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3. La risonanza magnetica nella scienza degli alimenti. Con e-book

Author

Mannina, L., Sobolev, A. P., Proietti, N., Di Tullio, V., Angilè, F., Bellomaria, A., Bertelli, D., Bertocchi, F., Caligiani, L. R., Mucci, A., Righi, V., Tenori, . L., and Urbinati, E.

Subjects
risonanza magnetica, NMR, Alimenti, risonanza magnetica, food, food, Alimenti, NMR
Published
2023

4. Risk assessment of Disease Recurrence (DR) through serum metabolomics analysis in elderly patients (pts) with early breast cancer (eBC)

Author

Lisanti, C., primary, Risi, E., additional, Vignoli, A., additional, Becheri, D., additional, Biagioni, C., additional, Paderi, A., additional, Cappadona, S., additional, Del Monte, F., additional, Moretti, E., additional, Sanna, G., additional, Livraghi, L., additional, Malorni, L., additional, Benelli, M., additional, Puglisi, F., additional, Mottino, G., additional, Luchinat, C., additional, Tenori, L., additional, and Biganzoli, L., additional

Published
2022
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5. 148P Serum metabolomics based risk assessment of disease recurrence in elderly patients with early breast cancer (eBC)

Author

Risi, E., primary, Vignoli, A., additional, Lisanti, C., additional, Biagioni, C., additional, Paderi, A., additional, Cappadona Sciammetta, S., additional, Del Monte, F., additional, Moretti, E., additional, Sanna, G., additional, Livraghi, L., additional, Malorni, L., additional, Benelli, M., additional, Puglisi, F., additional, Luchinat, C., additional, Tenori, L., additional, and Biganzoli, L., additional

Published
2022
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6. Early downregulation of hsa-miR-144-3p in serum from drug-naïve Parkinson's disease patients

Author

Zago E., Dal Molin A., Dimitri G. M., Xumerle L., Pirazzini C., Bacalini M. G., Maturo M. G., Azevedo T., Spasov S., Gomez-Garre P., Perinan M. T., Jesus S., Baldelli L., Sambati L., Calandra Buonaura G., Garagnani P., Provini F., Cortelli P., Mir P., Trenkwalder C., Mollenhauer B., Franceschi C., Lio P., Nardini C., Adarmes-Gomez A., Bartoletti-Stella A., Bhatia K. P., Marta B. -T., Boninsegna C., Broli M., Dolores B. -R., Calandra-Buonaura G., Capellari S., Carrion-Claro M., Cilea R., Clayton R., Molin A. D., De Luca S., De Massis P., Doykov I., Escuela-Martin R., Fabbri G., Gabellini A., Giuliani C., Guaraldi P., Hagg S., Hallqvist J., Halsband C., Heywood W., Houlden H., Huertas I., Jylhava J., Labrador-Espinosa M. A., Licari C., Luchinat C., Macias D., Macri S., Magrinelli F., Rodriguez J. F. M., Massimo D., Mengozzi G., Meoni G., Mignani F., Milazzo M., Mills K., Nassetti S. A., Pedersen N. L., Perinan-Tocino M. T., Ravaioli F., Sala C., Scaglione C. L. M., Schade S., Schreglmann S., Strom S., Tejera-Parrado C., Tenori L., Turano P., Valzania F., Ortega R. V., Williams D., Apollo - University of Cambridge Repository, Zago E., Dal Molin A., Dimitri G.M., Xumerle L., Pirazzini C., Bacalini M.G., Maturo M.G., Azevedo T., Spasov S., Gomez-Garre P., Perinan M.T., Jesus S., Baldelli L., Sambati L., Calandra Buonaura G., Garagnani P., Provini F., Cortelli P., Mir P., Trenkwalder C., Mollenhauer B., Franceschi C., Lio P., Nardini C., Adarmes-Gomez A., Bartoletti-Stella A., Bhatia K.P., Marta B.-T., Boninsegna C., Broli M., Dolores B.-R., Calandra-Buonaura G., Capellari S., Carrion-Claro M., Cilea R., Clayton R., Molin A.D., De Luca S., De Massis P., Doykov I., Escuela-Martin R., Fabbri G., Gabellini A., Giuliani C., Guaraldi P., Hagg S., Hallqvist J., Halsband C., Heywood W., Houlden H., Huertas I., Jylhava J., Labrador-Espinosa M.A., Licari C., Luchinat C., Macias D., Macri S., Magrinelli F., Rodriguez J.F.M., Massimo D., Mengozzi G., Meoni G., Mignani F., Milazzo M., Mills K., Nassetti S.A., Pedersen N.L., Perinan-Tocino M.T., Ravaioli F., Sala C., Scaglione C.L.M., Schade S., Schreglmann S., Strom S., Tejera-Parrado C., Tenori L., Turano P., Valzania F., Ortega R.V., and Williams D.

Subjects
Male, Aging, Molecular biology, Science, Immunology, Article, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Medical research, Humans, Parkinson, 030304 developmental biology, Aged, 0303 health sciences, Multidisciplinary, Biological techniques, Parkinson Disease, Middle Aged, 3. Good health, nervous system diseases, Computational biology and bioinformatics, MicroRNAs, Neurology, ageing, Medicine, Female, 030217 neurology & neurosurgery, Biomarkers
Abstract

