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Inchingolo R, Maino C, Cannella R, Vernuccio F, Cortese F, Dezio M, Pisani AR, Giandola T, Gatti M, Giannini V, Ippolito D, Faletti R. Radiomics in colorectal cancer patients. World J Gastroenterol 2023; 29:2888-2904. [PMID: 37274803 PMCID: PMC10237092 DOI: 10.3748/wjg.v29.i19.2888] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/07/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
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Affiliation(s)
- Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Francesco Cortese
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Michele Dezio
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Antonio Rosario Pisani
- Interdisciplinary Department of Medicine, Section of Nuclear Medicine, University of Bari “Aldo Moro”, Bari 70121, Italy
| | - Teresa Giandola
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
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2
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Nardone V, Belfiore MP, De Chiara M, De Marco G, Patanè V, Balestrucci G, Buono M, Salvarezza M, Di Guida G, D'Angiolella D, Grassi R, D'Onofrio I, Cimmino G, Della Corte CM, Gambardella A, Morgillo F, Ciardiello F, Reginelli A, Cappabianca S. CARdioimaging in Lung Cancer PatiEnts Undergoing Radical RadioTherapy: CARE-RT Trial. Diagnostics (Basel) 2023; 13:diagnostics13101717. [PMID: 37238201 DOI: 10.3390/diagnostics13101717] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/06/2023] [Accepted: 04/12/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Non-small-cell lung cancer (NSCLC) is a common, steady growing lung tumour that is often discovered when a surgical approach is forbidden. For locally advanced inoperable NSCLC, the clinical approach consists of a combination of chemotherapy and radiotherapy, eventually followed by adjuvant immunotherapy, a treatment that is useful but may cause several mild and severe adverse effect. Chest radiotherapy, specifically, may affect the heart and coronary artery, impairing heart function and causing pathologic changes in myocardial tissues. The aim of this study is to evaluate the damage coming from these therapies with the aid of cardiac imaging. METHODS This is a single-centre, prospective clinical trial. Patients with NSCLC who are enrolled will undergo computed tomography (CT) and magnetic resonance imaging (MRI) before chemotherapy 3 months, 6 months, and 9-12 months after the treatment. We expect to enrol 30 patients in 2 years. CONCLUSIONS Our clinical trial will be an opportunity not only to highlight the timing and the radiation dose needed for pathological cardiac tissue changes to happen but will also provide useful data to set new follow-up schedules and strategies, keeping in mind that, more often than not, patients affected by NSCLC may present other heart- and lung-related pathological conditions.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Maria Paola Belfiore
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Marco De Chiara
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Giuseppina De Marco
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Vittorio Patanè
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Giovanni Balestrucci
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Mauro Buono
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Maria Salvarezza
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Gaetano Di Guida
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Domenico D'Angiolella
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Ida D'Onofrio
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
- Radiotherapy Unit, Ospedale del Mare, ASL Napoli 1 Centro, 80138 Naples, Italy
| | - Giovanni Cimmino
- Department of Translational Medical Science, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | | | - Antonio Gambardella
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Floriana Morgillo
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Fortunato Ciardiello
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
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Nardone V, Reginelli A, De Marco G, Natale G, Patanè V, De Chiara M, Buono M, Russo GM, Monti R, Balestrucci G, Salvarezza M, Di Guida G, D’Ippolito E, Sangiovanni A, Grassi R, D’Onofrio I, Belfiore MP, Cimmino G, Della Corte CM, Vicidomini G, Fiorelli A, Gambardella A, Morgillo F, Cappabianca S. Role of Cardiac Biomarkers in Non-Small Cell Lung Cancer Patients. Diagnostics (Basel) 2023; 13:diagnostics13030400. [PMID: 36766506 PMCID: PMC9914841 DOI: 10.3390/diagnostics13030400] [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: 12/01/2022] [Revised: 01/17/2023] [Accepted: 01/18/2023] [Indexed: 01/24/2023] Open
Abstract
Treatment-induced cardiac toxicity represents an important issue in non-small cell lung cancer (NSCLC) patients, and no biomarkers are currently available in clinical practice. A novel and easy-to-calculate marker is the quantitative analysis of calcium plaque in the coronary, calculated on CT. It is called the Agatston score (or CAD score). At the same time, other potential predictors include cardiac ultrasonography and anamnesis of the patients. Our work aimed to correlate cardiac biomarkers with overall survival (OS) in NSCLC patients. We retrospectively analyzed patients with NSCLC discussed in the Multidisciplinary Tumor Board of our Institute for the present analysis between January 2018 and July 2022. Inclusion criteria were the availability of basal CT imaging of the thorax, cardiac ultrasonography with the calculation of ejection fraction (EF), and complete anamnesis, including assessment of co-pathologies and pharmacological drugs. The clinical data of the patients were retrospectively collected, and the CAD scores was calculated on a CT scan. All of these parameters were correlated with overall survival (OS) with univariate analysis (Kaplan-Meier analysis) and multivariate analysis (Cox regression analysis). Following the above-mentioned inclusion criteria, 173 patients were included in the present analysis. Of those, 120 patients died in the follow-up period (69.6%), and the median overall survival (OS) was 28 months (mean 47.2 months, 95% CI, 36-57 months). In univariate analysis, several parameters that significantly correlated with lower OS were the stage (p < 0.001), the CAD grading (p < 0.001), history of ischemic heart disease (p: 0.034), use of beta blocker drugs (p: 0.036), and cardiac ejection fraction (p: 0.005). In multivariate analysis, the only parameters that remained significant were as follows: CAD score (p: 0.014, OR 1.56, 95% CI: 1.04-1.83), stage (p: 0.016, OR: 1.26, 95% CI: 1.05-1.53), and cardiac ejection fraction (p: 0.011, OR 0.46, 95% CI: 0.25-0.84). Both CAD score and ejection fraction are correlated with survival in NSCLC patients at all stages of the disease. Independently from the treatment choice, a cardiological evaluation is mandatory for patients with NSCLC.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
