Publications

2024

Cabini RF, Barzaghi L, Cicolari D, Arosio P, Carrazza S, Figini S, et al. Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting. NMR Biomed 2024;37:1–16. https://doi.org/10.1002/nbm.5028.

Esposito D, Paternò G, Ricciardi R, Sarno A, Russo P, Mettivier G. A pre-processing tool to increase performance of deep learning-based CAD in digital breast Tomosynthesis. Health Technol (Berl) 2024;14:81–91. https://doi.org/10.1007/s12553-023-00804-9.

Saponaro S, Lizzi F, Serra G, Mainas F, Oliva P, Giuliano A, et al. Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders. Brain Informatics 2024;11:2. https://doi.org/10.1186/s40708-023-00217-4.

2023

Abenavoli EM, Barbetti M, Linguanti F, Mungai F, Nassi L, Puccini B, et al. Characterization of Mediastinal Bulky Lymphomas with FDG-PET-Based Radiomics and Machine Learning Techniques. Cancers (Basel) 2023;15:1–16. https://doi.org/10.3390/cancers15071931.

Alchera N, Garibotto V, Tomczyk S, Treyer V, Hock C, Gietl AF, et al. Patterns of amyloid accumulation in amyloid-negative cases. Neurobiol Aging 2023;129:99–108. https://doi.org/10.1016/j.neurobiolaging.2023.05.006.

Biondi R, Renzulli M, Golfieri R, Curti N, Carlini G, Sala C, et al. Machine Learning Pipeline for the Automated Prediction of MicrovascularInvasion in HepatocellularCarcinomas. Appl Sci 2023;13. https://doi.org/10.3390/app13031371.

Bosco P, Lancione M, Retico A, Nigri A, Aquino D, Baglio F, et al. Quality assessment, variability and reproducibility of anatomical measurements derived from T1-weighted brain imaging: The RIN–Neuroimaging Network case study. Phys Medica 2023;110. https://doi.org/10.1016/j.ejmp.2023.102577.

Campo F, Retico A, Calderoni S, Oliva P. Multi-Site MRI Data Harmonization with an Adversarial Learning Approach: Implementation to the Study of Brain Connectivity in Autism Spectrum Disorders. Appl Sci 2023;13:1–16. https://doi.org/10.3390/app13116486.

Carlini G, Gaudiano C, Golfieri R, Curti N, Biondi R, Bianchi L, et al. Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer. J Pers Med 2023;13. https://doi.org/10.3390/jpm13030478.

Grisanti SG, Massa F, Chincarini A, Pretta S, Rissotto R, Serrati C, et al. Discrepancy Between Patient and Caregiver Estimate of Apathy Predicts Dementia in Patients with Amnestic Mild Cognitive Impairment. J Alzheimers Dis 2023;93:75–86. https://doi.org/10.3233/JAD-220418.

Lizzi F. Deep learning applied to medical image analysis : Epistemology. Nuovo Cim C 2023;46:137. https://doi.org/10.1393/ncc/i2023-23137-3.

Lizzi F, Postuma I, Brero F, Cabini RF, Fantacci ME, Lascialfari A, et al. Quantification of pulmonary involvement in COVID-19 pneumonia: an upgrade of the LungQuant software for lung CT segmentation. Eur Phys J Plus 2023;138. https://doi.org/10.1140/epjp/s13360-023-03896-4.

Lopergolo D, Bianchi S, Gallus GN, Locci S, Pucci B, Leoni V, et al. Familial Alzheimer’s disease associated with heterozygous NPC1 mutation. J Med Genet 2023:jmg-2023-109219. https://doi.org/10.1136/jmg-2023-109219.

Massa F, Martinuzzo C, Gómez de San José N, Pelagotti V, Kreshpa W, Abu-Rumeileh S, et al. Cerebrospinal fluid NPTX2 changes and relationship with regional brain metabolism metrics across mild cognitive impairment due to Alzheimer’s disease. J Neurol 2023. https://doi.org/10.1007/s00415-023-12154-7.

Pasini G, Russo G, Mantarro C, Bini F, Richiusa S, Morgante L, et al. A Critical Analysis of the Robustness of Radiomics to Variations in Segmentation Methods in 18F-PSMA-1007 PET Images of Patients Affected by Prostate Cancer. Diagnostics 2023;13:3640. https://doi.org/10.3390/diagnostics13243640.

Peira E, Sensi F, Rei L, Gianeri R, Tortora D, Fiz F, et al. Towards an Automated Approach to the Semi-Quantification of [18F]F-DOPA PET in Pediatric-Type Diffuse Gliomas. J Clin Med 2023;12:2765. https://doi.org/10.3390/jcm12082765.

