1
|
Yoo SJ, Park YS, Choi H, Kim DS, Goo JM, Yoon SH. Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT. PLoS One 2024; 19:e0297390. [PMID: 38386632 PMCID: PMC10883577 DOI: 10.1371/journal.pone.0297390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 01/04/2024] [Indexed: 02/24/2024] Open
Abstract
PURPOSE To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR). METHODS The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool. RESULTS DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P < .001). In general, DLIR showed superior subjective image quality for ULDCT images (P < .001) and comparable quality for LDCT images compared to ASIR-V50 (P = .01-1). The per-nodule sensitivities of observers for Lung-RADS category 3-4 nodules were 70.6-88.2% and 64.7-82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P = .50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P = .65). CONCLUSION DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images.
Collapse
Affiliation(s)
- Seung-Jin Yoo
- Department of Radiology, Hanyang University Medical Center, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Young Sik Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea
| | - Hyewon Choi
- Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, South Korea
| | - Da Som Kim
- Departments of Radiology, Busan Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Jin Mo Goo
- Department of radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea
| | - Soon Ho Yoon
- Department of radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea
| |
Collapse
|
2
|
Guedes Pinto E, Penha D, Ravara S, Monaghan C, Hochhegger B, Marchiori E, Taborda-Barata L, Irion K. Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis. Insights Imaging 2023; 14:152. [PMID: 37741928 PMCID: PMC10517915 DOI: 10.1186/s13244-023-01480-z] [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: 04/18/2023] [Accepted: 07/08/2023] [Indexed: 09/25/2023] Open
Abstract
Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance. CRITICAL RELEVANCE STATEMENT: Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence. KEY POINTS: • Factors influencing the volumetry of pulmonary nodules have been extensively investigated. • Just 11% of studies test clinical significance (wrongly diagnosing growth). • Nodule size interacts with most other influencing factors (especially for smaller nodules). • Heterogeneity among studies makes comparison and consolidation of results challenging. • Future research should focus on clinical applicability, screening, and updated technology.
Collapse
Affiliation(s)
- Erique Guedes Pinto
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal.
| | - Diana Penha
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal
- Liverpool Heart and Chest Hospital NHS Foundation Trust, Thomas Dr, Liverpool, L14 3PE, UK
| | - Sofia Ravara
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal
| | - Colin Monaghan
- Liverpool Heart and Chest Hospital NHS Foundation Trust, Thomas Dr, Liverpool, L14 3PE, UK
| | | | - Edson Marchiori
- Faculdade de Medicina, Universidade Federal Do Rio de Janeiro, Bloco K - Av. Carlos Chagas Filho, 373 - 2º Andar, Sala 49 - Cidade Universitária da Universidade Federal Do Rio de Janeiro, Rio de Janeiro - RJ, 21044-020, Brasil
- Faculdade de Medicina, Universidade Federal Fluminense, Av. Marquês Do Paraná, 303 - Centro, Niterói - RJ, 24220-000, Brasil
| | - Luís Taborda-Barata
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal
| | - Klaus Irion
- Manchester University NHS Foundation Trust, Manchester Royal Infirmary, Oxford Rd, Manchester, M13 9WL, UK
| |
Collapse
|
3
|
Comparison of Lung Cancer Aggressiveness in Patients Who Never Smoked Compared to Those Who Smoked. Lung Cancer 2022; 171:90-96. [DOI: 10.1016/j.lungcan.2022.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 06/27/2022] [Accepted: 07/02/2022] [Indexed: 11/22/2022]
|
4
|
Balagurunathan Y, Beers A, McNitt-Gray M, Hadjiiski L, Napel S, Goldgof D, Perez G, Arbelaez P, Mehrtash A, Kapur T, Yang E, Moon JW, Bernardino G, Delgado-Gonzalo R, Farhangi MM, Amini AA, Ni R, Feng X, Bagari A, Vaidhya K, Veasey B, Safta W, Frigui H, Enguehard J, Gholipour A, Castillo LS, Daza LA, Pinsky P, Kalpathy-Cramer J, Farahani K. Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3748-3761. [PMID: 34264825 PMCID: PMC9531053 DOI: 10.1109/tmi.2021.3097665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Lung cancer is by far the leading cause of cancer death in the US. Recent studies have demonstrated the effectiveness of screening using low dose CT (LDCT) in reducing lung cancer related mortality. While lung nodules are detected with a high rate of sensitivity, this exam has a low specificity rate and it is still difficult to separate benign and malignant lesions. The ISBI 2018 Lung Nodule Malignancy Prediction Challenge, developed by a team from the Quantitative Imaging Network of the National Cancer Institute, was focused on the prediction of lung nodule malignancy from two sequential LDCT screening exams using automated (non-manual) algorithms. We curated a cohort of 100 subjects who participated in the National Lung Screening Trial and had established pathological diagnoses. Data from 30 subjects were randomly selected for training and the remaining was used for testing. Participants were evaluated based on the area under the receiver operating characteristic curve (AUC) of nodule-wise malignancy scores generated by their algorithms on the test set. The challenge had 17 participants, with 11 teams submitting reports with method description, mandated by the challenge rules. Participants used quantitative methods, resulting in a reporting test AUC ranging from 0.698 to 0.913. The top five contestants used deep learning approaches, reporting an AUC between 0.87 - 0.91. The team's predictor did not achieve significant differences from each other nor from a volume change estimate (p =.05 with Bonferroni-Holm's correction).
