1
|
Torregiani C, Baratella E, Segalotti A, Ruaro B, Salton F, Confalonieri P, Tavano S, Lapadula G, Bozzi C, Confalonieri M, Dellaca’ RL, Veneroni C. Oscillometry Longitudinal Data on COVID-19 Acute Respiratory Syndrome Treated with Non-Invasive Respiratory Support. J Clin Med 2024; 13:1868. [PMID: 38610633 PMCID: PMC11012861 DOI: 10.3390/jcm13071868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 03/02/2024] [Accepted: 03/16/2024] [Indexed: 04/14/2024] Open
Abstract
Background: Oscillometry allows for the non-invasive measurements of lung mechanics. In COVID-19 ARDS patients treated with Non-Invasive Oxygen Support (NI-OS), we aimed to (1) observe lung mechanics at the patients' admission and their subsequent changes, (2) compare lung mechanics with clinical and imaging data, and (3) evaluate whether lung mechanics helps to predict clinical outcomes. Methods: We retrospectively analyzed the data from 37 consecutive patients with moderate-severe COVID-19 ARDS. Oscillometry was performed on their 1st, 4th, and 7th day of hospitalization. Resistance (R5), reactance (X5), within-breath reactance changes (ΔX5), and the frequency dependence of the resistance (R5-R19) were considered. Twenty-seven patients underwent computed tomographic pulmonary angiography (CTPA): collapsed, poorly aerated, and normally inflated areas were quantified. Adverse outcomes were defined as intubation or death. Results: Thirty-two patients were included in this study. At the first measurement, only 44% of them had an abnormal R5 or X5. In total, 23 patients had measurements performed on their 3rd day and 7 on their 7th day of hospitalization. In general, their R5, R5-R19, and ΔX decreased with time, while their X5 increased. Collapsed areas on the CTPA correlated with the X5 z-score (ρ = -0.38; p = 0.046), while poorly aerated areas did not. Seven patients had adverse outcomes but did not present different oscillometry parameters on their 1st day of hospitalization. Conclusions: Our study confirms the feasibility of oscillometry in critically ill patients with COVID-19 pneumonia undergoing NI-OS. The X5 z-scores indicates collapsed but not poorly aerated lung areas in COVID-19 pneumonia. Our data, which show a severe impairment of gas exchange despite normal reactance in most patients with COVID-19 ARDS, support the hypothesis of a composite COVID-19 ARDS physiopathology.
Collapse
Affiliation(s)
- Chiara Torregiani
- Pulmonology Unit, Department of Medical Surgical and Health Sciences, University of Trieste, Hospital of Cattinara, 34149 Trieste, Italy
| | - Elisa Baratella
- Radiology Unit, Department of Medical Surgical and Health Sciences, University Hospital of Cattinara, 34149 Trieste, Italy
| | - Antonio Segalotti
- Radiology Unit, Department of Medical Surgical and Health Sciences, University Hospital of Cattinara, 34149 Trieste, Italy
| | - Barbara Ruaro
- Pulmonology Unit, Department of Medical Surgical and Health Sciences, University of Trieste, Hospital of Cattinara, 34149 Trieste, Italy
| | - Francesco Salton
- Pulmonology Unit, Department of Medical Surgical and Health Sciences, University of Trieste, Hospital of Cattinara, 34149 Trieste, Italy
| | - Paola Confalonieri
- Pulmonology Unit, Department of Medical Surgical and Health Sciences, University of Trieste, Hospital of Cattinara, 34149 Trieste, Italy
| | - Stefano Tavano
- Pulmonology Unit, Department of Medical Surgical and Health Sciences, University of Trieste, Hospital of Cattinara, 34149 Trieste, Italy
| | - Giulia Lapadula
- Pulmonology Unit, Department of Medical Surgical and Health Sciences, University of Trieste, Hospital of Cattinara, 34149 Trieste, Italy
| | - Chiara Bozzi
- Pulmonology Unit, Department of Medical Surgical and Health Sciences, University of Trieste, Hospital of Cattinara, 34149 Trieste, Italy
| | - Marco Confalonieri
- Pulmonology Unit, Department of Medical Surgical and Health Sciences, University of Trieste, Hospital of Cattinara, 34149 Trieste, Italy
| | - Raffaele L. Dellaca’
- Department of Electronics, Information and Biomedical Engineering (DEIB), TechRes Lab, Politecnico di Milano University, 20122 Milano, Italy; (R.L.D.); (C.V.)