Advanced age represents one of the major risk factors for Parkinson's Disease. Recent biomedical studies posit a role for microRNAs, also known to be remodelled during ageing. However, the relationship between microRNA remodelling and ageing in Parkinson's Disease, has not been fully elucidated. Therefore, the aim of the present study is to unravel the relevance of microRNAs as biomarkers of Parkinson's Disease within the ageing framework. We employed Next Generation Sequencing to profile serum microRNAs from samples informative for Parkinson's Disease (recently diagnosed, drug-naïve) and healthy ageing (centenarians) plus healthy controls, age-matched with Parkinson's Disease patients. Potential microRNA candidates markers, emerging from the combination of differential expression and network analyses, were further validated in an independent cohort including both drug-naïve and advanced Parkinson's Disease patients, and healthy siblings of Parkinson's Disease patients at higher genetic risk for developing the disease. While we did not find evidences of microRNAs co-regulated in Parkinson's Disease and ageing, we report that hsa-miR-144-3p is consistently down-regulated in early Parkinson's Disease patients. Moreover, interestingly, functional analysis revealed that hsa-miR-144-3p is involved in the regulation of coagulation, a process known to be altered in Parkinson's Disease. Our results consistently show the down-regulation of hsa-mir144-3p in early Parkinson's Disease, robustly confirmed across a variety of analytical and experimental analyses. These promising results ask for further research to unveil the functional details of the involvement of hsa-mir144-3p in Parkinson's Disease., This work was supported by the Horizon 2020 Framework Programme (Grant number 634821, PROPAG-AGING).

Published
2022

7. NMR Spectroscopy Combined with Machine Learning Approaches for Age Prediction in Healthy and Parkinson’s Disease Cohorts through Metabolomic Fingerprints

Author

Dimitri, GM, Meoni, G, Tenori, L, Luchinat, C, Lió, P, Dimitri, GM [0000-0002-2728-4272], Meoni, G [0000-0002-8608-4641], Tenori, L [0000-0001-6438-059X], Luchinat, C [0000-0003-2271-8921], and Apollo - University of Cambridge Repository

Subjects
Fluid Flow and Transfer Processes, metabolomics aging, Process Chemistry and Technology, Parkinson's disease, machine learning, spectrum, metabolites, lipids, Parkinson’s disease, biological age, General Engineering, Computer Science Applications, General Materials Science, Instrumentation
Abstract

Peer reviewed: True, Biological aging can be affected by several factors such as drug treatments and pathological conditions. Metabolomics can help in the estimation of biological age by analyzing the differences between predicted and actual chronological age in different subjects. In this paper, we compared three different and well-known machine learning approaches—SVM, ElasticNet, and PLS—to build a model based on the 1H-NMR metabolomic data of serum samples, able to predict chronological age in control individuals. Then, we tested these models in two pathological cohorts of de novo and advanced PD patients. The discrepancies observed between predicted and actual age in patients are interpreted as a sign of a (pathological) biological aging process.

Published
2022

8. A framework for validating AI in precision medicine: considerations from the European ITFoC consortium

Author

Tsopra, R, Fernandez, X, Luchinat, C, Alberghina, L, Lehrach, H, Vanoni, M, Dreher, F, Sezerman, O, Cuggia, M, de Tayrac, M, Miklasevics, E, Itu, L, Geanta, M, Ogilvie, L, Godey, F, Boldisor, C, Campillo-Gimenez, B, Cioroboiu, C, Ciusdel, C, Coman, S, Hijano Cubelos, O, Itu, A, Lange, B, Le Gallo, M, Lespagnol, A, Mauri, G, Soykam, H, Rance, B, Turano, P, Tenori, L, Vignoli, A, Wierling, C, Benhabiles, N, Burgun, A, Tsopra R., Fernandez X., Luchinat C., Alberghina L., Lehrach H., Vanoni M., Dreher F., Sezerman O. U., Cuggia M., de Tayrac M., Miklasevics E., Itu L. M., Geanta M., Ogilvie L., Godey F., Boldisor C. N., Campillo-Gimenez B., Cioroboiu C., Ciusdel C. F., Coman S., Hijano Cubelos O., Itu A., Lange B., Le Gallo M., Lespagnol A., Mauri G., Soykam H. O., Rance B., Turano P., Tenori L., Vignoli A., Wierling C., Benhabiles N., Burgun A., Tsopra, R, Fernandez, X, Luchinat, C, Alberghina, L, Lehrach, H, Vanoni, M, Dreher, F, Sezerman, O, Cuggia, M, de Tayrac, M, Miklasevics, E, Itu, L, Geanta, M, Ogilvie, L, Godey, F, Boldisor, C, Campillo-Gimenez, B, Cioroboiu, C, Ciusdel, C, Coman, S, Hijano Cubelos, O, Itu, A, Lange, B, Le Gallo, M, Lespagnol, A, Mauri, G, Soykam, H, Rance, B, Turano, P, Tenori, L, Vignoli, A, Wierling, C, Benhabiles, N, Burgun, A, Tsopra R., Fernandez X., Luchinat C., Alberghina L., Lehrach H., Vanoni M., Dreher F., Sezerman O. U., Cuggia M., de Tayrac M., Miklasevics E., Itu L. M., Geanta M., Ogilvie L., Godey F., Boldisor C. N., Campillo-Gimenez B., Cioroboiu C., Ciusdel C. F., Coman S., Hijano Cubelos O., Itu A., Lange B., Le Gallo M., Lespagnol A., Mauri G., Soykam H. O., Rance B., Turano P., Tenori L., Vignoli A., Wierling C., Benhabiles N., and Burgun A.

