Supervisor: Prof. Marco Paganoni External supervisor: Dott.ssa Isabella Castiglioni (IBFM-CNR; HSR)

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1 Christian Salvatore Second Year Report (2013/2014) XXVIII cycle. Dottorato di Ricerca in Fisica, Scuola di Dottorato di Scienze, Università degli Studi di Milano - Bicocca. Supervisor: Prof. Marco Paganoni External supervisor: Dott.ssa Isabella Castiglioni (IBFM-CNR; HSR) Title Development of a Decision Support System for automatic diagnosis of medical images from MRI studies Background Artificial Intelligence applied to medicine has the final aim to assist clinicians in medical decisions, such as individual or differential diagnosis. Among decision support systems, supervised machine learning methods are based on algorithms able to automatically extract multiple information from training datasets and to compute a predictive model for the classification of new data. During my first year of work, I developed a machine-learning method able to perform automatic classification of medical data. This method was applied to structural T1-weighted Magnetic Resonance Images (MRIs) of patients with Parkinson s Disease (PD) and Progressive Supranuclear Palsy (PSP), returning high accuracy, specificity and sensitivity rates (>90%). Image maps of pattern distribution of brain structural differences were also generated, reflecting the importance of each image voxel for the SVM classification. These maps allowed the identification of new MRI-related biomarkers useful for the diagnosis of PD and PSP. Purpose During the second year of work, I improved the classification performances of my supervised machine-learning algorithm and I adapted it in order to perform automatic classification of 1) Eating Disorders (ED), Anorexia Nervosa (AN) and Bulimia Nervosa (BN) by means of structural T1-weighted MRIs; 2) Autism Spectrum Disorder (ASD) by means of kinematic data acquired during a simple reach-to-drop task. The difficulty in the classification of ED patients is that, to date, individual diagnosis of ED is based only on a clinical interview complemented by physical, psychopathological and behavioral examinations. Moreover, ED diagnosis is unstable, with clinical features changing over time often switching from Anorexia to Bulimia and there are no valid biomarkers to identify individuals with ED. About ASD, the current standard for the diagnosis is the clinical judgment of symptoms and behavioral observations, together with standardized interviews or questionnaires. Recent studies have started to explore the predictive value of neurobiological and behavioral measures in ASD in order to enable a computer-aided diagnosis perspective. In particular, we considered kinematic measurements because motor abnormalities in ASD may occur very early in development and be apparent over time, being a pervasive feature of the disorder and possibly a consistent marker of ASD. Materials and Methods In this study, we acquired the following data: 1) MR images of 17 patients and 17 healthy control subjects. The group of 17 patients consisted of 11 patients with clinically diagnosed BN and 6 patients with clinical diagnosed AN. Brain structural MRI studies (T1-weighted, 3T) were performed by a Discovery MR-750 unit (GE Medical Systems, USA); 2) Kinematic data of 15 patients with ASD and 15 healthy controls. Data were automatically acquired during a simple reach-to-drop task; 17 movement-related features were measured for each subject. 1