Collapse
Affiliation(s)
| | | | | | | | - Sandy Napel
- Dept. of Radiology, School of Medicine, Stanford University (SU), CA
| | | | - Gustavo Perez
- Biomedical computer vision lab (BCV), Universidad de los Andes, Colombia
| | - Pablo Arbelaez
- Biomedical computer vision lab (BCV), Universidad de los Andes, Colombia
| | - Alireza Mehrtash
- Robotics and Control Laboratory (RCL), Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC
- Surgical Planning Laboratory (SPL), Radiology Department, Brigham and Women’s Hospital, Boston, MA, 02130
| | - Tina Kapur
- Surgical Planning Laboratory (SPL), Radiology Department, Brigham and Women’s Hospital, Boston, MA, 02130
| | - Ehwa Yang
- Sungkyunkwan University School of Medicine, Seoul 06351, Korea
| | - Jung Won Moon
- Human Medical Imaging & Intervention Center, Seoul 06524, Korea
| | - Gabriel Bernardino
- Centre Suisse d’Électronique et de Microtechnique, Neuchâtel, Switzerland
| | | | - M. Mehdi Farhangi
- Medical Imaging Laboratory, University of Louisville, Louisville, KY. USA
- Computer Engineering and Computer Science, University of Louisville
| | - Amir A. Amini
- Medical Imaging Laboratory, University of Louisville, Louisville, KY. USA
- Electrical and Computer Engineering Department, University of Louisville, Louisville, KY. USA
| | | | - Xue Feng
- Spingbok Inc
- Department of Biomedical Engineering, University of Virginia, Charlottesville
| | | | | | - Benjamin Veasey
- Medical Imaging Laboratory, University of Louisville, Louisville, KY. USA
- Electrical and Computer Engineering Department, University of Louisville, Louisville, KY. USA
| | - Wiem Safta
- Computer Engineering and Computer Science, University of Louisville
| | - Hichem Frigui
- Computer Engineering and Computer Science, University of Louisville
| | - Joseph Enguehard
- Department of Radiology, Boston Children’s Hospital, and Harvard Medical School
| | - Ali Gholipour
- Department of Radiology, Boston Children’s Hospital, and Harvard Medical School
| | | | - Laura Alexandra Daza
- Department of Biomedical Engineering, Universidad de los Andes, Bogota, Colombia
| | - Paul Pinsky
- Divsion of Cancer Prevention, National Cancer Institute (NCI), Washington DC
| | | | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), Washington DC
| |
Collapse
|
5
|
Prospective Study of Low- and Standard-dose Chest CT for Pulmonary Nodule Detection: A Comparison of Image Quality, Size Measurements and Radiation Exposure. Curr Med Sci 2021; 41:966-973. [PMID: 34652628 DOI: 10.1007/s11596-021-2433-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 12/20/2020] [Indexed: 12/19/2022]
Abstract
OBJECTIVE To comprehensively and accurately analyze the out-performance of low-dose chest CT (LDCT) vs. standard-dose CT (SDCT). METHODS The image quality, size measurements and radiation exposure for LDCT and SDCT protocols were evaluated. A total of 117 patients with extra-thoracic malignancies were prospectively enrolled for non-enhanced CT scanning using LDCT and SDCT protocols. Three experienced radiologists evaluated subjective image quality independently using a 5-point score system. Nodule detection efficiency was compared between LDCT and SDCT based on nodule characteristics (size and volume). Radiation metrics and organ doses were analyzed using Radimetrics. RESULTS The images acquired with the LDCT protocol yielded comparable quality to those acquired with the SDCT protocol. The sensitivity of LDCT for the detection of pulmonary nodules (n=650) was lower than that of SDCT (n=660). There was no significant difference in the diameter and volume of pulmonary nodules between LDCT and SDCT (for BMI <22 kg/m2, 4.37 vs. 4.46 mm, and 43.66 vs. 46.36 mm3; for BMI ≥22 kg/m2, 4.3 vs. 4.41 mm, and 41.66 vs. 44.86 mm3) (P>0.05). The individualized volume CT dose index (CTDIvol), the size specific dose estimate and effective dose were significantly reduced in the LDCT group compared with the SDCT group (all P<0.0001). This was especially true for dose-sensitive organs such as the lung (for BMI <22 kg/m2, 2.62 vs. 12.54 mSV, and for BMI ≥22 kg/m2, 1.62 vs. 9.79 mSV) and the breast (for BMI <22 kg/m2, 2.52 vs. 10.93 mSV, and for BMI ≥22 kg/m2, 1.53 vs. 9.01 mSV) (P<0.0001). CONCLUSION These results suggest that with the increases in image noise, LDCT and SDCT exhibited a comparable image quality and sensitivity. The LDCT protocol for chest scans may reduce radiation exposure by about 80% compared to the SDCT protocol.