| | - Chiara Veneroni
- Department of Electronics, Information and Biomedical Engineering (DEIB), TechRes Lab, Politecnico di Milano University, 20122 Milano, Italy; (R.L.D.); (C.V.)
| |
Collapse
|
2
|
Granata V, Fusco R, Villanacci A, Grassi F, Grassi R, Di Stefano F, Petrone A, Fusco N, Ianniello S. Qualitative and semi-quantitative ultrasound assessment in delta and Omicron Covid-19 patients: data from high volume reference center. Infect Agent Cancer 2023; 18:34. [PMID: 37245026 DOI: 10.1186/s13027-023-00515-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 05/24/2023] [Indexed: 05/29/2023] Open
Abstract
OBJECTIVE to evaluate the efficacy of US, both qualitatively and semi-quantitatively, in the selection of treatment for the Covid-19 patient, using patient triage as the gold standard. METHODS Patients admitted to the Covid-19 clinic to be treated with monoclonal antibodies (mAb) or retroviral treatment and undergoing lung ultrasound (US) were selected from the radiological data set between December 2021 and May 2022 according to the following inclusion criteria: patients with proven Omicron variant and Delta Covid-19 infection; patients with known Covid-19 vaccination with at least two doses. Lung US (LUS) was performed by experienced radiologists. The presence, location, and distribution of abnormalities, such as B-lines, thickening or ruptures of the pleural line, consolidations, and air bronchograms, were evaluated. The anomalous findings in each scan were classified according to the LUS scoring system. Nonparametric statistical tests were performed. RESULTS The LUS score median value in the patients with Omicron variant was 1.5 (1-20) while the LUS score median value in the patients with Delta variant was 7 (3-24). A difference statistically significant was observed for LUS score values among the patients with Delta variant between the two US examinations (p value = 0.045 at Kruskal Wallis test). There was a difference in median LUS score values between hospitalized and non-hospitalized patients for both the Omicron and Delta groups (p value = 0.02 on the Kruskal Wallis test). For Delta patients groups the sensitivity, specificity, positive and negative predictive values, considering a value of 14 for LUS score for the hospitalization, were of 85.29%, 44.44%, 85.29% and 76.74% respectively. CONCLUSIONS LUS is an interesting diagnostic tool in the context of Covid-19, it could allow to identify the typical pattern of diffuse interstitial pulmonary syndrome and could guide the correct management of patients.
Collapse
Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | | | - Alberta Villanacci
- Department of Radiology and Diagnostic Imaging, National Institute for Infectious Diseases IRCCS Lazzaro Spallanzani, 00149, Rome, Italy
| | - Francesca Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122, Milan, Italy
| | - Roberta Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122, Milan, Italy
| | - Federica Di Stefano
- Department of Radiology and Diagnostic Imaging, National Institute for Infectious Diseases IRCCS Lazzaro Spallanzani, 00149, Rome, Italy
| | - Ada Petrone
- Department of Radiology and Diagnostic Imaging, National Institute for Infectious Diseases IRCCS Lazzaro Spallanzani, 00149, Rome, Italy
| | - Nicoletta Fusco
- Department of Radiology and Diagnostic Imaging, National Institute for Infectious Diseases IRCCS Lazzaro Spallanzani, 00149, Rome, Italy
| | - Stefania Ianniello
- Department of Radiology and Diagnostic Imaging, National Institute for Infectious Diseases IRCCS Lazzaro Spallanzani, 00149, Rome, Italy
| |
Collapse
|
3
|
Grassi F, Granata V, Fusco R, De Muzio F, Cutolo C, Gabelloni M, Borgheresi A, Danti G, Picone C, Giovagnoni A, Miele V, Gandolfo N, Barile A, Nardone V, Grassi R. Radiation Recall Pneumonitis: The Open Challenge in Differential Diagnosis of Pneumonia Induced by Oncological Treatments. J Clin Med 2023; 12:jcm12041442. [PMID: 36835977 PMCID: PMC9964719 DOI: 10.3390/jcm12041442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 02/17/2023] Open
Abstract
The treatment of primary and secondary lung neoplasms now sees the fundamental role of radiotherapy, associated with surgery and systemic therapies. The improvement in survival outcomes has also increased attention to the quality of life, treatment compliance and the management of side effects. The role of imaging is not only limited to recognizing the efficacy of treatment but also to identifying, as soon as possible, the uncommon effects, especially when more treatments, such as chemotherapy, immunotherapy and radiotherapy, are associated. Radiation recall pneumonitis is an uncommon treatment complication that should be correctly characterized, and it is essential to recognize the mechanisms of radiation recall pneumonitis pathogenesis and diagnostic features in order to promptly identify them and adopt the best therapeutic strategy, with the shortest possible withdrawal of the current oncological drug. In this setting, artificial intelligence could have a critical role, although a larger patient data set is required.