2 For the ED study, feature extraction was performed on MR images by Principal Components Analysis (PCA) in order to reduce the number of features to be used by the classification algorithm. Extracted features (ED study) and measured features (ASD study) were ranked using the Fisher Discriminant Ratio (FDR) criterion. The SVM classifier was trained using the ranked features and the associated labels as features for the SVM training. To improve generalization, we used a linear kernel. Accuracy, specificity and sensitivity of the SVM classifier were assessed by a Leave-One-Out (LOO) crossvalidation strategy for the following comparisons: 1) ED vs. Controls, AN vs. Controls, BN vs. Controls and AN vs. BN 2) ASD vs. Controls. For the ED study, we were able to generate maps of voxel-based pattern distribution of brain structural differences, reflecting the importance of each voxel for the SVM classification for each comparison. Results The classifier allowed individual differential diagnosis with the following accuracy (specificity/sensitivity): 1) ED vs. Controls 96.7 (97.1/96.7)%, AN vs. Controls 91.7 (92.6/92.6)%, BN vs. Controls 96.5 (96.8/96.8)%, AN vs. BN 91.2 (100/88.1); 2) ASD vs. Controls 96.7 (93.8/100). For the ED study, the following MRI-related brain biomarkers were identified: primary visual cortex, cerebellar vermis, cerebellar tonsil and Anterior Cingulate Cortex (ACC) for ED vs. Controls; primary visual cortex, posterior cerebellar lobule and cerebella tonsil for AN vs. Controls; occipital lobe, cerebellum, ACC and Medial Orbitofrontal Cortex (OFC) for BN vs. Controls; primary visual cortex and cerebellar vermis for AN vs. BN. Conclusions In this second year of work, I further developed and optimized a voxel-based pattern recognition method for the automatic classification of medical data. This method was applied to 3D T1-weighted structural neuroimaging data of ED patients and to kinematic data pf ASD patients, returning high Accuracy, Sensitivity and Specificity values. Moreover, I generated maps of pattern distribution of brain structural differences for the ED (AN/BN) diagnosis, thus allowing the identification of MRI-related biomarkers for the individual diagnosis of ED and the differential diagnosis of AN and BN. Future Developments During the third year of work, I will test my algorithm on new datasets. In particular, I m applying SVM analysis to the diagnosis of Alzheimer s Disease using images from the Alzheimer s Disease Neuroimaging Initiative (ADNI) dataset (testing phase started on September); I will apply SVM analysis to the classification of structural MRI images of patients affected by Tourette Syndrome; I will apply SVM analysis to the classification of Near InfraRed Spectroscopy (NIRS) data of patients affected by Attention-Deficit/Hyperactivity Disorder (ADHD). 2

3 Attended Schools, Courses and Seminars Schools and Courses INIT/AERFAI International Summer School on Machine Learning (36 hours). Benicàsim, Spain. June 24-28, 2013 [Seminar: September, 19 th 2013]. Bayesian Methods (3 cfu - 16 hours). E. Milotti. Università degli Studi di Milano Bicocca. June 3-6, 2014 [The seminar will be held on October, 2014, as scheduled by the lecturer]. 5 th PAVIS School on Computer Vision, Pattern Recognition and Image Processing (21 hours). Istituto Italiano di Tecnologia at Fondazione Mediaterraneo, Sestri Levante (GE), Italia. September 29-October 1, 2014 [To be attended]. Percezione e Robotica (6 cfu). D. Sorrenti. Corso di Laurea Magistrale in Informatica [Exam: February, 2014]. Laboratorio di Elettronica (6 cfu + advanced individual course from june to september). G. Pessina. Corso di Laurea Triennale in Fisica [Exam: September, 2014]. Other Attended Courses Analisi Statistica dei Dati. M. Bonesini. Corso di Laurea Magistrale in Fisica. Connecting with English (66 hours). J. Weekes. [February December, 2013]. Attended Seminars Outlook in Neutrino Physics. P. Hernandez. Università degli Studi di Milano-Bicocca. February, 13 th LHC And The New Technologies To Go Beyond The Higgs Boson. L. Rossi. Università degli Studi di Milano-Bicocca. March, 13 th The Origin Of Life: From Geophysics To Biology. Development Of An Artificial Cell From Self- Organization To Replication. A. Libchaber. Università degli Studi di Milano-Bicocca. April, 10 th The Era Of Cosmic Reionization. F. Haardt. Università degli Studi di Milano-Bicocca. May, 15 th Author Workshop. M. Folizzi and E. Magistrelli. Scuola di Dottorato di Scienze, Università degli Studi di Milano-Bicocca. October, 2 nd Is there a breakdown of General Relativity in strong field regime? Addressing the issue with pulsar observation. A. Possenti. INAF/Oss. Astronomico di Cagliari. December, 4 th Cytoskeletal pattern formation: self organization of driven filaments. A. Bausch. Technische Universität, München. January, 30 th Biology in mathematical models: a new strategy for brain investigation. E. D Angelo. Università di Pavia. February, 12 th The quest for gravitational waves. E. Coccia. Università di Roma Tor Vergata. February, 26 th A demonstration fusion reactor: roadmap and remaining challenges. G. Federici. EFDA Power Plant Physics & Technology, Garching, Germany. May, 7 th