Scapicchio C, Chincarini A, Ballante E, Berta L, Bicci E, Bortolotto C, et al. A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia. Eur Radiol Exp 2023;7. https://doi.org/10.1186/s41747-023-00334-z.

Serra G, Mainas F, Golosio B, Retico A, Oliva P. Effect of data harmonization of multicentric dataset in ASD/TD classification. Brain Informatics 2023;10:32. https://doi.org/10.1186/s40708-023-00210-x.

Stockbauer A, Beyer L, Huber M, Kreuzer A, Palleis C, Katzdobler S, et al. Metabolic network alterations as a supportive biomarker in dementia with Lewy bodies with preserved dopamine transmission. Eur J Nucl Med Mol Imaging 2023;51:1023–34. https://doi.org/10.1007/s00259-023-06493-w.

Ubaldi L, Saponaro S, Giuliano A, Talamonti C. Robustness and predictivity of MRI-based radiomic features in glioma grade discrimination ( ∗ ). Nuovo Cim C 2023;46:142. https://doi.org/10.1393/ncc/i2023-23142-6.

Ubaldi L, Saponaro S, Giuliano A, Talamonti C, Retico A. Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning. Phys Medica 2023;107:102538. https://doi.org/10.1016/j.ejmp.2023.102538.

Verzellesi L, Botti A, Bertolini M, Trojani V, Carlini G, Nitrosi A, et al. Machine and Deep Learning Algorithms for COVID-19 Mortality Prediction Using Clinical and Radiomic Features. Electron 2023;12:1–14. https://doi.org/10.3390/electronics12183878.

Zorzi G, Berta L, Rizzetto F, De Mattia C, Felisi MMJ, Carrazza S, et al. Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches. Eur Radiol Exp 2023;7:3. https://doi.org/10.1186/s41747-022-00317-6.

2022

Etminani K, Soliman A, Davidsson A, Chang JR, Martínez-Sanchis B, Byttner S, et al. A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer’s disease, and mild cognitive impairment using brain 18F-FDG PET. Eur J Nucl Med Mol Imaging 2022;49:563–84. https://doi.org/10.1007/s00259-021-05483-0.

Soliman A, Chang JR, Etminani K, Byttner S, Davidsson A, Martínez-Sanchis B, et al. Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model. BMC Med Inform Decis Mak 2022;22:318. https://doi.org/10.1186/s12911-022-02054-7.

Massa F, Halbgebauer S, Barba L, Oeckl P, Gómez de San José N, Bauckneht M, et al. Exploring the brain metabolic correlates of process-specific CSF biomarkers in patients with MCI due to Alzheimer’s disease: preliminary data. Neurobiol Aging 2022;117:212–21. https://doi.org/10.1016/j.neurobiolaging.2022.03.019.

Girtler N, Chincarini A, Brugnolo A, Doglione E, Orso B, Morbelli S, et al. The Free and Cued Selective Reminding Test: Discriminative Values in a Naturalistic Cohort. J Alzheimer’s Dis 2022;87:887–99. https://doi.org/10.3233/JAD-215043.

Peira E, Poggiali D, Pardini M, Barthel H, Sabri O, Morbelli S, et al. A comparison of advanced semi-quantitative amyloid PET analysis methods. Eur J Nucl Med Mol Imaging 2022;49:4097–108. https://doi.org/10.1007/s00259-022-05846-1.

Orso B, Famà F, Giorgetti L, Mattioli P, Donniaquio A, Girtler N, et al. Polysomnographic correlates of sleep disturbances in de novo, drug naïve Parkinson’s Disease. Neurol Sci 2022;43:2531–6. https://doi.org/10.1007/s10072-021-05622-3.

Orso B, Arnaldi D, Peira E, Famá F, Giorgetti L, Girtler N, et al. The Role of Monoaminergic Tones and Brain Metabolism in Cognition in De Novo Parkinson’s Disease. J Parkinsons Dis 2022;12:1945–55. https://doi.org/10.3233/JPD-223308.

Orso B, Lorenzini L, Arnaldi D, Girtler N, Brugnolo A, Doglione E, et al. The Role of Hub and Spoke Regions in Theory of Mind in Early Alzheimer’s Disease and Frontotemporal Dementia. Biomedicines 2022;10:544. https://doi.org/10.3390/biomedicines10030544.

Nigri A, Ferraro S, Gandini Wheeler-Kingshott CAM, Tosetti M, Redolfi A, Forloni G, et al. Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network. Front Neurol 2022;13. https://doi.org/10.3389/fneur.2022.855125.

Palesi F, Nigri A, Gianeri R, Aquino D, Redolfi A, Biagi L, et al. MRI data quality assessment for the RIN – Neuroimaging Network using the ACR phantoms. Phys Medica 2022;104:93–100. https://doi.org/10.1016/j.ejmp.2022.10.008.

Demichelis G, Pinardi C, Giani L, Medina JP, Gianeri R, Bruzzone MG, et al. Chronic cluster headache: A study of the telencephalic and cerebellar cortical thickness. Cephalalgia 2022;42:444–54. https://doi.org/10.1177/03331024211058205.

Lombardi A, Amoroso N, Bellantuono L, Bove S, Comes MC, Fanizzi A, et al. Accurate Evaluation of Feature Contributions for Sentinel Lymph Node Status Classification in Breast Cancer. Appl Sci 2022;12. https://doi.org/10.3390/app12147227.

Tafuri B, Lombardi A, Nigro S, Urso D, Monaco A, Pantaleo E, et al. The impact of harmonization on radiomic features in Parkinson’s disease and healthy controls: A multicenter study. Front Neurosci 2022;16:1–10. https://doi.org/10.3389/fnins.2022.1012287.

Ferini G, Castorina P, Valenti V, Illari SI, Sachpazidis I, Castorina L, et al. A Novel Radiotherapeutic Approach to Treat Bulky Metastases Even From Cutaneous Squamous Cell Carcinoma: Its Rationale and a Look at the Reliability of the Linear-Quadratic Model to Explain Its Radiobiological Effects. Front Oncol 2022;12:1–15. https://doi.org/10.3389/fonc.2022.809279.

Transcriptomics B, Pantaleo E, Amoroso N, Lombardi A, Bellantuono L, Urso D, et al. A Machine Learning Approach to Parkinson ’ s Disease 2022:1–22.

Gambino G, Brighina F, Allegra M, Marrale M, Collura G, Gagliardo C, et al. Modulation of Human Motor Cortical Excitability and Plasticity by Opuntia Ficus Indica Fruit Consumption: Evidence from a Preliminary Study through Non-Invasive Brain Stimulation. Nutrients 2022;14. https://doi.org/10.3390/nu14224915.

Lombardi A, Diacono D, Amoroso N, Biecek P, Monaco A, Bellantuono L, et al. A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer’s Disease. Brain Informatics 2022;9. https://doi.org/10.1186/s40708-022-00165-5.

Quartuccio N, Marrale M, Laudicella R, Alongi P, Siracusa M, Sturiale L, et al. The role of PET radiomic features in prostate cancer: a systematic review. Clin Transl Imaging 2022;9:579–88. https://doi.org/10.1007/s40336-021-00436-x.

Lizzi F, Scapicchio C, Laruina F, Retico A, Fantacci ME. Convolutional Neural Networks for Breast Density Classification: Performance and Explanation Insights. Appl Sci 2022;12:148. https://doi.org/10.3390/app12010148.

Lombardi A, Tavares JMRS, Tangaro S. Editorial: Explainable Artificial Intelligence (XAI) in Systems Neuroscience. Front Syst Neurosci 2022;15. https://doi.org/10.3389/fnsys.2021.766980.

Cicolari D, Lizio D, Pedrotti P, Moioli MT, Lascialfari A, Mariani M, et al. A method for T1 and T2 relaxation times validation and harmonization as a support to MRI mapping. J Magn Reson 2022;334:107110. https://doi.org/10.1016/j.jmr.2021.107110.

Lizzi F, Brero F, Cabini RF, Fantacci ME, Piffer S, Postuma I, et al. Making data big for a deep-learning analysis: Aggregation of public COVID-19 datasets of lung computed tomography scans. Proc 10th Int Conf Data Sci Technol Appl DATA 2021 2022:316–21. https://doi.org/10.5220/0010584403160321.

Massa F, Chincarini A, Bauckneht M, Raffa S, Peira E, Arnaldi D, et al. Added value of semiquantitative analysis of brain FDG-PET for the differentiation between MCI-Lewy bodies and MCI due to Alzheimer’s disease. Eur J Nucl Med Mol Imaging 2022. https://doi.org/10.1007/s00259-021-05568-w.

Bianchini L, Santinha J, Loução N, Figueiredo M, Botta F, Origgi D, et al. A multicenter study on radiomic features from T 2 ‐weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics. Magn Reson Med 2022;85:1713–26. https://doi.org/10.1002/mrm.28521.

Monaco A, Pantaleo E, Amoroso N, Bellantuono L, Lombardi A, Tateo A, et al. Identifying potential gene biomarkers for Parkinson’s disease through an information entropy based approach. Phys Biol 2022;18:1–4. https://doi.org/10.1088/1478-3975/abc09a.

Monaco A, Pantaleo E, Amoroso N, Lacalamita A, Lo Giudice C, Fonzino A, et al. A primer on machine learning techniques for genomic applications. Comput Struct Biotechnol J 2022;19:4345–59. https://doi.org/10.1016/j.csbj.2021.07.021.

Lizzi F, Agosti A, Brero F, Cabini RF, Fantacci ME, Figini S, et al. Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade of two U-nets: training and assessment on multiple datasets using different annotation criteria. Int J Comput Assist Radiol Surg 2022;17:229–37. https://doi.org/10.1007/s11548-021-02501-2.

Bauckneht M, Chincarini A, Brendel M, Rominger A, Beyer L, Bruffaerts R, et al. Associations among education, age, and the dementia with Lewy bodies (DLB) metabolic pattern: A European‐DLB consortium project. Alzheimer’s Dement 2022;17:1277–86. https://doi.org/10.1002/alz.12294.

Corradini D, Brizi L, Gaudiano C, Bianchi L, Marcelli E, Golfieri R, et al. Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data. Cancers (Basel) 2022;13:3944. https://doi.org/10.3390/cancers13163944.

Monaco A, Lacalamita A, Amoroso N, D’orta A, Del Buono A, Di Tuoro F, et al. Random forests highlight the combined effect of environmental heavy metals exposure and genetic damages for cardiovascular diseases. Appl Sci 2022;11. https://doi.org/10.3390/app11188405.

Arnaldi D, Chincarini A, De Carli F, Famà F, Girtler N, Brugnolo A, et al. The fate of patients with REM sleep behavior disorder and mild cognitive impairment. Sleep Med 2022;79:205–10. https://doi.org/10.1016/j.sleep.2020.02.011.

Massafra R, Latorre A, Fanizzi A, Bellotti R, Didonna V, Giotta F, et al. A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results. Front Oncol 2022;11:1–13. https://doi.org/10.3389/fonc.2021.576007.

Lombardi A, Monaco A, Donvito G, Amoroso N, Bellotti R, Tangaro S. Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019. Front Psychiatry 2022;11:1–15. https://doi.org/10.3389/fpsyt.2020.619629.

Mengucci C, Remondini D, Castellani G, Giampieri E. WISDoM: Characterizing Neurological Time Series With the Wishart Distribution. Front Neuroinform 2022;14:1–8. https://doi.org/10.3389/fninf.2020.611762.

Stefano A, Comelli A. Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images. J Imaging 2022;7:131. https://doi.org/10.3390/jimaging7080131.

Arnaldi D, Chincarini A, Hu MT, Sonka K, Boeve B, Miyamoto T, et al. Dopaminergic imaging and clinical predictors for phenoconversion of REM sleep behaviour disorder. Brain 2022;144:278–87. https://doi.org/10.1093/brain/awaa365.

Lombardi A, Diacono D, Amoroso N, Monaco A, Tavares JMRS, Bellotti R, et al. Explainable Deep Learning for Personalized Age Prediction With Brain Morphology. Front Neurosci 2022;15. https://doi.org/10.3389/fnins.2021.674055.

Caminiti SP, Sala A, Presotto L, Chincarini A, Sestini S, Perani D, et al. Validation of FDG-PET datasets of normal controls for the extraction of SPM-based brain metabolism maps. Eur J Nucl Med Mol Imaging 2022;48:2486–99. https://doi.org/10.1007/s00259-020-05175-1.

Lombardi A, Amoroso N, Monaco A, Tangaro S, Bellotti R. Complex network modelling of origin–destination commuting flows for the COVID-19 epidemic spread analysis in Italian Lombardy Region. Appl Sci 2022;11. https://doi.org/10.3390/app11104381.

Alongi P, Chiaravalloti A, Berti V, Vellani C, Trifirò G, Puccini G, et al. Amyloid PET in the diagnostic workup of neurodegenerative disease. Clin Transl Imaging 2022;9:383–97. https://doi.org/10.1007/s40336-021-00428-x.

Rigotto G, Montini B, Mattiolo A, Lazzari N, Piano MA, Remondini D, et al. Mechanisms Involved in the Promoting Activity of Fibroblasts in HTLV-1-Mediated Lymphomagenesis: Insights into the Plasticity of Lymphomatous Cells. Int J Mol Sci 2022;22:10562. https://doi.org/10.3390/ijms221910562.