Abstract

Background: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. Methods: The European “ITFoC (Information Technology for the Future Of Cancer)” consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the “ITFoC Challenge”. This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. Conclusions: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

Published
2021

10. Early downregulation of hsa-miR-144-3p in serum from drug-naïve Parkinson’s disease patients

Author

Zago, E., Dal Molin, A., Dimitri, G. M., Xumerle, L., Pirazzini, C., Bacalini, M. G., Maturo, M. G., Azevedo, T., Spasov, S., Gomez-Garre, P., Perinan, M. T., Jesus, S., Baldelli, L., Sambati, L., Calandra-Buonaura, G., Garagnani, P., Provini, F., Cortelli, P., Mir, P., Trenkwalder, C., Mollenhauer, B., Franceschi, C., Lio, P., Nardini, C., Adarmes-Gomez, A., Bartoletti-Stella, A., Bhatia, K. P., Marta, B. -T., Boninsegna, C., Broli, M., Dolores, B. -R., Capellari, S., Carrion-Claro, M., Cilea, R., Clayton, R., Molin, A. D., De Luca, S., De Massis, P., Doykov, I., Escuela-Martin, R., Fabbri, G., Gabellini, A., Giuliani, C., Guaraldi, P., Hagg, S., Hallqvist, J., Halsband, C., Heywood, W., Houlden, H., Huertas, I., Jylhava, J., Labrador-Espinosa, M. A., Licari, C., Luchinat, C., Macias, D., Macri, S., Magrinelli, F., Rodriguez, J. F. M., Massimo, D., Mengozzi, G., Meoni, G., Mignani, F., Milazzo, M., Mills, K., Nassetti, S. A., Pedersen, N. L., Perinan-Tocino, M. T., Ravaioli, F., Sala, C., Scaglione, C. L. M., Schade, S., Schreglmann, S., Strom, S., Tejera-Parrado, C., Tenori, L., Turano, P., Valzania, F., Ortega, R. V., and Williams, D.

Subjects
hsa‑miR‑144‑3p, serum, Parkinson’s disease patients
Published
2022

13. Reliability of quantitative diffusion weighted imaging: a large multicenter and multiparametric study based on a unsupervised clustering analysis

Author

Mazzoni, L.N., primary, Fedeli, L., additional, Belli, G., additional, Ciccarone, A., additional, Coniglio, A., additional, Esposito, M., additional, Giannelli, M., additional, Sghedoni, R., additional, Tarducci, R., additional, Altabella, L., additional, Belligotti, E., additional, Benelli, M., additional, Bernardi, L., additional, Betti, M., additional, Caivano, R., additional, Carnì, M., additional, Chiappiniello, A., additional, Cimolai, S., additional, Cretti, F., additional, Fulcheri, C., additional, Gasperi, C., additional, Giacometti, M., additional, Levrero, F., additional, Lizio, D., additional, Maieron, M., additional, Marzi, S., additional, Mascaro, L., additional, Mazzocchi, S., additional, Meliadò, G., additional, Morzenti, S., additional, Niespolo, A., additional, Nocetti, L., additional, Noferini, L., additional, Oberhofer, N., additional, Orsingher, L., additional, Quattrocchi, M.G., additional, Ricci, A., additional, Savini, A., additional, Taddeucci, A., additional, Testa, C., additional, Tortoli, P., additional, Luchinat, C., additional, Tenori, L., additional, Gobbi, G., additional, Gori, C., additional, and Busoni, S., additional

Published
2021
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14. Multicenter comparison of MR scanners for quantitative diffusion weighted imaging: apparent diffusion coefficient dependence on acquisition plan and spatial position – preliminary results

Author

Fedeli, L., primary, Belli, G., additional, Ciccarone, A., additional, Coniglio, A., additional, Esposito, M., additional, Giannelli, M., additional, Sghedoni, R., additional, Tarducci, R., additional, Altabella, L., additional, Belligotti, E., additional, Benelli, M., additional, Bernardi, L., additional, Betti, M., additional, Caivano, R., additional, Carni, M., additional, Chiappiniello, A., additional, Cimolai, S., additional, Cretti, F., additional, Fulcheri, C., additional, Gasperi, C., additional, Giacometti, M., additional, Levrero, F., additional, Lizio, D., additional, Maieron, M., additional, Marzi, S., additional, Mascaro, L., additional, Mazzocchi, S., additional, Meliado, G., additional, Morzenti, S., additional, Niespolo, A., additional, Nocetti, L., additional, Noferini, L., additional, Oberhofer, N., additional, Orsingher, L., additional, Quattrocchi, M.G., additional, Ricci, A., additional, Savini, A., additional, Taddeucci, A., additional, Testa, C., additional, Tortoli, P., additional, Luchinat, C., additional, Tenori, L., additional, Gobbi, G., additional, Gori, C., additional, Busoni, S., additional, and Mazzoni, L.N., additional

Published
2021
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17. Metabolomics - the stethoscope for the 21st century

Author

Ashrafian, H, Sounderajah, V, Glen, R, Ebbels, T, Blaise, BJ, Kalra, D, Kultima, K, Spjuth, O, Tenori, L, Salek, R, Kale, N, Haug, K, Schober, D, Rocca-Serra, P, O'Donovan, C, Steinbeck, C, Cano, I, De Atauri, P, Cascante, M, and National Institute of Health Research

Subjects
Precision medicine, Metabolomics, Personalised medicine, Microbiology, 1117 Public Health and Health Services
Abstract

Metabolomics offers systematic identification and quantification of all metabolic products from the human body. This field could provide clinicians with new sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualised level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice and discuss the translational challenges that the field faces. We searched PubMed, Medline and Embase for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and NMR based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use centre around scalability of data interpretation, standardisation of sample handling practice and e-infrastructure. Routine utilisation of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.

Published
2020

19. Sarcolab pilot study into skeletal muscle's adaptation to long-term spaceflight

Author

Rittweger J, Albracht K, Flück M, Ruoss S, Brocca L, Longa E, Moriggi M, Seynnes O, Di Giulio I, Tenori L, Vignoli A, Capri M, Gelfi C, Luchinat C, Franceschi C, Bottinelli R, Cerretelli P, Narici M., and Rittweger J, Albracht K, Flück M, Ruoss S, Brocca L, Longa E, Moriggi M, Seynnes O, Di Giulio I, Tenori L, Vignoli A, Capri M, Gelfi C, Luchinat C, Franceschi C, Bottinelli R, Cerretelli P, Narici M.

Subjects
skeletal-muscle, Spaceflight
Abstract

Spaceflight causes muscle wasting. The Sarcolab pilot study investigated two astronauts with regards to plantar flexor muscle size, architecture, and function, and to the underlying molecular adaptations in order to further the understanding of muscular responses to spaceflight and exercise countermeasures. Two crew members (A and B) spent 6 months in space. Crew member A trained less vigorously than B. Postflight, A showed substantial decrements in plantar flexor volume, muscle architecture, in strength and in fiber contractility, which was strongly mitigated in B. The difference between these crew members closely reflected FAK-Y397 abundance, a molecular marker of muscle's loading history. Moreover, crew member A showed downregulation of contractile proteins and enzymes of anaerobic metabolism, as well as of systemic markers of energy and protein metabolism. However, both crew members exhibited decrements in muscular aerobic metabolism and phosphate high energy transfer. We conclude that countermeasures can be effective, particularly when resistive forces are of sufficient magnitude. However, to fully prevent space-related muscular deterioration, intersubject variability must be understood, and intensive exercise countermeasures programs seem mandatory. Finally, proteomic and metabolomic analyses suggest that exercise benefits in space may go beyond mere maintenance of muscle mass, but rather extend to the level of organismic metabolism.

Published
2018

23. A metabolomic recurrence score for risk-stratification of elderly patients (pts) with early colorectal cancer (eCRC)

Author

Donato, S Di, primary, Vignoli, A., additional, Mori, E., additional, Tenori, L., additional, Malorni, L., additional, Cantafio, S., additional, Mottino, G., additional, Becheri, D., additional, Mccartney, A., additional, Biagioni, C., additional, De Monte, F.l., additional, Leo, A Di, additional, Luchinat, C., additional, and Biganzoli, L., additional

Published
2019
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24. 20. Diffusion MRI and ADC accuracy at the isocenter: An AIFM multisite comparison study

Author

Coniglio, A., primary, Fedeli, L., additional, Belli, G., additional, Ciccarone, A., additional, Esposito, M., additional, Giannelli, M., additional, Gobbi, G., additional, Gori, C., additional, Mazzoni, L.N., additional, Nocetti, L., additional, Sghedoni, R., additional, Tarducci, R., additional, Altabella, L., additional, Belligotti, E., additional, Benelli, M., additional, Betti, M., additional, Caivano, R., additional, Carnì, M., additional, Chiappiniello, A., additional, Cimolai, S., additional, Cretti, F., additional, Fulcheri, C., additional, Gasperi, C., additional, Giacometti, M., additional, Levrero, F., additional, Lizio, D., additional, Luchinat, C., additional, Maieron, M., additional, Marzi, S., additional, Mascaro, L., additional, Mazzocchi, S., additional, Meliadò, G., additional, Morzenti, S., additional, Noferini, L., additional, Oberhofer, N., additional, Quattrocchi, M.G., additional, Ricci, A., additional, Taddeucci, A., additional, Tenori, L., additional, Torresin, A., additional, and Busoni, S., additional

Published
2018
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25. 21 Phase encoding direction and position effects on ADC in diffusion MRI: An AFIM multisite comparison study

Author

Fedeli, L, primary, Belli, G., additional, Ciccarone, A., additional, Coniglio, A., additional, Esposito, M., additional, Giannelli, M., additional, Gobbi, G., additional, Gori, C., additional, Mazzoni, L.N., additional, Nocetti, L., additional, Sghedoni, R., additional, Tarducci, R., additional, Altabella, L., additional, Belligotti, E., additional, Benelli, M., additional, Betti, M., additional, Caivano, R., additional, Carnì, M., additional, Chiappiniello, A., additional, Cimolai, S., additional, Cretti, F., additional, Fulcheri, C., additional, Gasperi, C., additional, Giacometti, M., additional, Levrero, F., additional, Lizio, D., additional, Luchinat, C., additional, Maieron, M., additional, Marzi, S., additional, Mascaro, L., additional, Mazzocchi, S., additional, Meliadò, G., additional, Morzenti, S., additional, Noferini, L., additional, Oberhofer, N., additional, Quattrocchi, M.G., additional, Ricci, A., additional, Taddeucci, A., additional, Tenori, L., additional, Torresin, A., additional, and Busoni, S., additional

Published
2018
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26. P170 - Multicenter comparison of MR scanners for quantitative diffusion weighted imaging: apparent diffusion coefficient dependence on acquisition plan and spatial position – preliminary results

Author

Fedeli, L., Belli, G., Ciccarone, A., Coniglio, A., Esposito, M., Giannelli, M., Sghedoni, R., Tarducci, R., Altabella, L., Belligotti, E., Benelli, M., Bernardi, L., Betti, M., Caivano, R., Carni, M., Chiappiniello, A., Cimolai, S., Cretti, F., Fulcheri, C., Gasperi, C., Giacometti, M., Levrero, F., Lizio, D., Maieron, M., Marzi, S., Mascaro, L., Mazzocchi, S., Meliado, G., Morzenti, S., Niespolo, A., Nocetti, L., Noferini, L., Oberhofer, N., Orsingher, L., Quattrocchi, M.G., Ricci, A., Savini, A., Taddeucci, A., Testa, C., Tortoli, P., Luchinat, C., Tenori, L., Gobbi, G., Gori, C., Busoni, S., and Mazzoni, L.N.

Published
2021
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27. OD101 - Reliability of quantitative diffusion weighted imaging: a large multicenter and multiparametric study based on a unsupervised clustering analysis

Author

Mazzoni, L.N., Fedeli, L., Belli, G., Ciccarone, A., Coniglio, A., Esposito, M., Giannelli, M., Sghedoni, R., Tarducci, R., Altabella, L., Belligotti, E., Benelli, M., Bernardi, L., Betti, M., Caivano, R., Carnì, M., Chiappiniello, A., Cimolai, S., Cretti, F., Fulcheri, C., Gasperi, C., Giacometti, M., Levrero, F., Lizio, D., Maieron, M., Marzi, S., Mascaro, L., Mazzocchi, S., Meliadò, G., Morzenti, S., Niespolo, A., Nocetti, L., Noferini, L., Oberhofer, N., Orsingher, L., Quattrocchi, M.G., Ricci, A., Savini, A., Taddeucci, A., Testa, C., Tortoli, P., Luchinat, C., Tenori, L., Gobbi, G., Gori, C., and Busoni, S.

Published
2021
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29. P3645Metabolomics by nuclear magnetic resonance identifies patients at high risk of death within two years after acute coronary syndrome

Author

Grifoni, E., primary, Vignoli, A., additional, Tenori, L., additional, Giusti, B., additional, Takis, P.G., additional, Valente, S., additional, Carrabba, N., additional, Balzi, D., additional, Barchielli, A.M., additional, Marchionni, N., additional, Gensini, G.F., additional, Marcucci, R., additional, Luchinat, C., additional, and Gori, A.M., additional

Published
2017
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31. Abstract P6-02-07: Metabolomic analysis by nuclear magnetic resonance spectroscopy discriminates hormone receptor positive/HER2 negative breast cancer cell lines resistant to palbociclib

Author

Bonechi, M, primary, Guarducci, C, additional, Meoni, G, additional, Tenori, L, additional, Biagioni, C, additional, Schiff, R, additional, Osborne, CK, additional, Luchinat, C, additional, Di Leo, A, additional, Malorni, L, additional, and Migliaccio, I, additional

Published
2017
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33. Erratum to: COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access (Metabolomics

Author

Salek, R.M., Neumann, S., Schober, D., Hummel, J., Billiau, K., Kopka, J., Correa, E., Reijmers, T., Rosato, A., Tenori, L., Turano, P., Marin, S., Deborde, C., Jacob, D., Rolin, D., Dartigues, B., Conesa, P., Haug, K., Rocca-Serra, P., O’Hagan, S., Hao, J., Vliet, M. van, Sysi-Aho, M., Ludwig, C., Bouwman, J., Cascante, M., Ebbels, T., Griffin, J.L., Moing, A., Nikolski, M., Oresic, M., Sansone, S.A., Viant, M.R., Goodacre, R., Günther, U.L., Hankemeier, T., Luchinat, C., Walther, D., and Steinbeck, C.

Subjects
MSB - Microbiology and Systems Biology, Life, Biomedical Innovation, ELSS - Earth, Life and Social Sciences, Biology, Healthy Living
Published
2015

36. Serum metabolomic as biomarkers to differentiate early from metastatic disease in elderly colorectal cancer (crc) patients

Author

Di Donato, S., primary, Mislang, A.R., additional, Vignoli, A., additional, Mori, E., additional, Vitale, S., additional, Biagioni, C., additional, Hart, C., additional, Becheri, D., additional, Del Monte, F., additional, Luchinat, C., additional, Di Leo, A., additional, Mottino, G., additional, Tenori, L., additional, and Biganzoli, L., additional

Published
2016
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37. Performance Assessment in Fingerprinting and Multi Component Quantitative NMR Analyses

Author

Gallo, V, Intini, N, Mastrorilli, P, Latronico, M, Scapicchio, P, Triggiani, M, Bevilacqua, V, Fanizzi, P, Acquotti, D, Airoldi, C, Arnesano, F, Assfalg, M, Benevelli, F, Bertelli, D, Cagliani, L, Casadei, L, Cesare Marincola, F, Colafemmina, G, Consonni, R, Cosentino, C, Davalli, S, De Pascali, S, D'Aiuto, V, Faccini, A, Gobetto, R, Lamanna, R, Liguori, F, Longobardi, F, Mallamace, D, Mazzei, P, Menegazzo, I, Milone, S, Mucci, A, Napoli, C, Pertinhez, T, Rizzuti, A, Rocchigiani, L, Schievano, E, Sciubba, F, Sobolev, A, Tenori, L, Valerio, M, Gallo, V, Intini, N, Mastrorilli, P, Latronico, M, Scapicchio, P, Triggiani, M, Bevilacqua, V, Fanizzi, P, Acquotti, D, Airoldi, C, Arnesano, F, Assfalg, M, Benevelli, F, Bertelli, D, Cagliani, L, Casadei, L, Cesare Marincola, F, Colafemmina, G, Consonni, R, Cosentino, C, Davalli, S, De Pascali, S, D'Aiuto, V, Faccini, A, Gobetto, R, Lamanna, R, Liguori, F, Longobardi, F, Mallamace, D, Mazzei, P, Menegazzo, I, Milone, S, Mucci, A, Napoli, C, Pertinhez, T, Rizzuti, A, Rocchigiani, L, Schievano, E, Sciubba, F, Sobolev, A, Tenori, L, and Valerio, M

Abstract

An interlaboratory comparison (ILC) was organized with the aim to set up quality control indicators suitable for multicomponent quantitative analysis by nuclear magnetic resonance (NMR) spectroscopy. A total of 36 NMR data sets (corresponding to 1260 NMR spectra) were produced by 30 participants using 34 NMR spectrometers. The calibration line method was chosen for the quantification of a five-component model mixture. Results show that quantitative NMR is a robust quantification tool and that 26 out of 36 data sets resulted in statistically equivalent calibration lines for all considered NMR signals. The performance of each laboratory was assessed by means of a new performance index (named Qp-score) which is related to the difference between the experimental and the consensus values of the slope of the calibration lines. Laboratories endowed with a Qp-score falling within the suitable acceptability range are qualified to produce NMR spectra that can be considered statistically equivalent in terms of relative intensities of the signals. In addition, the specific response of nuclei to the experimental excitation/relaxation conditions was addressed by means of the parameter named NR. NR is related to the difference between the theoretical and the consensus slopes of the calibration lines and is specific for each signal produced by a well-defined set of acquisition parameters. (Graph Presented).

Published
2015

38. A Metabolomic Perspective on Coeliac Disease

Author

Calabrò, A., Gralka, E., Luchinat, C., Saccenti, E., Tenori, L., Calabrò, A., Gralka, E., Luchinat, C., Saccenti, E., and Tenori, L.

Abstract

Metabolomics is an “omic” science that is now emerging with the purpose of elaborating a comprehensive analysis of the metabolome, which is the complete set of metabolites (i.e., small molecules intermediates) in an organism, tissue, cell, or biofluid. In the past decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. A metabolomics investigation of coeliac disease (CD) revealed that a metabolic fingerprint for CD can be defined, which accounts for three different but complementary components: malabsorption, energy metabolism, and alterations in gut microflora and/or intestinal permeability. In this review, we will discuss the major advancements in metabolomics of CD, in particular with respect to the role of gut microbiome and energy metabolism

Published
2014

39. Of Monkeys and Men: A Metabolomic Analysis of Static and Dynamic Urinary Metabolic Phenotypes in Two Species

Author

Saccenti, E., Tenori, L., Verbruggen, P., Timmerman, M.E., Bouwman, J., de Greef, J., Luchinat, C., Smilde, A.K., Saccenti, E., Tenori, L., Verbruggen, P., Timmerman, M.E., Bouwman, J., de Greef, J., Luchinat, C., and Smilde, A.K.

Abstract

Background Metabolomics has attracted the interest of the medical community for its potential in predicting early derangements from a healthy to a diseased metabolic phenotype. One key issue is the diversity observed in metabolic profiles of different healthy individuals, commonly attributed to the variation of intrinsic (such as (epi)genetic variation, gut microbiota, etc.) and extrinsic factors (such as dietary habits, life-style and environmental conditions). Understanding the relative contributions of these factors is essential to establish the robustness of the healthy individual metabolic phenotype. Methods To assess the relative contribution of intrinsic and extrinsic factors we compared multilevel analysis results obtained from subjects of Homo sapiens and Macaca mulatta, the latter kept in a controlled environment with a standardized diet by making use of previously published data and results. Results We observed similarities for the two species and found the diversity of urinary metabolic phenotypes as identified by nuclear magnetic resonance (NMR) spectroscopy could be ascribed to the complex interplay of intrinsic factors and, to a lesser extent, of extrinsic factors in particular minimizing the role played by diet in shaping the metabolic phenotype. Moreover, we show that despite the standardization of diet as the most relevant extrinsic factor, a clear individual and discriminative metabolic fingerprint also exists for monkeys. We investigate the metabolic phenotype both at the static (i.e., at the level of the average metabolite concentration) and at the dynamic level (i.e., concerning their variation over time), and we show that these two components sum up to the overall phenotype with different relative contributions of about 1/4 and 3/4, respectively, for both species. Finally, we show that the great degree diversity observed in the urinary metabolic phenotype of both species can be attributed to differences in both the static and dynamic part of t

Published
2014

44. NMR applications in food analysis: Part B

Author

Proietti, N., Capitani, D., Aru, V., Bellomaria, A., Bertocchi, F., Botta, B., Cagliani, L. R., Caligiani, A., Capozzi, F., Çela, D., Marincola, F. C., alessandra ciampa, Coco, L. D., Consonni, R., Corsaro, C., Delfini, M., Fanizzio, F. P., Gallo, V., Ghirga, F., Gianferri, R., Girellio, C. R., Ingallina, C., Laghi, L., Latronico, M., Longobardi, F., Luchinat, C., Mallamace, D., Mammi, S., Mandaliti, W., Mannina, L., Marini, F., Mastrorilli, P., Mazzei, P., Miccheli, A., Micozzio, A., Miloneo, S., Mucci, A., Nepravishta, R., Paci, M., Palisi, A., Sobolev, A. P., Piccolo, A., Picone, G., Randazzo, A., Righi, V., Rotondo, A., Salvo, A., Savorani, F., Scano, P., Schievano, E., Sciubba, F., Tenori, L., Trimigno, A., Turano, P., Vasi, S., Tullio, V. D., Proietti N., Capitani D., Aru V., Bellomaria A., Bertocchi F., Botta B., Cagliani L.R., Caligiani A., Capozzi F., Cela D., Marincola F.C., Ciampa A., Coco L.D., Consonni R., Corsaro C., Delfini M., Fanizzio F.P., Gallo V., Ghirga F., Gianferri R., Girellio C.R., Ingallina C., Laghi L., Latronico M., Longobardi F., Luchinat C., Mallamace D., Mammi S., Mandaliti W., Mannina L., Marini F., Mastrorilli P., Mazzei P., Miccheli A., Micozzio A., Miloneo S., Mucci A., Nepravishta R., Paci M., Palisi A., Sobolev A.P., Piccolo A., Picone G., Randazzo A., Righi V., Rotondo A., Salvo A., Savorani F., Scano P., Schievano E., Sciubba F., Tenori L., Trimigno A., Turano P., Vasi S., and Tullio V.D.

Subjects
low-field NMR relaxometry, NMR-imaging, food science, food composition, food composition, Food composition, Food science, Low-field NMR relaxometry, NMR-imaging, Chemistry (all), low-field NMR relaxometry, food science, chemometrics, NMR
Abstract

Multifarious applications of NMR (high-resolution NMR in liquid-state and in semi-solid matrices, low-field NMR relaxometry, and NMR-imaging) in the analysis of food components and entire food samples are described using examples of different food matrices and different problems related to food safety, traceability, geographical and botanical origin, farming methods, food processing, maturation and ageing, etc. Althoug NMR has not yet been recognized as an official methodology for the food control the numerous applications of NMR reported in the literature show the potenziality of this methodology also as an approach complementary to the other recognized conventional methodologies.

45. NMR applications in food analysis: Part A

Author

Sobolev, A. P., Mannina, L., Aru, V., Bellomaria, A., Bertocchi, F., Botta, B., Cagliani, L. R., Caligiani, A., Capozzi, F., Çela, D., Marincola, F. C., Ciampa, A., Del Coco, L., Consonni, R., Corsaro, C., Delfini, M., Di Tullio, V., Fanizzio, F. P., Gallo, V., Ghirga, F., Gianferri, R., Girellio, C. R., Cinzia Ingallina, Laghi, L., Latronico, M., Longobardi, F., Luchinat, C., Mallamace, D., Mammi, S., Mandaliti, W., Marini, F., Mastrorilli, P., Mazzei, P., Miccheli, A., Micozzio, A., Miloneo, S., Mucci, A., Nepravishta, R., Paci, M., Palisi, A., Piccolo, A., Picone, G., Proietti, N., Randazzo, A., Righi, V., Rotondo, A., Salvo, A., Savorani, F., Scano, P., Schievano, E., Sciubba, F., Tenori, L., Trimigno, A., Turano, P., Vasi, S., Capitani, D., Marcello Locatelli and Christian Celia (University 'G. d’Annunzio' of Chieti-Pescara, Chieti, Italy, and others), Sobolev, Anatoly Petrovich, Mannina, Luisa, Aru, Violetta, Bellomaria, Alessia, Bertocchi, Fabio, Botta, Bruno, Cagliani, Laura Ruth, Caligiani, Augusta, Capozzi, Francesco, Çela, Dorisa, Marincola, Flaminia Cesare, Ciampa, Alessandra, Coco, Laura Del, Consonni, Roberto, Corsaro, Carmelo, Delfini, Maurizio, Tullio, Valeria Di, Fanizzi, Francesco Paolo, Gallo, Vito, Ghirga, Francesca, Gianferri, Raffaella, Girelli, Chiara Roberta, Ingallina, Cinzia, Laghi, Luca, Latronico, Mario, Longobardi, Francesco, Luchinat, Claudio, Mallamace, Domenico, Mammi, Stefano, Mandaliti, Walter, Marini, Federico, Mastrorilli, Pietro, Mazzei, Pierluigi, Miccheli, Alfredo, Micozzi, Alessandra, Milone, Salvatore, Mucci, Adele, Nepravishta, Ridvan, Paci, Maurizio, Palisi, Angelica, Piccolo, Alessandro, Picone, Gianfranco, Proietti, Noemi, Randazzo, Antonio, Righi, Valeria, Rotondo, Archimede, Salvo, Andrea, Savorani, Francesco, Scano, Paola, Schievano, Elisabetta, Sciubba, Fabio, Tenori, Leonardo, Trimigno, Alessia, Turano, Paola, Vasi, Sebastiano, and Capitani, Donatella

Subjects
Chemometrics, Food composition, Food science, HR-MAS NMR, Liquid state NMR, liquid state NMR, food composition, digestive, oral, and skin physiology, Chemistry (all), Liquid state NMR, HR-MAS NMR, liquid state NMR, HR-MAS NMR, food science, food composition, chemometrics, food science, Chemometrics, Food composition, Food science, chemometrics, NMR
Abstract

Multifarious applications of NMR (high-resolution NMR in liquid-state and in semi-solid matrices, low-field NMR relaxometry, and NMR-imaging) in the analysis of food components and entire food samples are described using examples of different food matrices and different problems related to food safety, traceability, geographical and botanical origin, farming methods, food processing, maturation and ageing, etc. Althoug NMR has not yet been recognized as an official methodology for the food control the numerous applications of NMR reported in the literature show the potenziality of this methodology also as an approach complementary to the other recognized conventional methodologies.

46. NMR methodologies in food analysis

Author

Mannina, L., Sobolev, A. P., Aru, V., Bellomaria, A., Bertocchi, F., Botta, B., Cagliani, L. R., Caligiani, A., Capozzi, F., Çela, D., Marincola, F. C., Ciampa, A., Cocoo, L. D., Consonni, R., Carmelo Corsaro, Delfini, M., Tullio, V. D., Fanizzio, F. P., Gallo, V., Ghirga, F., Gianferri, R., Girellio, C. R., Ingallina, C., Laghi, L., Latronico, M., Longobardi, F., Luchinat, C., Mallamace, D., Mammi, S., Mandaliti, W., Marini, F., Mastrorilli, P., Mazzei, P., Miccheli, A., Micozzio, A., Miloneo, S., Mucci, A., Nepravishta, R., Paci, M., Palisi, A., Piccolo, A., Picone, G., Proietti, N., Randazzo, A., Righi, V., Rotondo, A., Salvo, A., Scano, P., Sciubba, F., Trimigno, A., Tenori, L., Schievano, E., Turano, P., Vasi, S., and Capitani, D.

48. NMR applications in food analysis: Part A

Author

Sobolev, A. P., Mannina, L., Aru, V., Bellomaria, A., Bertocchi, F., Botta, B., Cagliani, L. R., Caligiani, A., Capozzi, F., Çela, D., Marincola, F. C., Ciampa, A., Del Coco, L., Consonni, R., Corsaro, C., Delfini, M., Di Tullio, V., Fanizzio, F. P., Gallo, V., Ghirga, F., Gianferri, R., Girellio, C. R., Ingallina, C., Laghi, L., Latronico, M., Longobardi, F., Luchinat, C., Mallamace, D., Mammi, S., Mandaliti, W., Marini, F., Mastrorilli, P., Mazzei, P., Miccheli, A., Micozzio, A., Miloneo, S., Mucci, A., Ridvan Nepravishta, Paci, M., Palisi, A., Piccolo, A., Picone, G., Proietti, N., Randazzo, A., Righi, V., Rotondo, A., Salvo, A., Savorani, F., Scano, P., Schievano, E., Sciubba, F., Tenori, L., Trimigno, A., Turano, P., Vasi, S., and Capitani, D.

49. Serum or Plasma (and Which Plasma), That Is the Question

Author

Alessia Vignoli, Leonardo Tenori, Cristina Morsiani, Paola Turano, Miriam Capri, Claudio Luchinat, Vignoli A., Tenori L., Morsiani C., Turano P., Capri M., and Luchinat C.

Subjects
Serum, Blood Specimen Collection, EDTA, General Chemistry, Biochemistry, NMR, Citric Acid, Plasma, collection tube, lipidomics, Humans, Metabolomics, citrate, metabolomic, collection tubes, metabolomics, plasma, serum
Abstract

Blood derivatives are the biofluids of choice for metabolomic clinical studies since blood can be collected with low invasiveness and is rich in biological information. However, the choice of the blood collection tubes has an undeniable impact on the plasma and serum metabolic content. Here, we compared the metabolomic and lipoprotein profiles of blood samples collected at the same time and place from six healthy volunteers but using different collection tubes (each enrolled volunteer provided multiple blood samples at a distance of a few weeks/months): citrate plasma, EDTA plasma, and serum tubes. All samples were analyzed via nuclear magnetic resonance spectroscopy. Several metabolites showed statistically significant alterations among the three blood matrices, and also metabolites' correlations were shown to be affected. The effects of blood collection tubes on the lipoproteins' profiles are relevant too, but less marked. Overcoming the issue associated with different blood collection tubes is pivotal to scale metabolomics and lipoprotein analysis at the level of epidemiological studies based on samples from multicenter cohorts. We propose a statistical solution, based on regression, that is shown to be efficient in reducing the alterations induced by the different collection tubes for both the metabolomic and lipoprotein profiles.

Published
2022

50. A framework for validating AI in precision medicine: considerations from the European ITFoC consortium

Author

Tsopra, Rosy, Fernandez, Xose, Luchinat, Claudio, Alberghina, Lilia, Lehrach, Hans, Vanoni, Marco, Dreher, Felix, Sezerman, O Ugur, Cuggia, Marc, de Tayrac, Marie, Miklasevics, Edvins, Itu, Lucian Mihai, Geanta, Marius, Ogilvie, Lesley, Godey, Florence, Boldisor, Cristian Nicolae, Campillo-Gimenez, Boris, Cioroboiu, Cosmina, Ciusdel, Costin Florian, Coman, Simona, Hijano Cubelos, Oliver, Itu, Alina, Lange, Bodo, Le Gallo, Matthieu, Lespagnol, Alexandra, Mauri, Giancarlo, Soykam, H Okan, Rance, Bastien, Turano, Paola, Tenori, Leonardo, Vignoli, Alessia, Wierling, Christoph, Benhabiles, Nora, Burgun, Anita, Sezerman, O.Ugur, Soykam, H.Okan, Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Pontchaillou [Rennes], Institut Curie [Paris], Università degli Studi di Firenze = University of Florence (UniFI), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), Max Planck Institute for Molecular Genetics (MPIMG), Max-Planck-Gesellschaft, Alacris Theranostics GmbH [Berlin] (ATG), Acibadem University, Institut de Génétique et Développement de Rennes (IGDR), Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Riga Stradins University (RSU), Transilvania University of Brasov, Centre for Innovation in Medicine [Bucharest] (CIM), Chemistry, Oncogenesis, Stress and Signaling (COSS), Université de Rennes (UR)-CRLCC Eugène Marquis (CRLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM), CRLCC Eugène Marquis (CRLCC), Centre de Ressources Biologiques Santé (CRB Santé), Université de Rennes (UR)-CHU Pontchaillou [Rennes]-CRLCC Eugène Marquis (CRLCC), EPIGENETICS Inc. BUDOTEK [Istanbul, Turkey], Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Université Paris-Saclay, This work was supported by the ITFoC project (Information Technology for the Future of Cancer) – FLAG-ERA support., Malbec, Odile, École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), INSERM, Université de Paris, Sorbonne Université, Centre de Recherche des Cordeliers, Information Sciences to support Personalized Medicine, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), Tsopra, R, Fernandez, X, Luchinat, C, Alberghina, L, Lehrach, H, Vanoni, M, Dreher, F, Sezerman, O, Cuggia, M, de Tayrac, M, Miklasevics, E, Itu, L, Geanta, M, Ogilvie, L, Godey, F, Boldisor, C, Campillo-Gimenez, B, Cioroboiu, C, Ciusdel, C, Coman, S, Hijano Cubelos, O, Itu, A, Lange, B, Le Gallo, M, Lespagnol, A, Mauri, G, Soykam, H, Rance, B, Turano, P, Tenori, L, Vignoli, A, Wierling, C, Benhabiles, N, Burgun, A, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP)-École pratique des hautes études (EPHE), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Institut National de la Santé et de la Recherche Médicale (INSERM)-CRLCC Eugène Marquis (CRLCC)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), CHU Pontchaillou [Rennes]-CRLCC Eugène Marquis (CRLCC)-Université de Rennes 1 (UR1), Acibadem University Dspace, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-CRLCC Eugène Marquis (CRLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-CHU Pontchaillou [Rennes]-CRLCC Eugène Marquis (CRLCC)

Subjects
Artificial intelligence, MESH: Humans, MESH: Machine Learning, [SDV]Life Sciences [q-bio], Computer applications to medicine. Medical informatics, Precision medicine, Computerized decision support systems, R858-859.7, Cancer, Oncology, Personalized medicine, Algorithms, Humans, Machine Learning, Precision Medicine, Artificial Intelligence, Neoplasms, MESH: Algorithms, Computerized decision support system, MESH: Precision Medicine, [SDV] Life Sciences [q-bio], ComputingMethodologies_PATTERNRECOGNITION, MESH: Artificial Intelligence, [INFO]Computer Science [cs], MESH: Neoplasms, Research Article
Abstract

International audience; Abstract Background Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. Methods The European “ITFoC (Information Technology for the Future Of Cancer)” consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the “ITFoC Challenge”. This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. Conclusions The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

Published
2021

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