- Correspondence:
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Giuseppina De Marco
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Giovanni Natale
- Department of Translational Medical Science, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Vittorio Patanè
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Marco De Chiara
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Mauro Buono
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Gaetano Maria Russo
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Riccardo Monti
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Giovanni Balestrucci
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Maria Salvarezza
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Gaetano Di Guida
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Emma D’Ippolito
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Angelo Sangiovanni
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Ida D’Onofrio
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
- Radiotherapy Unit, Ospedale del Mare, ASL Napoli 1 Centro, 80138 Naples, Italy
| | - Maria Paola Belfiore
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Giovanni Cimmino
- Department of Translational Medical Science, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | | | - Giovanni Vicidomini
- Department of Translational Medical Science, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Alfonso Fiorelli
- Department of Translational Medical Science, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Antonio Gambardella
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Floriana Morgillo
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
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Robustness of Radiomics in Pre-Surgical Computer Tomography of Non-Small-Cell Lung Cancer. J Pers Med 2022; 13:jpm13010083. [PMID: 36675744 PMCID: PMC9864775 DOI: 10.3390/jpm13010083] [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: 11/22/2022] [Accepted: 12/20/2022] [Indexed: 12/31/2022] Open
Abstract
Background: Radiomic features are increasingly used in CT of NSCLC. However, their robustness with respect to segmentation variability has not yet been demonstrated. The aim of this study was to assess radiomic features agreement across three kinds of segmentation. Methods: We retrospectively included 48 patients suffering from NSCLC who underwent pre-surgery CT. Two expert radiologists in consensus manually delineated three 3D-ROIs on each patient. To assess robustness for each feature, the intra-class correlation coefficient (ICC) across segmentations was evaluated. The ‘sensitivity’ of ICC upon some parameters affecting features computation (such as bin-width for first-order features and pixel-distances for second-order features) was also evaluated. Moreover, an assessment with respect to interpolator and isotropic resolution was also performed. Results: Our results indicate that ‘shape’ features tend to have excellent agreement (ICC > 0.9) across segmentations; moreover, they have approximately zero sensitivity to other parameters. ‘First-order’ features are in general sensitive to parameters variation; however, a few of them showed excellent agreement and low sensitivity (below 0.1) with respect to bin-width and pixel-distance. Similarly, a few second-order features showed excellent agreement and low sensitivity. Conclusions: Our results suggest that a limited number of radiomic features can achieve a high level of reproducibility in CT of NSCLC.
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Belfiore MP, Nardone V, D’Onofrio I, Salvia AAH, D’Ippolito E, Gallo L, Caliendo V, Gatta G, Fasano M, Grassi R, Angrisani A, Guida C, Reginelli A, Cappabianca S. Diffusion-weighted imaging and apparent diffusion coefficient mapping of head and neck lymph node metastasis: a systematic review. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2022; 3:734-745. [PMID: 36530194 PMCID: PMC9750825 DOI: 10.37349/etat.2022.00110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 08/19/2022] [Indexed: 11/17/2023] Open
Abstract
AIM Head and neck squamous cell cancer (HNSCC) is the ninth most common tumor worldwide. Neck lymph node (LN) status is the major indicator of prognosis in all head and neck cancers, and the early detection of LN involvement is crucial in terms of therapy and prognosis. Diffusion-weighted imaging (DWI) is a non- invasive imaging technique used in magnetic resonance imaging (MRI) to characterize tissues based on the displacement motion of water molecules. This review aims to provide an overview of the current literature concerning quantitative diffusion imaging for LN staging in patients with HNSCC. METHODS This systematic review performed a literature search on the PubMed database (https://pubmed.ncbi.nlm.nih.gov/) for all relevant, peer-reviewed literature on the subject following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) criteria, using the keywords: DWI, MRI, head and neck, staging, lymph node. RESULTS After excluding reviews, meta-analyses, case reports, and bibliometric studies, 18 relevant papers out of the 567 retrieved were selected for analysis. CONCLUSIONS DWI improves the diagnosis, treatment planning, treatment response evaluation, and overall management of patients affected by HNSCC. More robust data to clarify the role of apparent diffusion coefficient (ADC) and DWI parameters are needed to develop models for prognosis and prediction in HNSCC cancer using MRI.
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Affiliation(s)
- Maria Paola Belfiore
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Valerio Nardone
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Ida D’Onofrio
- Unit of Radiation Oncology, Ospedale del Mare, 80138 Naples, Italy
| | | | - Emma D’Ippolito
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Luigi Gallo
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Valentina Caliendo
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Gianluca Gatta
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Morena Fasano
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Antonio Angrisani
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Cesare Guida
- Unit of Radiation Oncology, Ospedale del Mare, 80138 Naples, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
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Duan L, Shan W, Bo G, Lu G, Guo L. Qualitative (and Quantitative) Values of the Lung-RADS and Computed Tomography in Diagnosing Solitary Pulmonary Nodules. Diagnostics (Basel) 2022; 12:diagnostics12112699. [PMID: 36359542 PMCID: PMC9689942 DOI: 10.3390/diagnostics12112699] [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: 10/06/2022] [Revised: 10/30/2022] [Accepted: 10/31/2022] [Indexed: 11/09/2022] Open
Abstract
Background: Lung-RADS classification and CT signs can both help in the differential diagnosis of SPNs. The purpose of this study was to investigate the diagnostic value of these two methods and the combination of the two methods for solitary pulmonary nodules (SPNs). Methods: A total of 296 cases of SPNs were retrospectively analyzed. All the SPNs were classified according to the Lung-RADS grading version 1.1. The scores of each lesion were calculated according to their CT signs. Imaging features, such as the size and margin of the lesions, pleural traction, spiculation, lobulation, bronchial cutoff, air bronchogram, vacuoles, tumor vasculature, and cavity signs, were analyzed. The imaging results were compared with the pathology examination findings. Receiver operating characteristic (ROC) curves were applied to compare the values of the different methods in differentially diagnosing benign and malignant SPNs. Results: The sensitivity, specificity, and accuracy of Lung-RADS grading for diagnosing SPNs were 34.0%, 94.4%, and 47.6%, respectively. The area under the ROC curve (AUC) was 0.600 (p < 0.001). The sensitivity, specificity, and accuracy of the CT sign scores were 56.3%, 70.0%, and 60.5%, respectively, and the AUC was 0.657 (p < 0.001). The sensitivity, specificity, and accuracy of the combination of the two methods for diagnosing SPNs were 93.2%, 61.1%, and 83.5%, and the AUC was 0.777 (p < 0.001). Conclusion: The combination of Lung-RADS classification and CT signs significantly improved the differential diagnosis of SPNs.
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Affiliation(s)
- Lizhen Duan
- Department of Medical Imaging, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an 223300, China
| | - Wenli Shan
- Department of Medical Imaging, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an 223300, China
| | - Genji Bo
- Department of Medical Imaging, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an 223300, China
| | - Guangming Lu
- Department of Medical Imaging, The Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
| | - Lili Guo
- Department of Medical Imaging, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an 223300, China
- Correspondence: ; Tel.: +86-13651549848
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Preliminary Report on Computed Tomography Radiomics Features as Biomarkers to Immunotherapy Selection in Lung Adenocarcinoma Patients. Cancers (Basel) 2021; 13:cancers13163992. [PMID: 34439148 PMCID: PMC8393664 DOI: 10.3390/cancers13163992] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 12/29/2022] Open
Abstract
PURPOSE To assess the efficacy of radiomics features obtained by computed tomography (CT) examination as biomarkers in order to select patients with lung adenocarcinoma who would benefit from immunotherapy. METHODS Seventy-four patients (median age 63 years, range 42-86 years) with histologically confirmed lung cancer who underwent immunotherapy as first- or second-line therapy and who had baseline CT studies were enrolled in this approved retrospective study. As a control group, we selected 50 patients (median age 66 years, range 36-86 years) from 2005 to 2013 with histologically confirmed lung adenocarcinoma who underwent chemotherapy alone or in combination with targeted therapy. A total of 573 radiomic metrics were extracted: 14 features based on Hounsfield unit values specific for lung CT images; 66 first-order profile features based on intensity values; 43 second-order profile features based on lesion shape; 393 third-order profile features; and 57 features with higher-order profiles. Univariate and multivariate statistical analysis with pattern recognition approaches and the least absolute shrinkage and selection operator (LASSO) method were used to assess the capability of extracted radiomics features to predict overall survival (OS) and progression free survival (PFS) time. RESULTS A total of 38 patients (median age 61; range 41-78 years) with confirmed lung adenocarcinoma and subjected to immunotherapy satisfied inclusion criteria, and 50 patients in a control group were included in the analysis The shift in the center of mass of the lesion due to image intensity was significant both to predict OS in patients subjected to immunotherapy and to predict PFS in patients subjected to immunotherapy and in patients in the control group. With univariate analysis, low diagnostic accuracy was reached to stratify patients based on OS and PFS time. Regarding multivariate analysis, considering the robust (two morphological features, three textural features and three higher-order statistical metrics) application of the LASSO approach and all patients, a support vector machine reached the best results for stratifying patients based on OS (area under curve (AUC) of 0.89 and accuracy of 81.6%). Alternatively, considering the robust predictors (six textural features and one higher-order statistical metric) and application of the LASSO approach including all patients, a decision tree reached the best results for stratifying patients based on PFS time (AUC of 0.96 and accuracy of 94.7%). CONCLUSIONS Specific radiomic features could be used to select patients with lung adenocarcinoma who would benefit from immunotherapy because a subset of imaging radiomic features useful to predict OS or PFS time were different between the control group and the immunotherapy group.