Henriques RN, Correia MM, Marrale M, Huber E, Kruper J, Koudoro S, et al. Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project. Front Hum Neurosci 2022;15. https://doi.org/10.3389/fnhum.2021.675433.

Peira E, Grazzini M, Bauckneht M, Sensi F, Bosco P, Arnaldi D, et al. Probing the Role of a Regional Quantitative Assessment of Amyloid PET. J Alzheimers Dis 2022;80:383–96. https://doi.org/10.3233/JAD-201156.

Avanzo M, Porzio M, Lorenzon L, Milan L, Sghedoni R, Russo G, et al. Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy. Phys Medica 2022;83:221–41. https://doi.org/10.1016/j.ejmp.2021.04.010.

2021

Retico A, Avanzo M, Boccali T, Bonacorsi D, Botta F, Cuttone G, et al. Enhancing the impact of Artificial Intelligence in Medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure. Phys Medica 2021;91:140–50. https://doi.org/10.1016/j.ejmp.2021.10.005.

Lizzi F, Brero F, Cabini RF, Fantacci ME, Piffer S, Postuma I, et al. Making data big for a deep-learning analysis: Aggregation of public COVID-19 datasets of lung computed tomography scans. Proc 10th Int Conf Data Sci Technol Appl DATA 2021 2021:316–21. https://doi.org/10.5220/0010584403160321.

Stefano A, Comelli A. Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images. J Imaging 2021;7:131. https://doi.org/10.3390/jimaging7080131.

Lizzi F, Scapicchio C, Laruina F, Retico A, Fantacci ME. Convolutional Neural Networks for Breast Density Classification: Performance and Explanation Insights. Appl Sci 2021;12:148. https://doi.org/10.3390/app12010148.

Ubaldi L, Valenti V, Borgese RF, Collura G, Fantacci ME, Ferrera G, et al. Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples. Phys Medica 2021;90:13–22. https://doi.org/10.1016/j.ejmp.2021.08.015.

Ricciardi R, Mettivier G, Staffa M, Sarno A, Acampora G, Minelli S, et al. A deep learning classifier for digital breast tomosynthesis. Phys Medica 2021;83:184–93. https://doi.org/10.1016/j.ejmp.2021.03.021.

Henriques RN, Correia MM, Marrale M, Huber E, Kruper J, Koudoro S, et al. Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project. Front Hum Neurosci 2021;15. https://doi.org/10.3389/fnhum.2021.675433.

Corradini D, Brizi L, Gaudiano C, Bianchi L, Marcelli E, Golfieri R, et al. Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data. Cancers (Basel) 2021;13:3944. https://doi.org/10.3390/cancers13163944.

Bersanelli M, Travaglino E, Meggendorfer M, Matteuzzi T, Sala C, Mosca E, et al. Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes. J Clin Oncol 2021;39:1223–33. https://doi.org/10.1200/JCO.20.01659.

Barbieri M, Brizi L, Giampieri E, Solera F, Manners DN, Castellani G, et al. A deep learning approach for magnetic resonance fingerprinting: Scaling capabilities and good training practices investigated by simulations. Phys Medica 2021;89:80–92. https://doi.org/10.1016/j.ejmp.2021.07.013.

Zanghieri M, Menichetti G, Retico A, Calderoni S, Castellani G, Remondini D. Node Centrality Measures Identify Relevant Structural MRI Features of Subjects with Autism. Brain Sci 2021;11:498. https://doi.org/10.3390/brainsci11040498.

Arnaldi D, Chincarini A, Hu MT, Sonka K, Boeve B, Miyamoto T, et al. Dopaminergic imaging and clinical predictors for phenoconversion of REM sleep behaviour disorder. Brain 2021;144:278–87. https://doi.org/10.1093/brain/awaa365.

Lombardi A, Monaco A, Donvito G, Amoroso N, Bellotti R, Tangaro S. Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019. Front Psychiatry 2021;11:1–15. https://doi.org/10.3389/fpsyt.2020.619629.

Mengucci C, Remondini D, Castellani G, Giampieri E. WISDoM: Characterizing Neurological Time Series With the Wishart Distribution. Front Neuroinform 2021;14:1–8. https://doi.org/10.3389/fninf.2020.611762.

Monaco A, Pantaleo E, Amoroso N, Bellantuono L, Lombardi A, Tateo A, et al. Identifying potential gene biomarkers for Parkinson’s disease through an information entropy based approach. Phys Biol 2021;18:1–4. https://doi.org/10.1088/1478-3975/abc09a.

Piffer S, Casati M, Marrazzo L, Arilli C, Calusi S, Desideri I, et al. Validation of a secondary dose check tool against Monte Carlo and analytical clinical dose calculation algorithms in VMAT. J Appl Clin Med Phys 2021:1–11. https://doi.org/10.1002/acm2.13209.

2020

Casati M, Piffer S, Calusi S, Marrazzo L, Simontacchi G, Di Cataldo V, et al. Methodological approach to create an atlas using a commercial auto-contouring software. J Appl Clin Med Phys 2020;21:219–30. https://doi.org/10.1002/acm2.13093.

Chincarini A, Peira E, Corosu M, Morbelli S, Bauckneht M, Capitanio S, et al. A kinetics-based approach to amyloid PET semi-quantification. Eur J Nucl Med Mol Imaging 2020. https://doi.org/10.1007/s00259-020-04689-y.

Conti E, Retico A, Palumbo L, Spera G, Bosco P, Biagi L, et al. Autism spectrum disorder and childhood apraxia of speech: Early language-related hallmarks across structural mri study. J Pers Med 2020;10:1–19. https://doi.org/10.3390/jpm10040275.

Curzio O, Calderoni S, Maestro S, Rossi G, De Pasquale CF, Belmonti V, et al. Lower gray matter volumes of frontal lobes and insula in adolescents with anorexia nervosa restricting type: Findings from a Brain Morphometry Study. Eur Psychiatry 2020;63. https://doi.org/10.1192/j.eurpsy.2020.19.

Fanizzi A, Basile TMA, Losurdo L, Bellotti R, Bottigli U, Dentamaro R, et al. A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis. BMC Bioinformatics 2020;21:1–11. https://doi.org/10.1186/s12859-020-3358-4.

Ferrari E, Bosco P, Calderoni S, Oliva P, Palumbo L, Spera G, et al. Dealing with confounders and outliers in classification medical studies: The Autism Spectrum Disorders case study. Artif Intell Med 2020;108. https://doi.org/10.1016/j.artmed.2020.101926.

Ferrari E, Retico A, Bacciu D. Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI). Artif Intell Med 2020;103:101804. https://doi.org/10.1016/j.artmed.2020.101804.

Gagliardo C, Marrale M, D’Angelo C, Cannella R, Collura G, Iacopino G, et al. Transcranial Magnetic Resonance Imaging-Guided Focused Ultrasound Treatment at 1.5 T: A Retrospective Study on Treatment- and Patient-Related Parameters Obtained From 52 Procedures. Front Phys 2020;7:1–9. https://doi.org/10.3389/fphy.2019.00223.

Hoogman M, van Rooij D, Klein M, Boedhoe P, Ilioska I, Li T, et al. Consortium neuroscience of attention deficit/hyperactivity disorder and autism spectrum disorder: The ENIGMA adventure. Hum Brain Mapp 2020:1–19. https://doi.org/10.1002/hbm.25029.

Huber M, Beyer L, Prix C, Schönecker S, Palleis C, Rauchmann BS, et al. Metabolic Correlates of Dopaminergic Loss in Dementia with Lewy Bodies. Mov Disord 2020;35:595–605. https://doi.org/10.1002/mds.27945.

La Forgia D, Fanizzi A, Campobasso F, Bellotti R, Didonna V, Lorusso V, et al. Radiomic analysis in contrast-enhanced spectral mammography for predicting breast cancer histological outcome. Diagnostics 2020;10. https://doi.org/10.3390/diagnostics10090708.

Lella E, Lombardi A, Amoroso N, Diacono D, Maggipinto T, Monaco A, et al. Machine learning and DWI brain communicability networks for Alzheimer’s disease detection. Appl Sci 2020;10. https://doi.org/10.3390/app10030934.

Lombardi A, Amoroso N, Diacono D, Monaco A, Logroscino G, De Blasi R, et al. Association between structural connectivity and generalized cognitive spectrum in alzheimer’s disease. Brain Sci 2020;10:1–17. https://doi.org/10.3390/brainsci10110879.

Lombardi A, Amoroso N, Diacono D, Monaco A, Tangaro S, Bellotti R. Extensive evaluation of morphological statistical harmonization for brain age prediction. Brain Sci 2020;10:1–12. https://doi.org/10.3390/brainsci10060364.

Lombardi A, Amoroso N, Diacono D, Monaco A, Tangaro S, Bellotti R. Individual topological analysis of synchronization-based brain connectivity. Appl Sci 2020;10. https://doi.org/10.3390/app10093275.

Morbelli S, Arnaldi D, Cella E, Raffa S, Donegani MI, Capitanio S, et al. Striatal dopamine transporter SPECT quantification: head-to-head comparison between two three-dimensional automatic tools. EJNMMI Res 2020;10. https://doi.org/10.1186/s13550-020-00727-w.