Collapse
|
6
|
Snoeckx A, Franck C, Silva M, Prokop M, Schaefer-Prokop C, Revel MP. The radiologist's role in lung cancer screening. Transl Lung Cancer Res 2021; 10:2356-2367. [PMID: 34164283 PMCID: PMC8182709 DOI: 10.21037/tlcr-20-924] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Lung cancer is still the deadliest cancer in men and women worldwide. This high mortality is related to diagnosis in advanced stages, when curative treatment is no longer an option. Large randomized controlled trials have shown that lung cancer screening (LCS) with low-dose computed tomography (CT) can detect lung cancers at earlier stages and reduce lung cancer-specific mortality. The recent publication of the significant reduction of cancer-related mortality by 26% in the Dutch-Belgian NELSON LCS trial has increased the likelihood that implementation of LCS in Europe will move forward. Radiologists are important stakeholders in numerous aspects of the LCS pathway. Their role goes beyond nodule detection and nodule management. Being part of a multidisciplinary team, radiologists are key players in numerous aspects of implementation of a high quality LCS program. In this non-systematic review we discuss the multifaceted role of radiologists in LCS.
Collapse
Affiliation(s)
- Annemiek Snoeckx
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Caro Franck
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Mathias Prokop
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Marie-Pierre Revel
- Department of Radiology, Cochin Hospital, APHP Centre, Université de Paris, Paris, France
| |
Collapse
|
7
|
Meltzer C, Fagman E, Vikgren J, Molnar D, Borna E, Beni MM, Brandberg J, Bergman B, Båth M, Johnsson ÅA. Surveillance of small, solid pulmonary nodules at digital chest tomosynthesis: data from a cohort of the pilot Swedish CArdioPulmonary bioImage Study (SCAPIS). Acta Radiol 2021; 62:348-359. [PMID: 32438877 PMCID: PMC7930602 DOI: 10.1177/0284185120923106] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Background Digital tomosynthesis (DTS) might be a low-dose/low-cost alternative to computed tomography (CT). Purpose To investigate DTS relative to CT for surveillance of incidental, solid pulmonary nodules. Material and Methods Recruited from a population study, 106 participants with indeterminate solid pulmonary nodules on CT underwent surveillance with concurrently performed CT and DTS. Nodule size on DTS was assessed by manual diameter measurements and semi-automatic nodule segmentations were independently performed on CT. Measurement agreement was analyzed according to Bland–Altman with 95% limits of agreement (LoA). Detection of nodule volume change > 25% by DTS in comparison to CT was evaluated with receiver operating characteristics (ROC). Results A total of 81 nodules (76%) were assessed as measurable on DTS by two independent observers. Inter- and intra-observer LoA regarding change in average diameter were ± 2 mm. Calculation of relative volume change on DTS resulted in wide inter- and intra-observer LoA in the order of ± 100% and ± 50%. Comparing relative volume change between DTS and CT resulted in LoA of –58% to 67%. The area under the ROC curve regarding the ability of DTS to detect volumetric changes > 25% on CT was 0.58 (95% confidence interval [CI] = 0.40–0.76) and 0.50 (95% CI = 0.35–0.66) for the two observers. Conclusion The results of the present study show that measurement variability limits the agreement between DTS and CT regarding nodule size change for small solid nodules.