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8
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Granata V, Fusco R, Barretta ML, Picone C, Avallone A, Belli A, Patrone R, Ferrante M, Cozzi D, Grassi R, Grassi R, Izzo F, Petrillo A. Radiomics in hepatic metastasis by colorectal cancer. Infect Agent Cancer 2021; 16:39. [PMID: 34078424 PMCID: PMC8173908 DOI: 10.1186/s13027-021-00379-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/12/2021] [Indexed: 02/06/2023] Open
Abstract
Background Radiomics is an emerging field and has a keen interest, especially in the oncology field. The process of a radiomics study consists of lesion segmentation, feature extraction, consistency analysis of features, feature selection, and model building. Manual segmentation is one of the most critical parts of radiomics. It can be time-consuming and suffers from variability in tumor delineation, which leads to the reproducibility problem of calculating parameters and assessing spatial tumor heterogeneity, particularly in large or multiple tumors. Radiomic features provides data on tumor phenotype as well as cancer microenvironment. Radiomics derived parameters, when associated with other pertinent data and correlated with outcomes data, can produce accurate robust evidence based clinical decision support systems. The principal challenge is the optimal collection and integration of diverse multimodal data sources in a quantitative manner that delivers unambiguous clinical predictions that accurately and robustly enable outcome prediction as a function of the impending decisions. Methods The search covered the years from January 2010 to January 2021. The inclusion criterion was: clinical study evaluating radiomics of liver colorectal metastases. Exclusion criteria were studies with no sufficient reported data, case report, review or editorial letter. Results We recognized 38 studies that assessed radiomics in mCRC from January 2010 to January 2021. Twenty were on different tpics, 5 corresponded to most criteria; 3 are review, or letter to editors; so 10 articles were included. Conclusions In colorectal liver metastases radiomics should be a valid tool for the characterization of lesions, in the stratification of patients based on the risk of relapse after surgical treatment and in the prediction of response to chemotherapy treatment.
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Affiliation(s)
- Vincenza Granata
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
| | - Roberta Fusco
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy.
| | - Maria Luisa Barretta
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
| | - Carmine Picone
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
| | - Antonio Avallone
- Abdominal Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Andrea Belli
- Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Renato Patrone
- Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Marilina Ferrante
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Diletta Cozzi
- Division of Radiology, "Azienda Ospedaliera Universitaria Careggi", Florence, Italy
| | - Roberta Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Roberto 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
| | - Francesco Izzo
- Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Antonella Petrillo
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
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9
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A Rare Case of Cerebral Venous Thrombosis and Disseminated Intravascular Coagulation Temporally Associated to the COVID-19 Vaccine Administration. J Pers Med 2021; 11:jpm11040285. [PMID: 33917902 PMCID: PMC8068274 DOI: 10.3390/jpm11040285] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/02/2021] [Accepted: 04/08/2021] [Indexed: 12/16/2022] Open
Abstract
Globally, at the time of writing (20 March 2021), 121.759.109 confirmed COVID-19 cases have been reported to the WHO, including 2.690.731 deaths. Globally, on 18 March 2021, a total of 364.184.603 vaccine doses have been administered. In Italy, 3.306.711 confirmed COVID-19 cases with 103.855 deaths have been reported to WHO. In Italy, on 9 March 2021, a total of 6.634.450 vaccine doses have been administered. On 15 March 2021, Italian Medicines Agency (AIFA) decided to temporarily suspend the use of the AstraZeneca COVID-19 vaccine throughout the country as a precaution, pending the rulings of the European Medicines Agency (EMA). This decision was taken in line with similar measures adopted by other European countries due to the death of vaccinated people. On 18 March 2021, EMA’s safety committee concluded its preliminary review about thromboembolic events in people vaccinated with COVID-19 Vaccine AstraZeneca at its extraordinary meeting, confirming the benefits of the vaccine continue to outweigh the risk of side effects, however, the vaccine may be associated with very rare cases of blood clots associated with thrombocytopenia, i.e., low levels of blood platelets with or without bleeding, including rare cases of cerebral venous thrombosis (CVT). We report the case of a 54-year-old woman who developed disseminated intravascular coagulation (DIC) with multi-district thrombosis 12 days after the AstraZeneca COVID-19 vaccine administration. A brain computed tomography (CT) scan showed multiple subacute intra-axial hemorrhages in atypical locations, including the right frontal and the temporal lobes. A plain old balloon angioplasty (POBA) of the right coronary artery was performed, without stent implantation, with restoration of distal flow, but with persistence of extensive thrombosis of the vessel. A successive thorax angio-CT added the findings of multiple contrast filling defects with multi-vessel involvement: at the level of the left upper lobe segmental branches, of left interlobar artery, of the right middle lobe segmental branches and of the right interlobar artery. A brain magnetic resonance imaging (MRI) in the same day showed the presence of an acute basilar thrombosis associated with the superior sagittal sinus thrombosis. An abdomen angio-CT showed filling defects at the level of left portal branch and at the level of right suprahepatic vein. Bilaterally, it was adrenal hemorrhage and blood in the pelvis. An evaluation of coagulation factors did not show genetic alterations so as the nasopharyngeal swab ruled out a COVID-19 infection. The patient died after 5 days of hospitalization in intensive care.
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10
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Clinical Phase I/II Study: Local Disease Control and Survival in Locally Advanced Pancreatic Cancer Treated with Electrochemotherapy. J Clin Med 2021; 10:jcm10061305. [PMID: 33810058 PMCID: PMC8005134 DOI: 10.3390/jcm10061305] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/15/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
Abstract
Objective. To assess local disease control rates (LDCR) and overall survival (OS) in locally advanced pancreatic cancer (LAPC) treated with electrochemotherapy (ECT). Methods. Electrochemotherapy with bleomycin was performed in 25 LAPC patients who underwent baseline Magnetic Resonance Imaging (MRI) and/or Computed Tomography (CT) and Position Emission Tomography (PET) scans before ECT and 1 and 6 months post ECT. LDCR were assessed using Response Evaluation Criteria in Solid Tumors (RECIST 1.1) and Choi criteria. Needle electrodes with fixed linear (N-30-4B) or fixed hexagonal configurations (N-30-HG or I-40-HG or H-30-ST) or variable geometry (VGD1230 or VGD1240) (IGEA S.p.A., Carpi, Italy) were used to apply electric pulses. Pain evaluation was performed pre-ECT, after 1 month and after 6 months with ECT. Overall survival estimates were calculated by means of a Kaplan-Meier analysis. Results. At 1 month after ECT, 76% of patients were in partial response (PR) and 20% in stable disease (SD). Six months after ECT, 44.0% patients were still in PR and 12.0% in SD. A LDCR of 56.0% was reached six months after ECT: 13 patients treated with fixed geometry had a LDCR of 46.1%, while for the 12 patients treated with variable geometry, the LDCR was 66.7%. The overall survival median value was 11.5 months: for patients treated with fixed geometry the OS was 6 months, while for patients treated with variable geometry it was 12 months. Electrochemotherapy was well-tolerated and abdominal pain was rapidly resolved. Conclusions. Electrochemotherapy obtained good results in terms of LDCR and OS in LAPC. Multiple needle insertion in a variable geometry configuration optimized by pre-treatment planning determined an increase in LDCR and OS compared to a fixed geometry configuration.
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11
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Fusco R, Granata V, Mazzei MA, Meglio ND, Roscio DD, Moroni C, Monti R, Cappabianca C, Picone C, Neri E, Coppola F, Montanino A, Grassi R, Petrillo A, Miele V. Quantitative imaging decision support (QIDS TM) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan. Cancer Control 2021; 28:1073274820985786. [PMID: 33567876 PMCID: PMC8482708 DOI: 10.1177/1073274820985786] [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] [Indexed: 12/21/2022] Open
Abstract
Objective: To evaluate the consistency of the quantitative imaging decision support (QIDSTM) tool and radiomic analysis using 594 metrics in lung carcinoma on chest CT scan. Materials and Methods: We included, retrospectively, 150 patients with histologically confirmed lung cancer who underwent chemotherapy and baseline and follow-ups CT scans. Using the QIDSTM platform, 3 radiologists segmented each lesion and automatically collected the longest diameter and the density mean value. Inter-observer variability, Bland Altman analysis and Spearman’s correlation coefficient were performed. QIDSTM tool consistency was assessed in terms of agreement rate in the treatment response classification. Kruskal Wallis test and the least absolute shrinkage and selection operator (LASSO) method with 10-fold cross validation were used to identify radiomic metrics correlated with lesion size change. Results: Good and significant correlation was obtained between the measurements of largest diameter and of density among the QIDSTM tool and the radiologists measurements. Inter-observer variability values were over 0.85. HealthMyne QIDSTM tool quantitative volumetric delineation was consistent and matched with each radiologist measurement considering the RECIST classification (80-84%) while a lower concordance among QIDSTM and the radiologists CHOI classification was observed (58-63%). Among 594 extracted metrics, significant and robust predictors of RECIST response were energy, histogram entropy and uniformity, Kurtosis, coronal long axis, longest planar diameter, surface, Neighborhood Grey-Level Different Matrix (NGLDM) dependence nonuniformity and low dependence emphasis as Volume, entropy of Log(2.5 mm), wavelet energy, deviation and root man squared. Conclusion: In conclusion, we demonstrated that HealthMyne quantitative volumetric delineation was consistent and that several radiomic metrics extracted by QIDSTM were significant and robust predictors of RECIST response.
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Affiliation(s)
- Roberta Fusco
- Radiology Division, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Vincenza Granata
- Radiology Division, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Maria Antonietta Mazzei
- Department of Radiological Sciences, Diagnostic Imaging Unit, "Azienda Ospedaliera Universitaria Senese," Siena, Italy
| | - Nunzia Di Meglio
- Department of Radiological Sciences, Diagnostic Imaging Unit, "Azienda Ospedaliera Universitaria Senese," Siena, Italy
| | - Davide Del Roscio
- Department of Radiological Sciences, Diagnostic Imaging Unit, "Azienda Ospedaliera Universitaria Senese," Siena, Italy
| | - Chiara Moroni
- Division of Radiodiagnostic, 18561"Azienda Ospedaliero-Universitaria Careggi," Firenze, Italy
| | - Riccardo Monti
- Division of Radiodiagnostic, "Università degli Studi della Campania Luigi Vanvitelli," Naples, Italy
| | - Carlotta Cappabianca
- Division of Radiodiagnostic, "Università degli Studi della Campania Luigi Vanvitelli," Naples, Italy
| | - Carmine Picone
- Radiology Division, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Emanuele Neri
- Division of Radiodiagnostic, 9257"Azienda Ospedaliera Universitaria Pisana," Pisa, Italy
| | - Francesca Coppola
- Radiology Unit, Department of Specialized, Diagnostic and Experimental Medicine (DIMES), "S. Orsola Hospital, University of Bologna," Bologna, Italy
| | - Agnese Montanino
- Thoracic Medical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli," Naples, Italy
| | - Roberta Grassi
- Division of Radiodiagnostic, "Università degli Studi della Campania Luigi Vanvitelli," Naples, Italy
| | - Antonella Petrillo
- Radiology Division, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Vittorio Miele
- Division of Radiodiagnostic, 18561"Azienda Ospedaliero-Universitaria Careggi," Firenze, Italy
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12
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Guerrini S, Bagnacci G, Barile A, La Paglia E, Gentili F, Luzzi L, Giordano N, Fioravanti A, Bellisai F, Cantarini L, Volterrani L, Frediani B, Mazzei MA. Anterior chest wall non-traumatic diseases: a road map for the radiologist. ACTA BIO-MEDICA : ATENEI PARMENSIS 2020; 91:43-50. [PMID: 32945278 PMCID: PMC7944680 DOI: 10.23750/abm.v91i8-s.9972] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 06/11/2020] [Indexed: 12/17/2022]
Abstract
The anterior chest wall (AWC) non-traumatic pathologies are largely underestimated, and early detection through imaging is becoming increasingly important. This paper aims to review the major non-traumatic ACW pathologies, with a particular interest in imaging features and differential diagnosis. (www.actabiomedica.it)
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Affiliation(s)
- Susanna Guerrini
- Unit of Diagnostic Imaging, Department of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy.
| | - Giulio Bagnacci
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy.
| | - Antonio Barile
- Department of Applied Clinical Science and Biotechnology, University of L'Aquila, L'Aquila, Italy.
| | | | - Francesco Gentili
- Section of Radiology, Department of Medicine and Surgery, Azienda Ospedaliera Universitaria di Parma, Parma, Italy.
| | - Luca Luzzi
- Thoracic Surgery Unit, Department of Medical, Surgical and Neuro Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy.
| | - Nicola Giordano
- Scleroderma Unit, Internal Medicine, Department of Medical, Surgical and Neuro Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy.
| | - Antonella Fioravanti
- Unit of Rheumatology, Department of Medical, Surgical and Neuro Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Tuscany, Italy.
| | - Francesca Bellisai
- Unit of Rheumatology, Department of Medical, Surgical and Neuro Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Tuscany, Italy.
| | - Luca Cantarini
- Unit of Rheumatology, Department of Medical, Surgical and Neuro Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Tuscany, Italy.
| | - Luca Volterrani
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy.
| | - Bruno Frediani
- Unit of Rheumatology, Department of Medical, Surgical and Neuro Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Tuscany, Italy.
| | - Maria Antonietta Mazzei
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy.
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13
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Floridi C, Fogante M, Agostini A, Borgheresi A, Cellina M, Natella R, Bruno F, Cozzi D, Maggialetti N, Palumbo P, Miele V, Carotti M, Giovagnoni A. Radiological diagnosis of Coronavirus Disease 2019 (COVID-19): a Practical Guide. ACTA BIO-MEDICA : ATENEI PARMENSIS 2020; 91:51-59. [PMID: 32945279 PMCID: PMC7944677 DOI: 10.23750/abm.v91i8-s.9973] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 06/11/2020] [Indexed: 12/13/2022]
Abstract
Novel beta-coronavirus (2019-nCoV) is the cause of Coronavirus disease-19 (COVID-19), and on March 12th 2020, the World Health Organization defined COVID-19 as a controllable pandemic. Currently, the 2019 novel coronavirus (SARS-CoV-2) can be identified by virus isolation or viral nucleic acid detection; however, false negatives associated with the nucleic acid detection provide a clinical challenge. Imaging examination has become the indispensable means not only in the early detection and diagnosis but also in monitoring the clinical course, evaluating the disease severity, and may be presented as an important warning signal preceding the negative RT-PCR test results. Different radiological modalities can be used in different disease settings. Radiology Departments must be nimble in implementing operational changes to ensure continued radiology services and protect patients and staff health.
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Affiliation(s)
- Chiara Floridi
- University Politecnica delle Marche, Department of Clinical, Special and Dental Sciences and University Hospital "Umberto I - Lancisi - Salesi", Department of Radiology, Ancona, Italy.
| | - Marco Fogante
- University Hospital "Umberto I - Lancisi - Salesi", Department of Radiology, Ancona, Italy.
| | - Andrea Agostini
- University Politecnica delle Marche, Department of Clinical, Special and Dental Sciences and University Hospital "Umberto I - Lancisi - Salesi", Department of Radiology, Ancona, Italy.
| | - Alessandra Borgheresi
- University Hospital "Umberto I - Lancisi - Salesi", Department of Radiology, Ancona, Italy.
| | - Michaela Cellina
- Department of Radiology, ASST Fatebenefratelli Sacco, Milan, Italy.
| | - Raffaele Natella
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy.
| | - Federico Bruno
- Department of Biotecnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy.
| | - Diletta Cozzi
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.
| | - Nicola Maggialetti
- Department of Medicine and Health Sciences "V. Tiberio", University of Molise, Campobasso, Italy..
| | - Pierpaolo Palumbo
- Department of Biotecnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy.
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.
| | - Marina Carotti
- University Hospital "Umberto I - Lancisi - Salesi", Department of Radiology, Ancona, Italy.
| | - Andrea Giovagnoni
- University Politecnica delle Marche, Department of Clinical, Special and Dental Sciences and University Hospital "Umberto I - Lancisi - Salesi", Department of Radiology, Ancona, Italy.
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14
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The texture analysis as a predictive method in the assessment of the cytological specimen of CT-guided FNAC of the lung cancer. Med Oncol 2020; 37:54. [PMID: 32424733 DOI: 10.1007/s12032-020-01375-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 04/13/2020] [Indexed: 02/07/2023]
Abstract
The lung cancer is the principle cause of the worldwide deaths and its prognosis is poor with a 5-year overall survival rate. Computed tomography (CT) gives many information about the prognosis, but the problem is the subject interpretation of the findings. Thanks to the computer-aided diagnosis/detection (CAD), it is possible to reduce the second opinion. "Radiomics" is an extension of CAD and overlaps the quantitative imaging data of the CT texture analysis (CTTA) with the clinical information, increasing the power and precision of the decision going through the personalized medicine. The aim of this study is to describe the role of the radiomics in the characterization of the pulmonary nodule. For this study, we retrospectively analyzed the images of the 87 NSCLC patients with a waiver of informed consent from the Institutional Review Board (IRB) at the Campania University "Luigi Vanvitelli" of Naples. All tumors were semiautomatically segmented by a radiologist with 10 years of experience using three diameters (AW Server 3.2). The examinations were acquired using 128 MDCT (GSI CT, GE) with a peak tube voltage of 120 kVp, tube current of 100 or 200 mA, and rotation times of 0.5 or 0.8 s. To confirm the imaging results, the FNAC was performed and for every nodule the following parameters were extracted: the presence of the solid component (named = 1), papillary component (named = 2), and mixed component (named = 3). Feature calculation was performed using the HealthMyne software and Integrated Platform That Enables Better Patient Management Decisions For Oncology. The radiologist uses the Rapid Precise Metrics (RPM)™ functionality to identify a lesion with the algorithm and these methods are put to work. The correlation between each feature and the tumor volume was calculated using a two-step cluster statistical analysis. In this retrospective study, in one year from 2018 to 2019 20 patients with lung adenocarcinoma confirmed with FNAC were enrolled. The pathologic results were subdivided into three categories: the solid architecture (group 1), papillary architecture (group 2), and mixed architecture (group 3). Nine lesions resulted with component 1, seven patients with component 2, and 3 patients with component 3. Eight females and 12 males with a median age 61 and 15 years (mean ± SD = 67.4 ± 9.7 years, range 39-73 years) were enrolled. The two results suggest, with p < 0.05, that the GGO variable is a good discriminating estimator of the kurtosis variable: GGO = "no" implies a high kurtosis value, while GGO = "yes" implies a low value. The numerous data obtained from the automatic analysis allow to have a fertile ground on which to develop a new concept of medicine which is precision medicine. The limit of this study is the poor sample. In the future, in order to have a more mature and consolidated discipline, it is necessary to increase the large scale of observations with further studies to establish the rigorous evaluation criteria. In order for radiomics to mature as a discipline in the future, it will be necessary to develop studies that consolidate its role to standardize the collected data.