Orso B, Arnaldi D, Famà F, Girtler N, Brugnolo A, Doglione E, et al. Anatomical and neurochemical bases of theory of mind in de novo Parkinson’s Disease. Cortex 2020;130:401–12. https://doi.org/10.1016/j.cortex.2020.06.012.

Piccoli T, Maniaci G, Collura G, Gagliardo C, Brancato A, La Tona G, et al. Increased functional connectivity in gambling disorder correlates with behavioural and emotional dysregulation: Evidence of a role for the cerebellum. Behav Brain Res 2020;390:112668. https://doi.org/10.1016/j.bbr.2020.112668.

2019

Talamonti, C; Piffer, S; Greto, D; Mangoni, M; Ciccarone, A; Dicarolo, P; Fantacci, ME; Fusi, F; Oliva, P; Palumbo, L; Favre, C; Livi, L; Pallotta, S; Retico, A, Radiomic and dosiomic profiling of paediatric medulloblastoma tumours treated with intensity modulated radiation therapy, Communications in Computer and Information Science,56-64,2019, 10.1007/978-3-030-29930-9_6

Lizzi, F; Laruina, F; Oliva, P; Retico, A; Fantacci, ME, Residual convolutional neural networks to automatically extract significant breast density features, Communications in Computer and Information Science,28-35,2019,10.1007/978-3-030-29930-9_3

Lella, E; Amoroso, N; Lombardi, A; Maggipinto, T; Tangaro, S; Bellotti, R et al ,Communicability disruption in Alzheimer’s disease connectivity networks,J COMPLEX NETW, 83-100, 2019, 10.1093/comnet/cny009

Liguori, M; Nuzziello, N; Simone, M; Amoroso, N; Viterbo, RG; Tangaro, S; Consiglio, A; Giordano, P; Bellotti, R; Trojano, M,Association between miRNAs expression and cognitive performances of Pediatric Multiple Sclerosis patients: A pilot study,BRAIN BEHAV,-,2019,10.1002/brb3.1199

Bosco, P; Giuliano, A; Delafield-Butt, J; Muratori, F; Calderoni, S; Retico, A,Brainstem enlargement in preschool children with autism: Results from an intermethod agreement study of segmentation algorithms,HUM BRAIN MAPP,Jul-19,2019,10.1002/hbm.24351

Brugnolo, A; De Carli, F; Pagani, M; Morbelli, S; Jonsson, C; Chincarini, A; Frisoni, GB; Galluzzi, S; Perneczky, R; Drzezga, A; van Berckel, BNM; Ossenkoppele, R; Didic, M; Guedj, E; Arnaldi, D; Massa, F; Grazzini, M; Pardini, M; Mecocci, P; Dottorini, ME; Bauckneht, M; Sambuceti, G; Nobili, F,Head-to-Head Comparison among Semi-Quantification Tools of Brain FDG-PET to Aid the Diagnosis of Prodromal Alzheimer’s Disease,J ALZHEIMERS DIS,383-394,2019,10.3233/JAD-181022

Bacalini, MG; Franceschi, C; Gentilini, D; Ravaioli, F; Zhou, XY; Remondini, D; Pirazzini, C; Giuliani, C; Marasco, E; Gensous, N; Di Blasio, AM; Ellis, E; Gramignoli, R; Castellani, G; Capri, M; Strom, S; Nardini, C; Cescon, M; Grazi, GL; Garagnani, P, Molecular Aging of Human Liver: An Epigenetic/Transcriptomic Signature,J GERONTOL A-BIOL,01-Aug,2019,10.1093/gerona/gly048

Cencini, M; Biagi, L; Kaggie, JD; Schulte, RF; Tosetti, M; Buonincontri, G,Magnetic resonance fingerprinting with dictionary-based fat and water separation (DBFW MRF): A multi-component approach,MAGN RESON MED,3032-3045,2019,10.1002/mrm.27628

Sestini, S; Alongi, P; Berti, V; Calcagni, ML; Cecchin, D; Chiaravalloti, A; Chincarini, A; Cistaro, A; Guerra, UP; Pappata, S; Tiraboschi, P; Nobili, F,The role of molecular imaging in the frame of the revised dementia with Lewy body criteria,CLIN TRANSL IMAGING,83-98,2019,10.1007/s40336-019-00321-8