4 Teaching Experiences Tutor of the Imaging and Positioning Laboratory (FIS07 MED36) of the University Master of II Level in Adrotherapy (Università degli Studi di Pavia, Facoltà di Scienze MM.FF.NN., Dipartimento di Fisica Nucleare e Teorica - Pavia; Centro Nazionale di Adroterapia Oncologica (CNAO) - Pavia; Università degli Studi di Milano-Bicocca - Milano) [09/ /2014] Publications (2012/2014) Indexed International Papers 1. Grosso, E., López, M., Salvatore, C., Gallivanone, F., Di Grigoli, G., Valtorta, S., Moresco, R.M., Gilardi, M.C., Ramírez, J., Górriz, J.M. and Castiglioni, I. (2012). A decision Support System for the assisted diagnosis of brain tumors: a feasibility study for 18F-FDG PET preclinical studies. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS: pp ; ISSN: X; ISBN: ; DOI: /EMBC Gallivanone, F., Di Grigoli, G., Salvatore, C., Valtorta, S., Gilardi, M.C., Moresco, R.M. and Castiglioni, I. (2012). Acute stress studies in rats by 18F-FDG PET and SPM. IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) M15-4: pp ; ISBN: Castiglioni, I., Cerasa, A., Salvatore, C., Gallivanone, F., Augimeri, A., Lopez,M., Gilardi, M.C. and Quattrone, A. (2013). Machine Learning Performs Differential Individual Diagnosis of PD and PSP By Brain MRI Studies. Movement Disorders. 28 Suppl 1: Cava, C., Zoppis, I., Mauri, G., Ripamonti, M., Gallivanone, F., Salvatore, C., Gilardi, M.C. and Castiglioni, I. (2013). Combination of gene expression and genome copy number alteration has a prognostic valure for breast cancer. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 5. Gallivanone, F., Canevari, C., Gianolli, L., Salvatore, C., Della Rosa, P., Gilardi, M.C. and Castiglioni, I. (2013). A Partial Volume Effect Correction Tailored for 18F-FDG-PET oncological studies. Biomed Res Int. 2013: doi: /2013/ Salvatore, C., Cerasa, A., Castiglioni, I., Gallivanone, F., Augimeri, A., Lopez, M., Arabia, G., Morelli, M., Gilardi, M.C. and Quattrone, A. (2014). Machine Learning on brain MRI data for differential diagnosis of Parkinson s disease and Progressive Supranuclear Palsy. J Neurosci Methods Jan 30; 222: doi: /j.jneumeth Cerasa, A., Funaro, A., Castiglioni, I., Alfano, S., Salvatore, C., Martino, I., Donzuso, G., Perrotta, P., Gioia, M.C., Gilardi, M.C. and Quattrone, A. (2014). Individual detection of patients with eating disorders using support vector machine analysis of morphological imaging data: preliminary results. Internation Journal of Eating Disorders. [under revision] 8. Crippa, A., Salvatore, C., Perego, P., Forti, S., Nobile, M., Molteni, M. and Castiglioni, I. (2014). Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities. Journal of Autism and Developmental Disorders. [under revision] 4

5 International Book Chapters 9. Cava, C., Gallivanone, F., Salvatore, C., Della Rosa, P. and Castiglioni, I. (2014). Bioinformatics clouds for high-throughput technologies. Handbook of Research on Cloud Infrastructures for Big Data Anaytics. IGI Global: doi: / ch020. International Conference Proceedings 10. Gallivanone, F., Di Grigoli, G., Salvatore, C., Valtorta, S., Grosso, E., Gilardi, M.C., Moresco, R.M. and Castiglioni, I. (2012). SPM for activation studies in rats on stress conditions. 6th Hot Topics in Molecular Imaging Conference (TOPIM): p Gallivanone, F., Di Grigoli, G., Salvatore, C., Belloli, S., Valtorta, S., Raccagni, I., Gilardi, M.C., Castiglioni, I. and Moresco, R.M. (2013). Feasibility of supervised machine learning technique for assisted diagnosis of cancer: application to PET studies in small animals. European Molecular Imaging Meeting (EMIM), 8th annual meeting of the European Society for Molecular Imaging (ESMI), Turin, Italy. National Conference Proceedings 12. Gallivanone, F., Grosso, E., Di Grigoli, G., Salvatore, C., Valtorta, S., Gilardi, M.C., Moresco, R.M. and Castiglioni, I. (2012). Statistical Parametric Mapping for activation studies in rats. Atti del Congresso Nazionale di Bioingegneria: p.147; ISBN: