated with profound changes in cellular metabolism, but whether tracking these can improve disease stratification or influence therapy decision-making is largely unknown. Using the iKnife to sample the aerosol of cauterized specimens, we demonstrate a new mode of real-time diagnosis, coupling metabolic phenotype to mutant PIK3CA genotype. Oncogenic PIK3CA results in an increase in arachidonic acid and a concomitant overproduction of eicosanoids, acting to promote cell proliferation beyond a cell-autonomous manner. Mechanistically, mutant PIK3CA drives a multimodal signaling network involving mTORC2-PKCζ-mediated activation of the calcium-dependent phospholipase A2 (cPLA2). Notably, inhibiting cPLA2 synergizes with fatty acid-free diet to restore immunogenicity and selectively reduce mutant PIK3CA-induced tumorigenicity. Besides highlighting the potential for metabolic phenotyping in stratified medicine, this study reveals an important role for activated PI3K signaling in regulating arachidonic acid metabolism, uncovering a targetable metabolic vulnerability that largely depends on dietary fat restriction.

Journal article

Inglese P, Correia G, Pruski P, Glen RC, Takats Zet al., 2019, Colocalization features for classification of tumors using desorption electrospray ionization mass spectrometry imaging, Analytical Chemistry, Vol: 91, Pages: 6530-6540, ISSN: 0003-2700

Supervised modeling of mass spectrometry imaging (MSI) data is a crucial component for the detection of the distinct molecular characteristics of cancerous tissues. Currently, two types of supervised analyses are mainly used on MSI data: pixel-wise segmentation of sample images and whole-sample-based classification. A large number of mass spectra associated with each MSI sample can represent a challenge for designing models that simultaneously preserve the overall molecular content while capturing valuable information contained in the MSI data. Furthermore, intensity-related batch effects can introduce biases in the statistical models. Here we introduce a method based on ion colocalization features that allows the classification of whole tissue specimens using MSI data, which naturally preserves the spatial information associated the with the mass spectra and is less sensitive to possible batch effects. Finally, we propose data visualization strategies for the inspection of the derived networks, which can be used to assess whether the correlation differences are related to coexpression/suppression or disjoint spatial localization patterns and can suggest hypotheses based on the underlying mechanisms associated with the different classes of analyzed samples.

Journal article

Koundouros N, Tripp A, Karali E, Valle A, Inglese P, Anjomani-Virmouni S, Elder G, van Weverwijk A, Filipe-Soares R, Isacke C, Nicholson J, Glen RC, Takats Z, Poulogiannis Get al., 2019, Near real-time stratification of PIK3CA mutant breast cancers using the iKnife, 211th Meeting of the Pathological-Society-of-Great-Britain-and-Ireland, Publisher: Wiley, Pages: S8-S8, ISSN: 0022-3417

Conference paper

Inglese P, Correia G, Takats Z, Nicholson JK, Glen RCet al., 2019, SPUTNIK: an R package for filtering of spatially related peaks in mass spectrometry imaging data, Bioinformatics, Vol: 35, Pages: 178-180, ISSN: 1367-4803

Summary: SPUTNIK is an R package consisting of a series of tools to filter mass spectrometry imaging peaks characterized by a noisy or unlikely spatial distribution. SPUTNIK can produce mass spectrometry imaging datasets characterized by a smaller but more informative set of peaks, reduce the complexity of subsequent multi-variate analysis and increase the interpretability of the statistical results. Availability: SPUTNIK is freely available online from CRAN repository and at https://github.com/paoloinglese/SPUTNIK. The package is distributed under the GNU General Public License version 3 and is accompanied by example files and data. Supplementary information: Supplementary data are available at Bioinformatics online.

Journal article

Inglese P, Dos Santos Correia G, Pruski P, Glen R, Takats Zet al., 2018, Co-localization features for classification of tumors using mass spectrometry imaging

Statistical modeling of mass spectrometry imaging (MSI) data is a crucial component for the understanding of the molecular characteristics of cancerous tissues. Quantification of the abundances of metabolites or batch effect between multiple spectral acquisitions represents only a few of the challenges associated with this type of data analysis. Here we introduce a method based on ion co-localization features that allows the classification of whole tissue specimens using MSI data, which overcomes the possible batch effect issues and generates data-driven hypotheses on the underlying mechanisms associated with the different classes of analyzed samples.