Morbelli, S; Chincarini, A; Brendel, M; Rominger, A; Bruffaerts, R; Vandenberghe, R; Kramberger, MG; Trost, M; Garibotto, V; Nicastro, N; Frisoni, GB; Lemstra, AW; van der Zande, J; Pilotto, A; Padovani, A; Garcia-Ptacek, S; Savitcheva, I; Ochoa-Figueroa, MA; Davidsson, A; Camacho, V; Peira, E; Arnaldi, D; Bauckneht, M; Pardini, M; Sambuceti, G; Aarsland, D; Nobili, F, Metabolic patterns across core features in dementia with lewy bodies, ANN NEUROL,715-725,2019,10.1002/ana.25453

Nobili, F; Cagnin, A; Calcagni, ML; Chincarini, A; Guerra, UP; Morbelli, S; Padovani, A; Paghera, B; Pappata, S; Parnetti, L; Sestini, S; Schillaci, O,Emerging topics and practical aspects for an appropriate use of amyloid PET in the current Italian context,Q J NUCL MED MOL IM,83-92,2019,10.23736/S1824-4785.18.03069-8

Monaco, A; Sforza, G; Annoroso, N; Antonacci, M; Bellotti, R; de Toninnaso, M; Di Bitonto, P; Di Sciascio, E; Diacono, D; Gentile, E; Montemurno, A; Ruta, M; Ulloa, A; Tangaro, S,The PERSON project: a serious brain-computer interface game for treatment in cognitive impairment,HEALTH TECHNOL-GER,123-133,2019,10.1007/s12553-018-0258-y

Antonucci, LA; Di Carlo, P; Passiatore, R; Papalino, M; Monda, A; Amoroso, N; Tangaro, S; Taurisano, P; Rampino, A; Sambataro, F; Popolizio, T; Bertolino, A; Pergola, G; Blasi, G,Thalamic connectivity measured with fMRI is associated with a polygenic index predicting thalamo-prefrontal gene co-expression,BRAIN STRUCT FUNCT,1331-1344,2019,10.1007/s00429-019-01843-7

Lombardi, A; Guaragnella, C; Amoroso, N; Monaco, A; Fazio, L; Taurisano, P; Pergola, G; Blasi, G; Bertolino, A; Bellotti, R; Tangaro, S,Modelling cognitive loads in schizophrenia by means of new functional dynamic indexes,NEUROIMAGE,150-164,2019,10.1016/j.neuroimage.2019.03.055

Buonincontri, G; Biagi, L; Retico, A; Cecchi, P; Cosottini, M; Gallagher, FA; Gomez, PA; Graves, MJ; McLean, MA; Riemer, F; Schulte, RF; Tosetti, M; Zaccagna, F; Kaggie, JD,Multi-site repeatability and reproducibility of MR fingerprinting of the healthy brain at 1.5 and 3.0 T,NEUROIMAGE,362-372,2019,10.1016/j.neuroimage.2019.03.047

Amoroso, N; La Rocca, M; Bellantuono, L; Diacono, D; Fanizzi, A; Lella, E; Lombardi, A; Maggipinto, T; Monaco, A; Tangaro, S; Bellotti, R,Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age,FRONT AGING NEUROSCI,-,2019,10.3389/fnagi.2019.00115

Sestini, S; Alongi, P; Berti, V; Calcagni, ML; Cecchin, D; Chiaravalloti, A; Chincarini, A; Cistaro, A; Guerra, UP; Pappata, S; Tiraboschi, P; Nobili, F,The role of molecular imaging in the frame of the revised dementia with Lewy body criteria (vol 7, pg 83, 2019),CLIN TRANSL IMAGING,231-231,2019,10.1007/s40336-019-00324-5

Andrisani, A; Mininni, RM; Mazzia, F; Settanni, G; Iurino, A; Tangaro, S; Tateo, A; Bellotti, R,APPLICATIONS OF PDES INPAINTING TO MAGNETIC PARTICLE IMAGING AND CORNEAL TOPOGRAPHY,OPUSC MATH,453-482,2019,10.7494/OpMath.2019.39.4.453

Lella, E; Amoroso, N; Diacono, D; Lombardi, A; Maggipinto, T; Monaco, A; Bellotti, R; Tangaro, S,Communicability Characterization of Structural DWI Subcortical Networks in Alzheimer’s Disease,ENTROPY-SWITZ,-,2019,10.3390/e21050475

Fanizzi, A; Losurdo, L; Basile, TMA; Bellotti, R; Bottigli, U; Delogu, P; Diacono, D; Didonna, V; Fausto, A; Lombardi, A; Lorusso, V; Massafra, R; Tangaro, S; La Forgia, D,Fully Automated Support System for Diagnosis of Breast Cancer in Contrast-Enhanced Spectral Mammography Images,J CLIN MED,-,2019,10.3390/jcm8060891