Dr Sebastiano Massaro
Sebastiano is a Senior Lecturer of Organizational Neuroscience at the Surrey Business School and an Associate Professor of Behavioural Science at the University of Warwick (Hon). Before joining Surrey, he was the Deputy Academic Lead of the Global Research Priority in Behavioural Science and an Assistant Professor of Behavioural Science at the Warwick Business School. A pioneer in the field of Organizational Neuroscience, Sebastiano has co-founded and chaired the Interest Group in Organizational Neuroscience (NEU) at the Academy of Management.
Sebastiano was awarded the inaugural PhD in Management Science from the UCL School of Management and he also graduated in Neuroscience at the University of Trieste and the International School of Advanced Studies, and in Neuroimaging at the University of Edinburgh.
Sebastiano’s research has been published in top-journal across fields, the like of Nature Biotechnology, The Lancet, Journal of Personality and Social Psychology, Organizational Research Methods, among others. His research is theoretically and methodologically focused on mapping the scholarly boundaries of the emerging field of organizational neuroscience; empirically, he investigates the interplay between affect and cognition in various kinds of decision-making, such as moral, interpersonal, and strategic; his context of choice is healthcare. Sebastiano has received several awards for both his teaching and research and his work has appeared in world-leading journals across scientific areas.
Areas of specialism
18 OCT 2019
Parenting, driverless cars and work humour among topics of free talks and events with leading academics
organizational neuroscience; behavioral science; research methods; healthcare
organizational neuroscience; behavioral science; research methods; healthcare
Postgraduate research supervision
PhD students supervised at Surrey Business School:
Sebastiano's teaching in 2022/23 is:
Leadership and Management (MSc - MANM484)
Advanced Quantitative Methods II (PhD - MAND031)
Background: Theranostic approaches—the use of diagnostics for developing targeted therapies—are gaining popularity in the field of precision medicine. They are predominately used in cancer research, whereas there is little evidence of their use in respiratory medicine. This study aims to detect theranostic biomarkers associated with respiratory-treatment responses. This will advance theory and practice on the use of biomarkers in the diagnosis of respiratory diseases and contribute to developing targeted treatments. Methods: We performed a cross-sectional analysis on a sample of 13,102 adults from the UK household longitudinal study ‘Understanding Society’. We used recursive feature selection to identify 16 biomarkers associated with respiratory treatment responses. We then implemented several machine learning algorithms using the identified biomarkers as well as age, sex, body mass index, and lung function to predict treatment response. Results: Our analysis shows that subjects with increased levels of alkaline phosphatase, glycated haemoglobin, high-density lipoprotein cholesterol, c-reactive protein, triglycerides, hemoglobin, and Clauss fibrinogen are more likely to receive respiratory treatments, adjusting for age, sex, body mass index, and lung function. Conclusions: These findings offer a valuable blueprint on why and how the use of biomarkers as diagnostic tools can prove beneficial in guiding treatment management in respiratory diseases.
Background: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous group of lung conditions that are challenging to diagnose and treat. As the presence of comorbidities often exacerbates this scenario, the characterization of patients with COPD and cardiovascular comorbidities may allow early intervention and improve disease management and care. Methods: We analysed a 4-year observational cohort of 6883 UK patients who were ultimately diagnosed with COPD and at least one cardiovascular comorbidity. The cohort was extracted from the UK Royal College of General Practitioners and Surveillance Centre database. The COPD phenotypes were identified prior to diagnosis and their reproducibility was assessed following COPD diagnosis. We then developed four classifiers for predicting cardiovascular comorbidities. Results: Three subtypes of the COPD cardiovascular phenotype were identified prior to diagnosis. Phenotype A was characterised by a higher prevalence of severe COPD, emphysema, hypertension. Phenotype B was char-acterised by a larger male majority, a lower prevalence of hypertension, the highest prevalence of the other cardiovascular comorbidities, and diabetes. Finally, phenotype C was characterised by universal hypertension, a higher prevalence of mild COPD and the low prevalence of COPD exacerbations. These phenotypes were reproduced after diagnosis with 92% accuracy. The random forest model was highly accurate for predicting hypertension while ruling out less prevalent comorbidities. Conclusions: This study identified three subtypes of the COPD cardiovascular phenotype that may generalize to other populations. Among the four models tested, the random forest classifier was the most accurate at predicting cardiovascular comorbidities in COPD patients with the cardiovascular phenotype.
Purpose Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage. Methods We used the COVID-19 Radiography database , which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model’s performance. From the external test dataset, we calculated the model’s accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity. Conclusions Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19.