Working paper

Inglese P, Strittmatter N, Doria L, Mroz A, Speller A, Poynter L, Dannhorn A, Kudo H, Mirnezami R, Goldin R, Nicholson J, Takats Z, Glen Ret al., 2018, Mass spectrometry: from imaging to metabolic networks

A deeper understanding of inter-tumorand intra-tumorheterogeneity is a critical factor for the advancement of next generation strategies against cancer. Under the hypothesis that heterogeneous progression of tumorsis mirrored by their metabolic heterogeneity, detection of biochemical mechanisms responsible of the local metabolism becomes crucial.We show that network analysis of co-localized ions from mass spectrometry imaging data provides a detailed chemo-spatial insightinto the metabolic heterogeneity of tumor. Furthermore, module preservation analysis between colorectal cancer patients with and without metastatic recurrence suggests hypotheses on the nature of the different local metabolic pathways.

Poster

Alexander JL, Scott A, Poynter LR, McDonald JA, Cameron S, Inglese P, Doria L, Kral J, Hughes DJ, Susova S, Liska V, Soucek P, Hoyles L, Gomez-Romero M, Nicholson JK, Takats Z, Marchesi J, Kinross JM, Teare JPet al., 2018, THE COLORECTAL CANCER MUCOSAL MICROBIOME IS DEFINED BY DISEASE STAGE AND THE TUMOUR METABONOME, Annual Meeting of the American-Society-for-Gastrointestinal-Endoscopy / Digestive Disease Week, Publisher: W B SAUNDERS CO-ELSEVIER INC, Pages: S415-S415, ISSN: 0016-5085

Conference paper

Alexander JL, Scott A, Poynter LR, McDonald JA, Cameron S, Inglese P, Doria L, Kral J, Hughes DJ, Susova S, Liska V, Soucek P, Hoyles L, Gomez-Romero M, Nicholson JK, Takats Z, Marchesi J, Kinross JM, Teare JPet al., 2018, Sa1840 - The colorectal cancer mucosal microbiome is defined by disease stage and the tumour metabonome, Digestive Disease Week 2018, Publisher: Elsevier, Pages: S415-S415, ISSN: 0016-5085

Conference paper

Inglese P, Strittmatter N, Doria L, Mroz A, Speller A, Poynter L, Dannhorn A, Kudo H, Mirnezami R, Goldin RD, Nicholson JK, Takats Z, Glen RCet al., 2017, Network analysis of mass spectrometry imaging data from colorectal cancer identifies key metabolites common to metastatic development, Publisher: Cold Spring Harbor Laboratory

<jats:title>Abstract</jats:title><jats:p>A deeper understanding of inter-tumor and intra-tumor heterogeneity is a critical factor for the advancement of next generation strategies against cancer. The heterogeneous morphology exhibited by solid tumors is mirrored by their metabolic heterogeneity. Defining the basic biological mechanisms that underlie tumor cell variability will be fundamental to the development of personalized cancer treatments. Variability in the molecular signatures found in local regions of cancer tissues can be captured through an untargeted analysis of their metabolic constituents. Here we demonstrate that DESI mass spectrometry imaging (MSI) combined with network analysis can provide detailed insight into the metabolic heterogeneity of colorectal cancer (CRC). We show that network modules capture signatures which differentiate tumor metabolism in the core and in the surrounding region. Moreover, module preservation analysis of network modules between patients with and without metastatic recurrence explains the inter-subject metabolic differences associated with diverse clinical outcomes such as metastatic recurrence.</jats:p><jats:sec><jats:title>Significance</jats:title><jats:p>Network analysis of DESI-MSI data from CRC human tissue reveals clinically relevant co-expression ion patterns associated with metastatic susceptibility. This delineates a more complex picture of tumor heterogeneity than conventional hard segmentation algorithms. Using tissue sections from central regions and at a distance from the tumor center, ion co-expression patterns reveal common features among patients who developed metastases (up of &gt; 5 years) not preserved in patients who did not develop metastases. This offers insight into the nature of the complex molecular interactions associated with cancer recurrence. Presently, predicting CRC relapse is challenging, and histopathologically like-for-like cancers freque

Working paper

Galea D, Inglese P, Cammack L, Strittmatter N, Rebec M, Mirnezami R, Laponogov I, Kinross J, Nicholson J, Takats Z, Veselkov KAet al., 2017, Translational utility of a hierarchical classification strategy in biomolecular data analytics., Scientific Reports, Vol: 7, ISSN: 2045-2322

Hierarchical classification (HC) stratifies and classifies data from broad classes into more specific classes. Unlike commonly used data classification strategies, this enables the probabilistic prediction of unknown classes at different levels, minimizing the burden of incomplete databases. Despite these advantages, its translational application in biomedical sciences has been limited. We describe and demonstrate the implementation of a HC approach for "omics-driven" classification of 15 bacterial species at various taxonomic levels achieving 90-100% accuracy, and 9 cancer types into morphological types and 35 subtypes with 99% and 76% accuracy, respectively. Unknown bacterial species were probabilistically assigned with 100% accuracy to their respective genus or family using mass spectra (n = 284). Cancer types were predicted by mRNA data (n = 1960) for most subtypes with 95-100% accuracy. This has high relevance in clinical practice where complete datasets are difficult to compile with the continuous evolution of diseases and emergence of new strains, yet prediction of unknown classes, such as bacterial species, at upper hierarchy levels may be sufficient to initiate antimicrobial therapy. The algorithms presented here can be directly translated into clinical-use with any quantitative data, and have broad application potential, from unlabeled sample identification, to hierarchical feature selection, and discovery of new taxonomic variants.

Journal article

Inglese P, Alexander JL, Mroz A, Takats Z, Glen Ret al., 2017, Variational autoencoders for tissue heterogeneity exploration from (almost) no preprocessed mass spectrometry imaging data, arXiv

The paper presents the application of Variational Autoencoders (VAE) for datadimensionality reduction and explorative analysis of mass spectrometry imagingdata (MSI). The results confirm that VAEs are capable of detecting the patternsassociated with the different tissue sub-types with performance than standardapproaches.

Journal article

Kinross JM, Alexander J, Perdones-Monter A, Cameron S, Scott A, Poynter L, Inglese P, Atkinson S, Soucek P, Hughes D, Susova S, Liska V, Goldin R, Takats Z, Marchesi J, Kinross J, Teare Jet al., 2017, A Prospective Multi-National Study of the Colorectal Cancer Mucosal Microbiome Reveals Specific Taxonomic Changes Indicative of Disease Stage and Prognosis, DDW

Conference paper

Lewis H, Pruski P, Kindinger L, Brown R, Lee Y, Inglese P, Bennett P, Takats Z, MacIntyre DAet al., 2017, Desorption electrospray ionisation mass spectrometry permits identification of vaginal mucosal metabolome signatures associated with preterm birth risk, Publisher: WILEY, Pages: 135-135, ISSN: 1470-0328

Conference paper

Inglese P, McKenzie JS, Mroz A, Kinross J, Veselkov K, Holmes E, Takats Z, Nicholson JK, Glen RCet al., 2017, Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer, Chemical Science, Vol: 8, Pages: 3500-3511, ISSN: 2041-6539

Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.

Journal article

Pruski P, MacIntyre DA, Lewis HV, Inglese P, dos Santos Correia G, Hansel TT, Bennett PR, Holmes E, Takats Zet al., 2016, Medical swab analysis using desorption electrospray ionization mass spectrometry (DESI-MS) – a non-invasive approach for mucosal diagnostics, Analytical Chemistry, Vol: 89, Pages: 1540-1550, ISSN: 0003-2700

Medical swabs are routinely used worldwide to sample human mucosa for microbiological screening with culture methods. These are usually time-consuming and have a narrow focus on screening for particular microorganism species. As an alternative, direct mass spectrometric profiling of the mucosal metabolome provides a broader window into the mucosal ecosystem. We present for the first time a minimal effort/minimal-disruption technique for augmenting the information obtained from clinical swab analysis with mucosal metabolome profiling using desorption electrospray ionization mass spectrometry (DESI-MS) analysis. Ionization of mucosal biomass occurs directly from a standard rayon swab mounted on a rotating device and analyzed by DESI MS using an optimized protocol considering swab–inlet geometry, tip–sample angles and distances, rotation speeds, and reproducibility. Multivariate modeling of mass spectral fingerprints obtained in this way readily discriminate between different mucosal surfaces and display the ability to characterize biochemical alterations induced by pregnancy and bacterial vaginosis (BV). The method was also applied directly to bacterial biomass to confirm the ability to detect intact bacterial species from a swab. These results highlight the potential of direct swab analysis by DESI-MS for a wide range of clinical applications including rapid mucosal diagnostics for microbiology, immune responses, and biochemistry.

