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Farhangi MM, Maynord M, Fermüller C, Aloimonos Y, Sahiner B, Petrick N. Exploring synthetic datasets for computer-aided detection: a case study using phantom scan data for enhanced lung nodule false positive reduction. J Med Imaging (Bellingham) 2024; 11:044507. [PMID: 39119067 PMCID: PMC11304989 DOI: 10.1117/1.jmi.11.4.044507] [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: 11/28/2023] [Revised: 06/03/2024] [Accepted: 07/09/2024] [Indexed: 08/10/2024] Open
Abstract
Purpose Synthetic datasets hold the potential to offer cost-effective alternatives to clinical data, ensuring privacy protections and potentially addressing biases in clinical data. We present a method leveraging such datasets to train a machine learning algorithm applied as part of a computer-aided detection (CADe) system. Approach Our proposed approach utilizes clinically acquired computed tomography (CT) scans of a physical anthropomorphic phantom into which manufactured lesions were inserted to train a machine learning algorithm. We treated the training database obtained from the anthropomorphic phantom as a simplified representation of clinical data and increased the variability in this dataset using a set of randomized and parameterized augmentations. Furthermore, to mitigate the inherent differences between phantom and clinical datasets, we investigated adding unlabeled clinical data into the training pipeline. Results We apply our proposed method to the false positive reduction stage of a lung nodule CADe system in CT scans, in which regions of interest containing potential lesions are classified as nodule or non-nodule regions. Experimental results demonstrate the effectiveness of the proposed method; the system trained on labeled data from physical phantom scans and unlabeled clinical data achieves a sensitivity of 90% at eight false positives per scan. Furthermore, the experimental results demonstrate the benefit of the physical phantom in which the performance in terms of competitive performance metric increased by 6% when a training set consisting of 50 clinical CT scans was enlarged by the scans obtained from the physical phantom. Conclusions The scalability of synthetic datasets can lead to improved CADe performance, particularly in scenarios in which the size of the labeled clinical data is limited or subject to inherent bias. Our proposed approach demonstrates an effective utilization of synthetic datasets for training machine learning algorithms.
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Affiliation(s)
- Mohammad Mehdi Farhangi
- FDA, CDRH, OSEL, Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, Maryland, United States
| | - Michael Maynord
- FDA, CDRH, OSEL, Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, Maryland, United States
- University of Maryland, Iribe Center for Computer Science and Engineering, Computer Science Department, College Park, Maryland, United States
| | - Cornelia Fermüller
- University of Maryland, Iribe Center for Computer Science and Engineering, Computer Science Department, College Park, Maryland, United States
| | - Yiannis Aloimonos
- University of Maryland, Iribe Center for Computer Science and Engineering, Computer Science Department, College Park, Maryland, United States
| | - Berkman Sahiner
- FDA, CDRH, OSEL, Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, Maryland, United States
| | - Nicholas Petrick
- FDA, CDRH, OSEL, Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, Maryland, United States
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Varghese BA, Cen SY, Jensen K, Levy J, Andersen HK, Schulz A, Lei X, Duddalwar VA, Goodenough DJ. Technical and clinical considerations of a physical liver phantom for CT radiomics analysis. J Appl Clin Med Phys 2024; 25:e14309. [PMID: 38386922 PMCID: PMC11005983 DOI: 10.1002/acm2.14309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 01/17/2024] [Accepted: 02/03/2024] [Indexed: 02/24/2024] Open
Abstract
OBJECTIVE This study identifies key characteristics to help build a physical liver computed tomography (CT) phantom for radiomics harmonization; particularly, the higher-order texture metrics. MATERIALS AND METHODS CT scans of a radiomics phantom comprising of 18 novel 3D printed inserts with varying size, shape, and material combinations were acquired on a 64-slice CT scanner (Brilliance 64, Philips Healthcare). The images were acquired at 120 kV, 250 mAs, CTDIvol of 16.36 mGy, 2 mm slice thickness, and iterative noise-reduction reconstruction (iDose, Philips Healthcare, Andover, MA). Radiomics analysis was performed using the Cancer Imaging Phenomics Toolkit (CaPTk), following automated segmentation of 3D regions of interest (ROI) of the 18 inserts. The findings were compared to three additional ROI obtained of an anthropomorphic liver phantom, a patient liver CT scan, and a water phantom, at comparable imaging settings. Percentage difference in radiomic metrics values between phantom and tissue was used to assess the biological equivalency and <10% was used to claim equivalent. RESULTS The HU for all 18 ROI from the phantom ranged from -30 to 120 which is within clinically observed HU range of the liver, showing that our phantom material (T3-6B) is representative of biological CT tissue densities (liver) with >50% radiomic features having <10% difference from liver tissue. Based on the assessment of the Neighborhood Gray Tone Difference Matrix (NGTDM) metrics it is evident that the water phantom ROI show extreme values compared to the ROIs from the phantom. This result may further reinforce the difference between a structureless quantity such as water HU values and tissue HU values found in liver. CONCLUSION The 3-D printed patterns of the constructed radiomics phantom cover a wide span of liver tissue textures seen in CT images. Using our results, texture metrics can be selectively harmonized to establish clinically relevant and reliable radiomics panels.
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Affiliation(s)
- Bino Abel Varghese
- Department of RadiologyKeck Medical CenterUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Steven Yong Cen
- Department of RadiologyKeck Medical CenterUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Kristin Jensen
- Department of Physics and Computational RadiologyOslo University HospitalOsloNorway
| | | | | | - Anselm Schulz
- Department of Radiology and Nuclear MedicineOslo University HospitalOsloNorway
| | - Xiaomeng Lei
- Department of RadiologyKeck Medical CenterUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Vinay Anant Duddalwar
- Department of RadiologyKeck Medical CenterUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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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.