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15
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Delta-radiomics increases multicentre reproducibility: a phantom study. Med Oncol 2020; 37:38. [PMID: 32236847 DOI: 10.1007/s12032-020-01359-9] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 03/06/2020] [Indexed: 12/19/2022]
Abstract
Texture analysis (TA) can provide quantitative features from medical imaging that can be correlated to clinical endpoints. The challenges relevant to robustness of radiomics features have been analyzed by many researchers, as it seems to be influenced by acquisition and reconstruction protocols. Delta-texture analysis (D-TA), conversely, consist in the analysis of TA feature variations at different acquisition times, usually before and after a therapy. Aim of this study was to investigate the influence of different CT scanners and acquisition parameters in the robustness of TA and D-TA. We scanned a commercial phantom (CIRS model 467, Gammex, Middleton, WI, USA), that is used for the calibration of electron density, two times by varying the disposition of plugs, using three different scanners. After the segmentation, we extracted TA features with LifeX and calculated TA features and D-TA features, defined as the variation of each TA parameters extracted from the same position by varying the plugs with the formula (Y-X)/X. The robustness of TA and D-TA features were then tested with intraclass coefficient correlation (ICC) analysis. The reliability of TA parameters across different scans, with different acquisition parameters and ROI positions has shown poor reliability in 12/37 and moderate reliability in the remaining 25/37, with no parameters showing good reliability. The reliability of D-TA, conversely, showed poor reliability in 10/37 parameters, moderate reliability in 10/37 parameters, and good reliability in 17/37 parameters. The comparison between TA and D-TA ICCs showed a significant difference for the whole group of parameters (p:0.004) and for the subclasses of GLCM parameters (p:0.033), whereas for the other subclasses of matrices (GLRLM, NGLDM, GLZLM, Histogram), the difference was not significant. D-TA features seem to be more robust than TA features. These findings reinforce the potentiality for using D-TA features for early assessment of treatment response and for developing tailored therapies. More work is needed in a clinical setting to confirm the results of the present study.
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16
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A simple prediction model using fluorodeoxyglucose-PET and high-resolution computed tomography for discrimination of invasive adenocarcinomas among solitary pulmonary ground-glass opacity nodules. Nucl Med Commun 2019; 40:1256-1262. [PMID: 31568191 DOI: 10.1097/mnm.0000000000001092] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
OBJECTIVE To analyze the FDG-PET and high-resolution computed tomography (HRCT) features of early lung adenocarcinoma manifesting as solitary ground-glass opacity nodules (GGNs), and to establish a new risk model for predicting the invasiveness of early lung adenocarcinoma. METHODS We retrospectively analyzed the data of clinical stage IA lung adenocarcinoma patients who received preoperative PET/CT and HRCT examination. Patients were divided into invasive adenocarcinoma (IVA) group and preinvasive minimally invasive adenocarcinoma (MIA) group. The correlations between FDG-PET parameters, HRCT parameters and histopathological invasiveness, and their predictive efficacy were analyzed. A mathematical model for predicting histopathological invasiveness of early lung adenocarcinoma was established and assessed. RESULTS This study enrolled 56 patients, 48 were in IVA group and 8 were in preinvasive MIA group. Compared with those in preinvasive MIA group, GGNs in IVA group showed larger diameter, higher ground-glass opacity (GGO) density and more pleural indentation signs (70.8%) on HRCT; they also showed higher maximum standardized uptake value (SUV) and SUV index on FDG-PET (P = 0.001-0.037). Logistic regression analysis found a risk model for predicting IVA of solitary GGNs that were established by CTGGO and SUV index. Receiver operating characteristic curves showed that this model had the highest area under the curve (AUC), sensitivity, specificity and accuracy (AUC, 0.948; sensitivity, 95.8%; specificity, 87.5%; accuracy, 94.6%). CONCLUSION Using HRCT combined with FDG-PET to establish the corresponding mathematical prediction model has the potential to identify IVA in early lung adenocarcinoma preoperatively.
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