Journal article

Veselkov KA, Inglese, Galea D, McKenzie JS, Nicholson JKet al., 2016, Statistical Tools for Molecular Covariance Spectroscopy, Encyclopedia of Spectroscopy and Spectrometry, Editors: Lindon, Tranter, Koppenaal, Publisher: Elsevier B.V., Pages: 243-249, ISBN: 978-0-12-803224-4

One major application of modern spectroscopic and spectrometric techniques is to measure hundreds to thousands of molecules in biological specimens as part of a process of metabolic phenotyping. Statistical spectroscopy covers a range of techniques used for the recovery of correlated intensity patterns within and between molecules. This plays an essential role in the annotation of molecular features of potential biological or diagnostic significance. The article introduces a variety of univariate and multivariate statistical tools for molecular covariance spectroscopy.

Book chapter

Allen GI, Amoroso N, Anghel C, Balagurusamy V, Bare CJ, Beaton D, Bellotti R, Bennett DA, Boehme K, Boutros PC, Caberlotto L, Caloian C, Campbell F, Neto EC, Chang YC, Chen B, Chen CY, Chien TY, Clark T, Das S, Davatzikos C, Deng J, Dillenberger D, Dobson RJ, Dong Q, Doshi J, Duma D, Errico R, Erus G, Everett E, Fardo DW, Friend SH, Fröhlich H, Gan J, St George-Hyslop P, Ghosh SS, Glaab E, Green RC, Guan Y, Hong MY, Huang C, Hwang J, Ibrahim J, Inglese P, Jiang Q, Katsumata Y, Kauwe JS, Klein A, Kong D, Krause R, Lalonde E, Lauria M, Lee E, Lin X, Liu Z, Livingstone J, Logsdon BA, Lovestone S, Lyappan A, Ma M, Malhotra A, Mangravite LM, Maxwell TJ, Merrill E, Nagorski J, Namasivayam A, Narayan M, Naz M, Newhouse SJ, Norman TC, Nurtdinov RN, Oyang YJ, Pawitan Y, Peng S, Peters MA, Piccolo SR, Praveen P, Priami C, Sabelnykova VY, Senger P, Shen X, Simmons A, Sotiras A, Stolovitzky G, Tangaro S, Tateo A, Tung YA, Tustison NJ, Varol E, Vradenburg G, Weiner MW, Xiao G, Xie L, Xie Y, Xu J, Yang H, Zhan X, Zhou Y, Zhu F, Zhu H, Zhu Set al., 2016, Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease, Alzheimer's & Dementia, Vol: 12, Pages: 645-653, ISSN: 1552-5260

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for to prediction of cognitive performance.

Journal article

Veselkov KA, Inglese P, Galea D, McKenzie JS, Nicholson JKet al., 2016, Statistical tools for molecular covariance spectroscopy, Encyclopedia of Spectroscopy and Spectrometry, Pages: 243-249, ISBN: 9780128032244

One major application of modern spectroscopic and spectrometric techniques is to measure hundreds to thousands of molecules in biological specimens as part of a process of metabolic phenotyping. Statistical spectroscopy covers a range of techniques used for the recovery of correlated intensity patterns within and between molecules. This plays an essential role in the annotation of molecular features of potential biological or diagnostic significance. The article introduces a variety of univariate and multivariate statistical tools for molecular covariance spectroscopy.

Book chapter

Inglese P, Amoroso N, Boccardi M, Bocchetta M, Bruno S, Chincarini A, Errico R, Frisoni GB, Maglietta R, Redolfi A, Sensi F, Tangaro S, Tateo A, Bellotti R, Initiative ADNet al., 2015, Multiple RF classifier for the hippocampus segmentation: method and validation on EADC-ADNI harmonized hippocampal protocol, Physica Medica-European Journal of Medical Physics, Vol: 31, Pages: 1085-1091, ISSN: 1120-1797

The hippocampus has a key role in a number of neurodegenerative diseases, such as Alzheimer’s Disease.Here we present a novel method for the automated segmentation of the hippocampus from structuralmagnetic resonance images (MRI), based on a combination of multiple classifiers. The method is validatedon a cohort of 50 T1 MRI scans, comprehending healthy control, mild cognitive impairment, andAlzheimer’s Disease subjects. The preliminary release of the EADC-ADNI Harmonized Protocol traininglabels is used as gold standard. The fully automated pipeline consists of a registration using an affinetransformation, the extraction of a local bounding box, and the classification of each voxel in two classes(background and hippocampus). The classification is performed slice-by-slice along each of the three orthogonaldirections of the 3D-MRI using a Random Forest (RF) classifier, followed by a fusion of the threefull segmentations. Dice coefficients obtained by multiple RF (0.87 ± 0.03) are larger than those obtainedby a single monolithic RF applied to the entire bounding box, and are comparable to state-ofthe-art.A test on an external cohort of 50 T1 MRI scans shows that the presented method is robust andreliable. Additionally, a comparison of local changes in the morphology of the hippocampi between thethree subject groups is performed. Our work showed that a multiple classification approach can be implementedfor the segmentation for the measurement of volume and shape changes of the hippocampuswith diagnostic purposes.

Journal article

Maglietta R, Amoroso N, Boccardi M, Bruno S, Chincarini A, Frisoni GB, Inglese P, Redolfi A, Tangaro S, Tateo A, Bellotti R, The ADNIet al., 2015, Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm, Pattern Analysis and Applications, Vol: 19, Pages: 579-591, ISSN: 1433-755X

The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algorithm, called RUSBoost, which combines data random undersampling with a boosting algorithm. RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class. The RUSBoost performances were compared with those of ADABoost, Random Forest and the publicly available brain segmentation package, FreeSurfer. This study was conducted on a data set of 50 T1-weighted structural brain images. The RUSBoost-based segmentation tool achieved the best results with a Dice’s index of (Formula presented.) ((Formula presented.)) for the left (right) brain hemisphere. An independent data set of 50 T1-weighted structural brain scans was used for an independent validation of the fully trained strategies. Again the RUSBoost segmentations compared favorably with manual segmentations with the highest performances among the four tools. Moreover, the Pearson correlation coefficient between hippocampal volumes computed by manual and RUSBoost segmentations was 0.83 (0.82) for left (right) side, statistically significant, and higher than those computed by Adaboost, Random Forest and FreeSurfer. The proposed method may be suitable for accurate, robust and statistically significant segmentations of hippocampi.

Journal article

Bron EE, Smits M, van der Flier WM, Vrenken H, Barkhof F, Scheltens P, Papma JM, Steketee RME, Orellana CM, Meijboom R, otherset al., 2015, Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge, Neuroimage, Vol: 111, Pages: 562-579, ISSN: 1095-9572

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.

Journal article

Tangaro S, Amoroso N, Brescia M, Cavuoti S, Chincarini A, Errico R, Inglese P, Longo G, Maglietta R, Tateo A, otherset al., 2015, Feature Selection in MRIs for Hippocampal Segmentation, arXiv

Journal article

Tangaro S, Amoroso N, Brescia M, Cavuoti S, Chincarini A, Errico R, Inglese P, Longo G, Maglietta R, Tateo A, otherset al., 2015, Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation, Computational and Mathematical Methods in Medicine

Journal article

Maglietta R, Amoroso N, Bruno S, Chincarini A, Frisoni G, Inglese P, Tangaro S, Tateo A, Bellotti Ret al., 2013, Random Forest Classification for Hippocampal Segmentation in 3D MR Images, Publisher: IEEE Computer Society, Pages: 264-267

Conference paper

Inglese P, Huang H, Wu V, Lewis MR, Takats Zet al., Mass recalibration for desorption electrospray ionization mass spectrometry imaging using endogenous reference ions

<jats:label>1</jats:label><jats:title>Abstract</jats:title><jats:p>Mass spectrometry imaging (MSI) data often consist of tens of thousands of mass spectra collected from a sample surface. During the time necessary to perform a single acquisition, it is likely that uncontrollable factors alter the validity of the initial mass calibration of the instrument, resulting in mass errors of magnitude significantly larger than their nominal values. This phenomenon has a two-fold detrimental effect: a) it reduces the ability to interpret the results based on the observed signals, b) it can affect the quality of the observed signal spatial distributions. Here, we present a post-acquisition computational method capable of reducing the observed mass drift in biological samples, exploiting the presence of typical molecules with a known mass-to-charge ratio. The method is tested on time-of-flight and Orbitrap mass spectrometry analyzers interfaced to a desorption electrospray ionization (DESI) source.</jats:p>

Journal article

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