Collapse
Affiliation(s)
- Carin Meltzer
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy at University of Gothenburg, Sweden
- Department of Radiology, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Norway
| | - Erika Fagman
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy at University of Gothenburg, Sweden
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jenny Vikgren
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy at University of Gothenburg, Sweden
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - David Molnar
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy at University of Gothenburg, Sweden
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Eivind Borna
- Krefting Research Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Maral Mirzai Beni
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - John Brandberg
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy at University of Gothenburg, Sweden
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Bengt Bergman
- Department of Respiratory Medicine, Sahlgrenska University Hospital, Sweden
- Department of Respiratory Medicine, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Sweden
| | - Magnus Båth
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Åse A Johnsson
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy at University of Gothenburg, Sweden
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| |
Collapse
|
8
|
Henschke CI, Yankelevitz DF, Jirapatnakul A, Yip R, Reccoppa V, Benjamin C, Llamo T, Williams A, Liu S, Max D, Aguayo SM, Morales P, Igel BJ, Abbaszadegan H, Fredricks PA, Garcia DP, Permana PA, Fawcett J, Sultan S, Murphy LA. Implementation of low-dose CT screening in two different health care systems: Mount Sinai Healthcare System and Phoenix VA Health Care System. Transl Lung Cancer Res 2021; 10:1064-1082. [PMID: 33718045 PMCID: PMC7947390 DOI: 10.21037/tlcr-20-761] [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: 06/19/2020] [Accepted: 11/10/2020] [Indexed: 12/18/2022]
Abstract
Implementation of lung screening (LS) programs is challenging even among health care organizations that have the motivation, the resources, and more importantly, the goal of providing for life-saving early detection, diagnosis, and treatment of lung cancer. We provide a case study of LS implementation in different healthcare systems, at the Mount Sinai Healthcare System (MSHS) in New York City, and at the Phoenix Veterans Affairs Health Care System (PVAHCS) in Phoenix, Arizona. This will illustrate the commonalities and differences of the LS implementation process in two very different health care systems in very different parts of the United States. Underlying the successful implementation of these LS programs was the use of a comprehensive management system, the Early Lung Cancer Action Program (ELCAP) Management SystemTM. The collaboration between MSHS and PVAHCS over the past decade led to the ELCAP Management SystemTM being gifted by the Early Diagnosis and Treatment Research Foundation to the PVAHCS, to develop a "VA-ELCAP" version. While there remain challenges and opportunities to continue improving LS and its implementation, there is an increasing realization that most patients who are diagnosed with lung cancer as a result of annual LS can be cured, and that of all the possible risks associated with LS, the greater risk of all is for heavy cigarette smokers not to be screened. We identified 10 critical components in implementing a LS program. We provided the details of each of these components for the two healthcare systems. Most importantly, is that continual re-evaluation of the screening program is needed based on the ongoing quality assurance program and database of the actual screenings. At minimum, there should be an annual review and updating. As early diagnosis of lung cancer must be followed by optimal treatment to be effective, treatment advances for small, early lung cancers diagnosed as a result of screening also need to be assessed and incorporated into the entire screening and treatment program.
Collapse
Affiliation(s)
- Claudia I. Henschke
- Mount Sinai Healthcare System, New York, NY, USA
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA
| | - David F. Yankelevitz
- Mount Sinai Healthcare System, New York, NY, USA
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA
| | - Artit Jirapatnakul
- Mount Sinai Healthcare System, New York, NY, USA
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA
| | - Rowena Yip
- Mount Sinai Healthcare System, New York, NY, USA
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA
| | | | | | | | | | - Simon Liu
- Mount Sinai Healthcare System, New York, NY, USA
| | - Daniel Max
- Mount Sinai Healthcare System, New York, NY, USA
| | | | | | - Brian J. Igel
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA
| | | | | | | | | | - Janet Fawcett
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA
| | - Samir Sultan
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA
| | | |
Collapse
|
9
|
Accuracy of Pulmonary Nodule Volumetry Using Noise-Optimized Virtual Monoenergetic Image and Nonlinear Blending Image Algorithms in Dual-Energy Computed Tomography: A Phantom Study. J Comput Assist Tomogr 2020; 44:847-851. [PMID: 32976271 DOI: 10.1097/rct.0000000000001102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The aim of the study was to assess accuracy of pulmonary nodule volumetry using noise-optimized virtual monoenergetic image (VMI+) and nonlinear blending image (NBI) algorithms in dual-energy computed tomography (DECT). METHODS An anthropomorphic chest phantom with 10 simulated nodules (5 solid nodules and 5 ground-glass opacities) was scanned using DECT80/Sn140kV, DECT100/Sn140kV, and single-energy CT (SECT120kV/200mAs), respectively. The dual-energy images were reconstructed using VMI+ (70 keV) and NBI algorithms. The contrast-to-noise ratio and absolute percentage error (APE) of nodule volume were measured to assess image quality and accuracy of nodule volumetry. The radiation dose was also estimated. RESULTS The contrast-to-noise ratio of SECT120kV/200mAs was significantly higher than that of NBI80/Sn140kV and VMI+80/Sn140kV (both corrected P < 0.05), whereas there were no significant differences between NBI100/sn140kV and SECT120kV/200mAs and between VMI+100/sn140kV and SECT120kV/200mAs (both corrected P > 0.05). The APE of SECT120kV/200mAs was significantly lower than that of NBI80/Sn140kV and VMI+80/Sn140kV in both types of nodules (all corrected P < 0.05), whereas there were no significant differences between VMI+100/sn140kV and SECT120kV/200mAs in solid nodules and between NBI100/Sn140kV and SECT120kV/200mAs in ground-glass opacities (both corrected P > 0.05). The radiation dose of DECT100/Sn140kV and DECT80/Sn140kV were significantly lower than that of SECT120kV/200mAs (both corrected P < 0.05). CONCLUSIONS The DECT100/sn140kV can ensure image quality and nodule volumetry accuracy with lower radiation dose compared with SECT120kV/200mAs. Specifically, the VMI+ algorithm could be used in solid nodules and NBI algorithm in ground-glass opacities.