Collapse
Affiliation(s)
- Francesca Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80127 Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
- Correspondence:
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80015 Naples, Italy
| | - Federica De Muzio
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Salerno, Italy
| | - Michela Gabelloni
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, 56126 Pisa, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica Delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Carmine Picone
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica Delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149 Genoa, Italy
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Valerio Nardone
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80127 Naples, Italy
| | - Roberta Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80127 Naples, Italy
| |
Collapse
|
4
|
Giannessi C, Smorchkova O, Cozzi D, Zantonelli G, Bertelli E, Moroni C, Cavigli E, Miele V. Behçet's Disease: A Radiological Review of Vascular and Parenchymal Pulmonary Involvement. Diagnostics (Basel) 2022; 12:2868. [PMID: 36428928 PMCID: PMC9689730 DOI: 10.3390/diagnostics12112868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/13/2022] [Accepted: 11/15/2022] [Indexed: 11/22/2022] Open
Abstract
Behcet's disease (BD) is a chronic systemic inflammatory disorder characterized by underlying chronic vasculitis of both large- and small-caliber vessels. Thoracic involvement in BD can occur with various types of manifestations, which can be detected with contrast-enhanced MSCT scanning. In addition, MR can be useful in diagnosis. Characteristic features are aneurysms of the pulmonary arteries that can cause severe hemoptysis and SVC thrombosis that manifests as SVC syndrome. Other manifestations are aortic and bronchial artery aneurysms, alveolar hemorrhage, pulmonary infarction, and rarely pleural effusion. Achieving the right diagnosis of these manifestations is important for setting the correct therapy and improving the patient's outcome.
Collapse
Affiliation(s)
| | | | - Diletta Cozzi
- Department of Emergency Radiology, Careggi University Hospital, 50134 Florence, Italy
| | | | | | | | | | | |
Collapse
|
5
|
Granata V, Fusco R, Villanacci A, Magliocchetti S, Urraro F, Tetaj N, Marchioni L, Albarello F, Campioni P, Cristofaro M, Di Stefano F, Fusco N, Petrone A, Schininà V, Grassi F, Girardi E, Ianniello S. Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center. J Pers Med 2022; 12:955. [PMID: 35743740 PMCID: PMC9224665 DOI: 10.3390/jpm12060955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 06/03/2022] [Accepted: 06/09/2022] [Indexed: 11/17/2022] Open
Abstract
Purpose: To analyze the vaccine effect by comparing five groups: unvaccinated patients with Alpha variant, unvaccinated patients with Delta variant, vaccinated patients with Delta variant, unvaccinated patients with Omicron variant, and vaccinated patients with Omicron variant, assessing the “gravity” of COVID-19 pulmonary involvement, based on CT findings in critically ill patients admitted to Intensive Care Unit (ICU). Methods: Patients were selected by ICU database considering the period from December 2021 to 23 March 2022, according to the following inclusion criteria: patients with proven Omicron variant COVID-19 infection with known COVID-19 vaccination with at least two doses and with chest Computed Tomography (CT) study during ICU hospitalization. Wee also evaluated the ICU database considering the period from March 2020 to December 2021, to select unvaccinated consecutive patients with Alpha variant, subjected to CT study, consecutive unvaccinated and vaccinated patients with Delta variant, subjected to CT study, and, consecutive unvaccinated patients with Omicron variant, subjected to CT study. CT images were evaluated qualitatively using a severity score scale of 5 levels (none involvement, mild: ≤25% of involvement, moderate: 26−50% of involvement, severe: 51−75% of involvement, and critical involvement: 76−100%) and quantitatively, using the Philips IntelliSpace Portal clinical application CT COPD computer tool. For each patient the lung volumetry was performed identifying the percentage value of aerated residual lung volume. Non-parametric tests for continuous and categorical variables were performed to assess statistically significant differences among groups. Results: The patient study group was composed of 13 vaccinated patients affected by the Omicron variant (Omicron V). As control groups we identified: 20 unvaccinated patients with Alpha variant (Alpha NV); 20 unvaccinated patients with Delta variant (Delta NV); 18 vaccinated patients with Delta variant (Delta V); and 20 unvaccinated patients affected by the Omicron variant (Omicron NV). No differences between the groups under examination were found (p value > 0.05 at Chi square test) in terms of risk factors (age, cardiovascular diseases, diabetes, immunosuppression, chronic kidney, cardiac, pulmonary, neurologic, and liver disease, etc.). A different median value of aerated residual lung volume was observed in the Delta variant groups: median value of aerated residual lung volume was 46.70% in unvaccinated patients compared to 67.10% in vaccinated patients. In addition, in patients with Delta variant every other extracted volume by automatic tool showed a statistically significant difference between vaccinated and unvaccinated group. Statistically significant differences were observed for each extracted volume by automatic tool between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant of COVID-19. Good statistically significant correlations among volumes extracted by automatic tool for each lung lobe and overall radiological severity score were obtained (ICC range 0.71−0.86). GGO was the main sign of COVID-19 lesions on CT images found in 87 of the 91 (95.6%) patients. No statistically significant differences were observed in CT findings (ground glass opacities (GGO), consolidation or crazy paving sign) among patient groups. Conclusion: In our study, we showed that in critically ill patients no difference were observed in terms of severity of disease or exitus, between unvaccinated and vaccinated patients. The only statistically significant differences were observed, with regard to the severity of COVID-19 pulmonary parenchymal involvement, between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant, and between unvaccinated patients with Delta variant and vaccinated patients with Delta variant.
Collapse
Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
| | - Alberta Villanacci
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Simona Magliocchetti
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy; (S.M.); (F.U.); (F.G.)
| | - Fabrizio Urraro
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy; (S.M.); (F.U.); (F.G.)
| | - Nardi Tetaj
- Intensive Care Unit, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (N.T.); (L.M.)
| | - Luisa Marchioni
- Intensive Care Unit, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (N.T.); (L.M.)
| | - Fabrizio Albarello
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Paolo Campioni
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Massimo Cristofaro
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Federica Di Stefano
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Nicoletta Fusco
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Ada Petrone
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Vincenzo Schininà
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Francesca Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy; (S.M.); (F.U.); (F.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122 Milan, Italy
| | - Enrico Girardi
- Department of Epidemiology and Research, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy;
| | - Stefania Ianniello
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| |
Collapse
|
6
|
Committeri U, Fusco R, Di Bernardo E, Abbate V, Salzano G, Maglitto F, Dell’Aversana Orabona G, Piombino P, Bonavolontà P, Arena A, Perri F, Maglione MG, Setola SV, Granata V, Iaconetta G, Ionna F, Petrillo A, Califano L. Radiomics Metrics Combined with Clinical Data in the Surgical Management of Early-Stage (cT1-T2 N0) Tongue Squamous Cell Carcinomas: A Preliminary Study. BIOLOGY 2022; 11:biology11030468. [PMID: 35336841 PMCID: PMC8945467 DOI: 10.3390/biology11030468] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/08/2022] [Accepted: 03/16/2022] [Indexed: 11/16/2022]
Abstract
Objective: To predict the risk of metastatic lymph nodes and the tumor grading related to oral tongue squamous cell carcinoma (OTSCC) through the combination of clinical data with radiomics metrics by computed tomography, and to develop a supportive approach in the management of the lymphatic cervical areas, with particular attention to the early stages (T1−T2). Between March 2016 and February 2020, patients with histologically confirmed OTSCC, treated by partial glossectomy and ipsilateral laterocervical lymphadenectomy and subjected to computed tomography (CT) before surgery, were identified by two centers: 81 patients (49 female and 32 male) with 58 years as the median age (range 19−86 years). Univariate analysis with non-parametric tests and multivariate analysis with machine learning approaches were used. Clinical, hematological parameters and radiological features extracted by CT were considered individually and in combination. All clinical parameters showed statistically significant differences (p < 0.05) for the Kruskal−Wallis test when discriminating both the tumor grading and the metastatic lymph nodes. DOI, PLR, SII, and SIRI showed an accuracy of 0.70 (ROC analysis) when identifying the tumor grading, while an accuracy ≥ 0.78 was shown by DOI, NLR, PLR, SII, and SIRI when discriminating metastatic lymph nodes. In the context of the analysis of radiomics metrics, the original_glszm_HighGrayLevelZoneEmphasis feature was selected for identifying the tumor grading (accuracy of 0.70), while the wavelet_HHH_glrlm_LowGrayLevelRunEmphasis predictor was selected for determining metastatic lymph nodes (accuracy of 0.96). Remarkable findings were also obtained when classifying patients with a machine learning approach. Radiomics features alone can predict tumor grading with an accuracy of 0.76 using a logistic regression model, while an accuracy of 0.82 can be obtained by running a CART algorithm through a combination of three clinical parameters (SIRI, DOI, and PLR) with a radiomics feature (wavelet_LLL_glszm_SizeZoneNonUniformityNormalized). In the context of predicting metastatic lymph nodes, an accuracy of 0.94 was obtained using 15 radiomics features in a logistic regression model, while both CART and CIDT achieved an asymptotic accuracy value of 1.00 using only one radiomics feature. Radiomics features and clinical parameters have an important role in identifying tumor grading and metastatic lymph nodes. Machine learning approaches can be used as an easy-to-use tool to stratify patients with early-stage OTSCC, based on the identification of metastatic and non-metastatic lymph nodes.