Background: Chronic obstructive pulmonary disease (COPD) is a heterogeneous group of lung conditions challenging to diagnose and treat. Identification of phenotypes of patients with lung function loss may allow early intervention and improve disease management. We characterised patients with the ‘fast decliner’ phenotype, determined its reproducibility and predicted lung function decline after COPD diagnosis. Methods: A prospective 4 years observational study that applies machine learning tools to identify COPD phenotypes among 13 260 patients from the UK Royal College of General Practitioners and Surveillance Centre database. The phenotypes were identified prior to diagnosis (training data set), and their reproducibility was assessed after COPD diagnosis (validation data set). Results: Three COPD phenotypes were identified, the most common of which was the ‘fast decliner’—characterised by patients of younger age with the lowest number of COPD exacerbations and better lung function—yet a fast decline in lung function with increasing number of exacerbations. The other two phenotypes were characterised by (a) patients with the highest prevalence of COPD severity and (b) patients of older age, mostly men and the highest prevalence of diabetes, cardiovascular comorbidities and hypertension. These phenotypes were reproduced in the validation data set with 80% accuracy. Gender, COPD severity and exacerbations were the most important risk factors for lung function decline in the most common phenotype. Conclusions: In this study, three COPD phenotypes were identified prior to patients being diagnosed with COPD. The reproducibility of those phenotypes in a blind data set following COPD diagnosis suggests their generalisability among different populations.
Chronic Obstructive Pulmonary Disease (COPD) is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, clinicians have classified patients sharing certain symptomatic characteristics, such as symptom intensity and history of exacerbations, into distinct phenotypes. In recent years, the growing use of machine learning algorithms, and cluster analysis in particular, has promised to advance this classification through the integration of additional patient characteristics, including comorbidities, biomarkers, and genomic information. This combination would allow researchers to more reliably identify new COPD phenotypes, as well as better characterize existing ones, with the aim of improving diagnosis and developing novel treatments. Here, we systematically review the last decade of research progress, which uses cluster analysis to identify COPD phenotypes. Collectively, we provide a systematized account of the extant evidence, describe the strengths and weaknesses of the main methods used, identify gaps in the literature, and suggest recommendations for future research
Porumb, M., Massaro, S., Iadanza, E., & Pecchia, L., (2020), A Convolutional Neural Network Approach to Detect Congestive Heart Failure. Biomedical Signal Processing and Control.
Massaro, S., & Pecchia, L. (2019), Heart rate variability (HRV) analysis: A methodology for organizational neuroscience. Organizational Research Methods, 22, 354-393.
Scarpazza, C., Huang, H., Zangrossi, A., & Massaro, S. (2018), Is interoceptive sensitivity linked to interoceptive awareness in Alexithymia? Journal of Psychosomatic Research, 109, 132.
Cropanzano, R. S., Massaro, S., & Becker, W. J. (2017), Deontic justice and organizational neuroscience. Journal of Business Ethics, 144, 733-754.
Castaldo, R., Montesinos, L., Wan, T.S., Serban, A., Massaro, S., & Pecchia, L. (2017), Heart Rate Variability Analysis and Performance during a Repeated Mental Workload Task. IFMBE Proceedings, 65, 69-72.
Castaldo, R., Montesinos, L., Melillo, P., Massaro, S., & Pecchia, L. (2017), To What Extent Can We Shorten HRV Analysis in Wearable Sensing? A Case Study on Mental Stress Detection. IFMBE Proceedings, 65, 643-646.
Massaro, S. (2015), Neurofeedback in the Workplace: From Neurorehabilitation Hope to Neuroleadership Hype? International Journal of Rehabilitation Research, 38, 276-278.
Massaro, S. (2013), Can WHO survive? An organizational strategy question. The Lancet, 381, 726.
Massaro, S. (2012), Managing Knowledge-Intensive Workers. Nature Biotechnology, 30, 721-723.
Massaro, S. & Jong, S. (2011), Managing Knowledge-Intensive Work: A Trust Based Model. In: Toombs L.A. (Ed.), Best Paper Proceedings of the Academy of Management.
assaro, S. (2012), Breast cancer screening: new technologies for a new debate. British Medical Journal (Online First), 345: e7330/rr/615968.
Massaro, S. (2020, forthcoming), The Organizational Neuroscience of Emotions. In: L. Yang, R. S. Cropanzano, C. Daus, & V. A. M. Tur (Eds.) Cambridge Handbook of Workplace Affect Cambridge University Press: New York, USA.
Healey, M. P., Hodgkinson, G. P., & Massaro, S. (2018), Can Brains Manage? The Brain, Emotion, and Cognition in Organizations. In: L. Petitta, C. E. J. Härtel, N. M. Ashkanasy, W. J. Zerbe (Eds.), Research on Emotion in Organizations - Individual, Relational, and Contextual Dynamics of Emotions, p. 27-58. Emerald Group Publishing Limited: Bingley, UK.
Massaro, S. (2017), Neuroscience Methods: A Framework for Managerial and Organizational Cognition. In: G. P. Hodgkinson, R. J. Galavan, & K. J. Sund (Eds.), Methodological Challenges and Advances in Managerial and Organizational Cognition, New Horizons in Managerial and Organizational Cognition, p. 241-278. Emerald Group Publishing Limited: Bingley, UK.
Massaro, S. (2016), Neuroscientific Methods Applications in Strategic Management. In: G. Dagnino & C. Cinci (Eds.), Strategic Management: A Research Method Handbook, p. 254-282, Routledge: New York, USA.
Massaro, S., & Becker, W. (2015), Organizational Justice Through the Window of Neuroscience. In: D. Waldman & P. Balthazard (Eds.), Organizational Neuroscience, Monographs in Leadership and Management, p. 257-276. Emerald Group Publishing Limited: Bradford, UK.