Collapse
|
10
|
Lee HN, Kim JI, Shin SY. Measurement accuracy of lung nodule volumetry in a phantom study: Effect of axial-volume scan and iterative reconstruction algorithm. Medicine (Baltimore) 2020; 99:e20543. [PMID: 32502015 PMCID: PMC7306330 DOI: 10.1097/md.0000000000020543] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
An axial-volume scan with adaptive statistical iterative reconstruction-V (ASIR-V) is newly developed. Our goal was to identify the influence of axial-volume scan and ASIR-V on accuracy of automated nodule volumetry.An "adult' chest phantom containing various nodules was scanned using both helical and axial-volume modes at different dose settings using 256-slice CT. All CT scans were reconstructed using 30% and 50% blending of ASIR-V and filtered back projection. Automated nodule volumetry was performed using commercial software. The image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were measured.The axial-volume scan reduced radiation dose by 19.7% compared with helical scan at all radiation dose settings without affecting the accuracy of nodule volumetric measurement (P = .375). Image noise, CNR, and SNR were not significantly different between two scan modes (all, P > .05).The use of axial-volume scan with ASIR-V achieved effective radiation dose reduction while preserving the accuracy of nodule volumetry.
Collapse
Affiliation(s)
- Han Na Lee
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Jung Im Kim
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - So Youn Shin
- Department of Radiology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| |
Collapse
|
11
|
Soo E, Edey A, Mak S, Moser J, Mohammadi S, Rodrigues T, Duffy S, Field J, Baldwin D, Nair A, Devaraj A. Impact of choice of volumetry software and nodule management guidelines on recall rates in lung cancer screening. Eur J Radiol 2019; 120:108646. [DOI: 10.1016/j.ejrad.2019.108646] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 08/07/2019] [Accepted: 08/30/2019] [Indexed: 11/15/2022]
|
12
|
Eberhard M, Stocker D, Milanese G, Martini K, Nguyen-Kim TDL, Wurnig MC, Frauenfelder T, Baumueller S. Volumetric assessment of solid pulmonary nodules on ultralow-dose CT: a phantom study. J Thorac Dis 2019; 11:3515-3524. [PMID: 31559058 DOI: 10.21037/jtd.2019.08.12] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background To reduce the radiation exposure from chest computed tomography (CT), ultralow-dose CT (ULDCT) protocols performed at sub-millisievert levels were previously tested for the evaluation of pulmonary nodules (PNs). The purpose of our study was to investigate the effect of ULDCT and iterative image reconstruction on volumetric measurements of solid PNs. Methods CT datasets of an anthropomorphic chest phantom containing solid microspheres were obtained with a third-generation dual-source CT at standard dose, 1/8th, 1/20th and 1/70th of standard dose [CT volume dose index (CTDIvol): 0.03-2.03 mGy]. Semi-automated volumetric measurements were performed on CT datasets reconstructed with filtered back projection (FBP) and advanced modelled iterative reconstruction (ADMIRE), at strength level 3 and 5. Absolute percentage error (APE) evaluated measurement accuracy related to the effective volume. Scan repetition differences were evaluated using Bland-Altman analysis. Two-way analysis of variance (ANOVA) assessed influence of different scan parameters on APE. Proportional differences (PDs) tested the effect of dose settings and reconstruction algorithms on volumetric measurements, as compared to the standard protocol (standard dose-FBP). Results Bland-Altman analysis revealed small mean interscan differences of APE with narrow limits of agreement (-0.1%±4.3% to -0.3%±3.8%). Dose settings (P<0.001), reconstruction algorithms (P<0.001), nodule diameters (P<0.001) and nodule density (P=0.011) had statistically significant influence on APE. Post-hoc Bonferroni tests showed slightly higher APE when scanning with 1/70th of standard dose [mean difference: 3.4%, 95% confidence interval (CI): 2.5-4.3%; P<0.001], and for image reconstruction with ADMIRE5 (mean difference: 1.8%, 95% CI: 1.0-2.5%; P<0.001). No significant differences for scanning with 1/20th of standard dose (P=0.42), and image reconstruction with ADMIRE3 (P=0.19) were found. Scanning with 1/70th of standard dose and image reconstruction with FBP showed the widest range of PDs (-16.8% to 23.4%) compared to standard dose-FBP. Conclusions Our phantom study showed no significant difference between nodule volume measurements on standard dose CT (CTDIvol: 2 mGy) and ULDCT with 1/20th of standard dose (CTDIvol: 0.10 mGy).
Collapse
Affiliation(s)
- Matthias Eberhard
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Daniel Stocker
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Gianluca Milanese
- Division of Radiology, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Katharina Martini
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Thi Dan Linh Nguyen-Kim
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Moritz C Wurnig
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Thomas Frauenfelder
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Stephan Baumueller
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| |
Collapse
|
13
|
Paks M, Leong P, Einsiedel P, Irving LB, Steinfort DP, Pascoe DM. Ultralow dose CT for follow-up of solid pulmonary nodules: A pilot single-center study using Bland-Altman analysis. Medicine (Baltimore) 2018; 97:e12019. [PMID: 30142849 PMCID: PMC6112944 DOI: 10.1097/md.0000000000012019] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Solid pulmonary nodules are a common finding requiring serial computed tomography (CT) imaging. We sought to explore the detection and measurement accuracy of an ultralow-dose CT (ULDCT) protocol compared with our standard low-dose CT (LDCT) nodule follow-up protocol.In this pragmatic single-center pilot prospective cohort study, patients scheduled for clinically indicated CT surveillance of 1 or more known solid pulmonary nodules >2 mm underwent ULDCT immediately after routine LDCT. The Bland-Altman 95% limits of agreement for diameter and volumetry were calculated.In all, 57 patients underwent 60 imaging episodes, with 170 evaluable nodules. ULDCT detected all known solid pulmonary nodules >2 mm. Bland-Altman analyses demonstrated clinically agreement for both nodule diameter and volume, both of which fell within prespecified limits.This single-center pilot study suggests that ULDCT may be of use in surveillance of known solid pulmonary nodules >2 mm.
Collapse
Affiliation(s)
| | - Paul Leong
- Department of Respiratory Medicine, Melbourne Health
| | | | - Louis B. Irving
- Department of Respiratory Medicine, Melbourne Health
- Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
| | - Daniel P. Steinfort
- Department of Respiratory Medicine, Melbourne Health
- Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
| | - Diane M. Pascoe
- Department of Radiology
- Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
| |
Collapse
|
14
|
Kamiya S, Iwano S, Umakoshi H, Ito R, Shimamoto H, Nakamura S, Naganawa S. Computer-aided Volumetry of Part-Solid Lung Cancers by Using CT: Solid Component Size Predicts Prognosis. Radiology 2018. [DOI: 10.1148/radiol.2018172319] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Shinichiro Kamiya
- From the Department of Radiology (S.K., S.I., H.U., R.I., H.S., Shinji Naganawa) and Department of Thoracic Surgery (Shota Nakamura), Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Japan
| | - Shingo Iwano
- From the Department of Radiology (S.K., S.I., H.U., R.I., H.S., Shinji Naganawa) and Department of Thoracic Surgery (Shota Nakamura), Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Japan
| | - Hiroyasu Umakoshi
- From the Department of Radiology (S.K., S.I., H.U., R.I., H.S., Shinji Naganawa) and Department of Thoracic Surgery (Shota Nakamura), Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Japan
| | - Rintaro Ito
- From the Department of Radiology (S.K., S.I., H.U., R.I., H.S., Shinji Naganawa) and Department of Thoracic Surgery (Shota Nakamura), Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Japan
| | - Hironori Shimamoto
- From the Department of Radiology (S.K., S.I., H.U., R.I., H.S., Shinji Naganawa) and Department of Thoracic Surgery (Shota Nakamura), Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Japan
| | - Shota Nakamura
- From the Department of Radiology (S.K., S.I., H.U., R.I., H.S., Shinji Naganawa) and Department of Thoracic Surgery (Shota Nakamura), Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Japan
| | - Shinji Naganawa
- From the Department of Radiology (S.K., S.I., H.U., R.I., H.S., Shinji Naganawa) and Department of Thoracic Surgery (Shota Nakamura), Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Japan
| |
Collapse
|
15
|
Mets OM, Chung K, Zanen P, Scholten ET, Veldhuis WB, van Ginneken B, Prokop M, Schaefer-Prokop CM, de Jong PA. In vivo growth of 60 non-screening detected lung cancers: a computed tomography study. Eur Respir J 2018; 51:13993003.02183-2017. [PMID: 29650547 DOI: 10.1183/13993003.02183-2017] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 03/07/2018] [Indexed: 12/23/2022]
Abstract