Collapse
Affiliation(s)
- Umberto Committeri
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy; (R.F.); (E.D.B.)
| | - Elio Di Bernardo
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy; (R.F.); (E.D.B.)
| | - Vincenzo Abbate
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Giovanni Salzano
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Fabio Maglitto
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Giovanni Dell’Aversana Orabona
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Pasquale Piombino
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Paola Bonavolontà
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Antonio Arena
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Francesco Perri
- Head and Neck Medical Oncology Unit, Istituto Nazionale dei Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy;
| | - Maria Grazia Maglione
- Division of Surgical Oncology Maxillo-Facial Unit, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, Via Mariano Semmola, 80131 Naples, Italy; (M.G.M.); (F.I.)
| | - Sergio Venanzio Setola
- Divisions of Radiology, Istituto Nazionale dei Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy; (S.V.S.); (V.G.)
| | - Vincenza Granata
- Divisions of Radiology, Istituto Nazionale dei Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy; (S.V.S.); (V.G.)
| | - Giorgio Iaconetta
- Department of Neurosurgery, University of Salerno, 84084 Salerno, Italy;
| | - Franco Ionna
- Division of Surgical Oncology Maxillo-Facial Unit, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, Via Mariano Semmola, 80131 Naples, Italy; (M.G.M.); (F.I.)
| | - Antonella Petrillo
- Divisions of Radiology, Istituto Nazionale dei Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy; (S.V.S.); (V.G.)
- Correspondence:
| | - Luigi Califano
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| |
Collapse
|
7
|
Fusco R, Grassi R, Granata V, Setola SV, Grassi F, Cozzi D, Pecori B, Izzo F, Petrillo A. Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment. J Pers Med 2021; 11:993. [PMID: 34683133 PMCID: PMC8540782 DOI: 10.3390/jpm11100993] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/22/2021] [Accepted: 09/28/2021] [Indexed: 12/26/2022] Open
Abstract
OBJECTIVE To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified. METHODS Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC). RESULTS Twenty-two studies met the inclusion criteria: 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4-99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0-100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0-99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0-100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05). CONCLUSIONS Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.
Collapse
Affiliation(s)
- Roberta Fusco
- IGEA SpA Medical Division—Oncology, Via Casarea 65, Casalnuovo di Napoli, 80013 Naples, Italy;
| | - Roberta Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy; (R.G.); (F.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
| | - Francesca Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy; (R.G.); (F.G.)