Current pulmonary nodule management guidelines are based on nodule volume doubling time, which assumes exponential growth behaviour. However, this is a theory that has never been validated in vivo in the routine-care target population. This study evaluates growth patterns of untreated solid and subsolid lung cancers of various histologies in a non-screening setting.Growth behaviour of pathology-proven lung cancers from two academic centres that were imaged at least three times before diagnosis (n=60) was analysed using dedicated software. Random-intercept random-slope mixed-models analysis was applied to test which growth pattern most accurately described lung cancer growth. Individual growth curves were plotted per pathology subgroup and nodule type.We confirmed that growth in both subsolid and solid lung cancers is best explained by an exponential model. However, subsolid lesions generally progress slower than solid ones. Baseline lesion volume was not related to growth, indicating that smaller lesions do not grow slower compared to larger ones.By showing that lung cancer conforms to exponential growth we provide the first experimental basis in the routine-care setting for the assumption made in volume doubling time analysis.
Collapse
Affiliation(s)
- Onno M Mets
- Dept of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Kaman Chung
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Pieter Zanen
- Dept of Pulmonology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Ernst T Scholten
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Wouter B Veldhuis
- Dept of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Bram van Ginneken
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mathias Prokop
- Dept of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Cornelia M Schaefer-Prokop
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.,Dept of Radiology, Meander Medical Center, Amersfoort, The Netherlands
| | - Pim A de Jong
- Dept of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| |
Collapse
|
16
|
Balagurunathan Y, Beers A, Kalpathy-Cramer J, McNitt-Gray M, Hadjiiski L, Zhao B, Zhu J, Yang H, Yip SSF, Aerts HJWL, Napel S, Cherezov D, Cha K, Chan HP, Flores C, Garcia A, Gillies R, Goldgof D. Semi-automated pulmonary nodule interval segmentation using the NLST data. Med Phys 2018; 45:1093-1107. [PMID: 29363773 DOI: 10.1002/mp.12766] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 01/04/2018] [Accepted: 01/04/2018] [Indexed: 01/26/2023] Open
Abstract
PURPOSE To study the variability in volume change estimates of pulmonary nodules due to segmentation approaches used across several algorithms and to evaluate these effects on the ability to predict nodule malignancy. METHODS We obtained 100 patient image datasets from the National Lung Screening Trial (NLST) that had a nodule detected on each of two consecutive low dose computed tomography (LDCT) scans, with an equal proportion of malignant and benign cases (50 malignant, 50 benign). Information about the nodule location for the cases was provided by a screen capture with a bounding box and its axial location was indicated. Five participating quantitative imaging network (QIN) institutions performed nodule segmentation using their preferred semi-automated algorithms with no manual correction; teams were allowed to provide additional manually corrected segmentations (analyzed separately). The teams were asked to provide segmentation masks for each nodule at both time points. From these masks, the volume was estimated for the nodule at each time point; the change in volume (absolute and percent change) across time points was estimated as well. We used the concordance correlation coefficient (CCC) to compare the similarity of computed nodule volumes (absolute and percent change) across algorithms. We used Logistic regression model on the change in volume (absolute change and percent change) of the nodules to predict the malignancy status, the area under the receiver operating characteristic curve (AUROC) and confidence intervals were reported. Because the size of nodules was expected to have a substantial effect on segmentation variability, analysis of change in volumes was stratified by lesion size, where lesions were grouped into those with a longest diameter of <8 mm and those with longest diameter ≥ 8 mm. RESULTS We find that segmentation of the nodules shows substantial variability across algorithms, with the CCC ranging from 0.56 to 0.95 for change in volume (percent change in volume range was [0.15 to 0.86]) across the nodules. When examining nodules based on their longest diameter, we find the CCC had higher values for large nodules with a range of [0.54 to 0.93] among the algorithms, while percent change in volume was [0.3 to 0.95]. Compared to that of smaller nodules which had a range of [-0.0038 to 0.69] and percent change in volume was [-0.039 to 0.92]. The malignancy prediction results showed fairly consistent results across the institutions, the AUC using change in volume ranged from 0.65 to 0.89 (Percent change in volume was 0.64 to 0.86) for entire nodule range. Prediction improves for large nodule range (≥ 8 mm) with AUC range 0.75 to 0.90 (percent change in volume was 0.74 to 0.92). Compared to smaller nodule range (<8 mm) with AUC range 0.57 to 0.78 (percent change in volume was 0.59 to 0.77). CONCLUSIONS We find there is a fairly high concordance in the size measurements for larger nodules (≥8 mm) than the lower sizes (<8 mm) across algorithms. We find the change in nodule volume (absolute and percent change) were consistent predictors of malignancy across institutions, despite using different segmentation algorithms. Using volume change estimates without corrections shows slightly lower predictability (for two teams).
Collapse
Affiliation(s)
| | - Andrew Beers
- Massachusetts General Hospital (MGH), Boston, MA, USA
| | | | | | | | | | | | - Hao Yang
- Columbia University (CUMU), New York, NY, USA
| | - Stephen S F Yip
- Radiation Oncology, Dana-Farber Cancer Institute (DFCC), Brigham and Women's Hospital (BWH) and Harvard Medical School (HMC), Boston, MA, USA.,Radiology, Dana-Farber Cancer Institute (DFCC) Brigham and Women's Hospital (BWH) and Harvard Medical School (HMC), Boston, MA, USA
| | - Hugo J W L Aerts
- Radiation Oncology, Dana-Farber Cancer Institute (DFCC), Brigham and Women's Hospital (BWH) and Harvard Medical School (HMC), Boston, MA, USA.,Radiology, Dana-Farber Cancer Institute (DFCC) Brigham and Women's Hospital (BWH) and Harvard Medical School (HMC), Boston, MA, USA
| | | | - Dmitrii Cherezov
- H.L.Moffitt Cancer Center (MCC), Tampa, FL, USA.,University of South Florida (USF), Tampa, FL, USA
| | - Kenny Cha
- University of Michigan (UMICH), Ann Arbor, MI, USA
| | | | - Carlos Flores
- University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | | | | | - Dmitry Goldgof
- H.L.Moffitt Cancer Center (MCC), Tampa, FL, USA.,University of South Florida (USF), Tampa, FL, USA
| |
Collapse
|
17
|
Silva M, Milanese G, Seletti V, Ariani A, Sverzellati N. Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications. Br J Radiol 2018; 91:20170644. [PMID: 29172671 PMCID: PMC5965469 DOI: 10.1259/bjr.20170644] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Revised: 11/14/2017] [Accepted: 11/23/2017] [Indexed: 12/14/2022] Open
Abstract
The frenetic development of imaging technology-both hardware and software-provides exceptional potential for investigation of the lung. In the last two decades, CT was exploited for detailed characterization of pulmonary structures and description of respiratory disease. The introduction of volumetric acquisition allowed increasingly sophisticated analysis of CT data by means of computerized algorithm, namely quantitative CT (QCT). Hundreds of thousands of CTs have been analysed for characterization of focal and diffuse disease of the lung. Several QCT metrics were developed and tested against clinical, functional and prognostic descriptors. Computer-aided detection of nodules, textural analysis of focal lesions, densitometric analysis and airway segmentation in obstructive pulmonary disease and textural analysis in interstitial lung disease are the major chapters of this discipline. The validation of QCT metrics for specific clinical and investigational needs prompted the translation of such metrics from research field to patient care. The present review summarizes the state of the art of QCT in both focal and diffuse lung disease, including a dedicated discussion about application of QCT metrics as parameters for clinical care and outcomes in clinical trials.
Collapse
Affiliation(s)
- Mario Silva
- Department of Medicine and Surgery (DiMeC), Section of Radiology, Unit of Surgical Sciences, University of Parma, Parma, Italy
| | - Gianluca Milanese
- Department of Medicine and Surgery (DiMeC), Section of Radiology, Unit of Surgical Sciences, University of Parma, Parma, Italy
| | - Valeria Seletti
- Department of Medicine and Surgery (DiMeC), Section of Radiology, Unit of Surgical Sciences, University of Parma, Parma, Italy
| | - Alarico Ariani
- Department of Medicine, Internal Medicine and Rheumatology Unit, University Hospital of Parma, Parma, Italy
| | - Nicola Sverzellati
- Department of Medicine and Surgery (DiMeC), Section of Radiology, Unit of Surgical Sciences, University of Parma, Parma, Italy
| |
Collapse
|