| | - Diletta Cozzi
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy;
| | - Biagio Pecori
- Division of Radiotherapy and Innovative Technologies, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Francesco Izzo
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
| |
Collapse
|
8
|
Evolution of CT Findings and Lung Residue in Patients with COVID-19 Pneumonia: Quantitative Analysis of the Disease with a Computer Automatic Tool. J Pers Med 2021; 11:jpm11070641. [PMID: 34357108 PMCID: PMC8305822 DOI: 10.3390/jpm11070641] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/20/2021] [Accepted: 07/03/2021] [Indexed: 02/06/2023] Open
Abstract
Purpose: the purpose of this study was to assess the evolution of computed tomography (CT) findings and lung residue in patients with COVID-19 pneumonia, via quantified evaluation of the disease, using a computer aided tool. Materials and methods: we retrospectively evaluated 341 CT examinations of 140 patients (68 years of median age) infected with COVID-19 (confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR)), who were hospitalized, and who received clinical and CT examinations. All CTs were evaluated by two expert radiologists, in consensus, at the same reading session, using a computer-aided tool for quantification of the pulmonary disease. The parameters obtained using the computer tool included the healthy residual parenchyma, ground glass opacity, consolidation, and total lung volume. Results: statistically significant differences (p value ≤ 0.05) were found among quantified volumes of healthy residual parenchyma, ground glass opacity (GGO), consolidation, and total lung volume, considering different clinical conditions (stable, improved, and worsened). Statistically significant differences were found among quantified volumes for healthy residual parenchyma, GGO, and consolidation (p value ≤ 0.05) between dead patients and discharged patients. CT was not performed on cadavers; the death was an outcome, which was retrospectively included to differentiate findings of patients who survived vs. patients who died during hospitalization. Among discharged patients, complete disease resolutions on CT scans were observed in 62/129 patients with lung disease involvement ≤5%; lung disease involvement from 5% to 15% was found in 40/129 patients, while 27/129 patients had lung disease involvement between 16 and 30%. Moreover, 8–21 days (after hospital admission) was an “advanced period” with the most severe lung disease involvement. After the extent of involvement started to decrease—particularly after 21 days—the absorption was more obvious. Conclusions: a complete disease resolution on chest CT scans was observed in 48.1% of discharged patients using a computer-aided tool to quantify the GGO and consolidation volumes; after 16 days of hospital admission, the abnormalities identified by chest CT began to improve; in particular, the absorption was more obvious after 21 days.
Collapse
|
9
|
Maio F, Tari DU, Granata V, Fusco R, Grassi R, Petrillo A, Pinto F. Breast Cancer Screening during COVID-19 Emergency: Patients and Department Management in a Local Experience. J Pers Med 2021; 11:380. [PMID: 34066425 PMCID: PMC8148132 DOI: 10.3390/jpm11050380] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/30/2021] [Accepted: 05/04/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND During the COVID-19 public health emergency, our breast cancer screening activities have been interrupted. In June 2020, they resumed, calling for mandatory safe procedures to properly manage patients and staff. METHODS A protocol supporting medical activities in breast cancer screening was created, based on six relevant articles published in the literature and in the following National and International guidelines for COVID-19 prevention. The patient population, consisting of both screening and breast ambulatory patients, was classified into one of four categories: 1. Non-COVID-19 patient; 2. Confirmed COVID-19 in an asymptomatic screening patient; 3. suspected COVID-19 in symptomatic or confirmed breast cancer; 4. Confirmed COVID-19 in symptomatic or confirmed breast cancer. The day before the radiological exam, patients are screened for COVID-19 infection through a telephone questionnaire. At a subsequent in person appointment, the body temperature is checked and depending on the clinical scenario at stake, the scenario-specific procedures for medical and paramedical staff are adopted. RESULTS In total, 203 mammograms, 76 breast ultrasound exams, 4 core needle biopsies, and 6 vacuum-assisted breast biopsies were performed in one month. Neither medical nor paramedical staff were infected on any of these occasions. CONCLUSION Our department organization model can represent a case of implementation of National and International guidelines applied in a breast cancer screening program, assisting hospital personnel into COVID-19 infection prevention.
Collapse
Affiliation(s)
- Francesca Maio
- Department of Radiology, Marcianise Hospital, Caserta Local Health Authority, Viale Sossietta Scialla, 81025 Marcianise, Italy; (F.M.); (F.P.)
| | - Daniele Ugo Tari
- Department of Breast Radiology, Caserta Local Health Authority Dictrict 12, Viale Paul Harris 79, 81100 Caserta, Italy;
| | - Vincenza Granata
- Department of Radiology, Istituto Nazionale Tumori IRCCS Fondazione G.Pascale di Napoli, Via Mariano Semmola 53, 80131 Naples, Italy; (V.G.); (R.F.)
| | - Roberta Fusco
- Department of Radiology, Istituto Nazionale Tumori IRCCS Fondazione G.Pascale di Napoli, Via Mariano Semmola 53, 80131 Naples, Italy; (V.G.); (R.F.)
| | - Roberta Grassi
- Department of Radiology, Università degli Studi della Campania “Luigi Vanvitelli”, Piazza Miraglia, 80138 Naples, Italy;
| | - Antonella Petrillo
- Department of Radiology, Istituto Nazionale Tumori IRCCS Fondazione G.Pascale di Napoli, Via Mariano Semmola 53, 80131 Naples, Italy; (V.G.); (R.F.)
| | - Fabio Pinto
- Department of Radiology, Marcianise Hospital, Caserta Local Health Authority, Viale Sossietta Scialla, 81025 Marcianise, Italy; (F.M.); (F.P.)
| |
Collapse
|