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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
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Assessment of the Carotid Bodies in Magnetic Resonance-A Head-to-Head Comparison with Computed Tomography. Diagnostics (Basel) 2023; 13:diagnostics13050993. [PMID: 36900137 PMCID: PMC10000419 DOI: 10.3390/diagnostics13050993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/28/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
OBJECTIVES To evaluate carotid body visibility in contrast-enhanced magnetic resonance (MR) studies and to compare the results to contrast-enhanced computed tomography (CT). METHODS Two observers separately evaluated MR and CT examinations of 58 patients. MR scans were acquired with contrast-enhanced isometric T1-weighted water-only Dixon sequence. CT examinations were performed 90 s after contrast agent administration. Carotid bodies' dimensions were noted and their volumes calculated. To quantify the agreement between both methods, Bland-Altman plots were computed. Receiver operating characteristic (ROC) and its localization-oriented variant (LROC) curves were plotted. RESULTS Of the 116 expected carotid bodies, 105 were found on CT and 103 on MR at least by a single observer. Significantly more findings were concordant in CT (92.2%) than in MR (83.6%). The mean carotid body volume was smaller in CT (19.4 mm3) than in MR (20.8 mm3). The inter-observer agreement on volumes was moderately good (ICC (2,k) 0.42, p < 0.001), but with significant systematic error. The diagnostic performance of the MR method added up to 88.4% of the ROC's area under the curve and 78.0% in the LROC algorithm. CONCLUSIONS Carotid bodies can be visualized on contrast-enhanced MR with good accuracy and inter-observer agreement. Carotid bodies assessed on MR had similar morphology as described in anatomical studies.
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Varghese BA, Hwang D, Cen SY, Lei X, Levy J, Desai B, Goodenough DJ, Duddalwar VA. Identification of robust and reproducible CT-texture metrics using a customized 3D-printed texture phantom. J Appl Clin Med Phys 2021; 22:98-107. [PMID: 33434374 PMCID: PMC7882093 DOI: 10.1002/acm2.13162] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 12/15/2020] [Accepted: 12/16/2020] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE The objective of this study was to evaluate the robustness and reproducibility of computed tomography-based texture analysis (CTTA) metrics extracted from CT images of a customized texture phantom built for assessing the association of texture metrics to three-dimensional (3D) printed progressively increasing textural heterogeneity. MATERIALS AND METHODS A custom-built 3D-printed texture phantom comprising of six texture patterns was used to evaluate the robustness and reproducibility of a radiomics panel under a variety of routine abdominal imaging protocols. The phantom was scanned on four CT scanners (Philips, Canon, GE, and Siemens) to assess reproducibility. The robustness assessment was conducted by imaging the texture phantom across different CT imaging parameters such as slice thickness, field of view (FOV), tube voltage, and tube current for each scanner. The texture panel comprised of 387 features belonging to 15 subgroups of texture extraction methods (e.g., Gray-level Co-occurrence Matrix: GLCM). Twelve unique image settings were tested on all the four scanners (e.g., FOV125). Interclass correlation two-way mixed with absolute agreement (ICC3) was used to assess the robustness and reproducibility of radiomic features. Linear regression was used to test the association between change in radiomic features and increased texture heterogeneity. Results were summarized in heat maps. RESULTS A total of 5612 (23.2%) of 24 090 features showed excellent robustness and reproducibility (ICC ≥ 0.9). Intensity, GLCM 3D, and gray-level run length matrix (GLRLM) 3D features showed best performance. Among imaging variables, changes in slice thickness affected all metrics more intensely compared to other imaging variables in reducing the ICC3. From the analysis of linear trend effect of the CTTA metrics, the top three metrics with high linear correlations across all scanners and scanning settings were from the GLRLM 2D/3D and discrete cosine transform (DCT) texture family. CONCLUSION The choice of scanner and imaging protocols affect texture metrics. Furthermore, not all CTTA metrics have a linear association with linearly varying texture patterns.
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Affiliation(s)
- Bino A. Varghese
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Darryl Hwang
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Steven Y. Cen
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Xiaomeng Lei
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCAUSA
| | | | - Bhushan Desai
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCAUSA
| | | | - Vinay A. Duddalwar
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCAUSA
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Radiologists and Clinical Trials: Part 1 The Truth About Reader Disagreements. Ther Innov Regul Sci 2021; 55:1111-1121. [PMID: 34228319 PMCID: PMC8259547 DOI: 10.1007/s43441-021-00316-6] [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] [Received: 10/11/2020] [Accepted: 06/18/2021] [Indexed: 02/06/2023]
Abstract
The debate over human visual perception and how medical images should be interpreted have persisted since X-rays were the only imaging technique available. Concerns over rates of disagreement between expert image readers are associated with much of the clinical research and at times driven by the belief that any image endpoint variability is problematic. The deeper understanding of the reasons, value, and risk of disagreement are somewhat siloed, leading, at times, to costly and risky approaches, especially in clinical trials. Although artificial intelligence promises some relief from mistakes, its routine application for assessing tumors within cancer trials is still an aspiration. Our consortium of international experts in medical imaging for drug development research, the Pharma Imaging Network for Therapeutics and Diagnostics (PINTAD), tapped the collective knowledge of its members to ground expectations, summarize common reasons for reader discordance, identify what factors can be controlled and which actions are likely to be effective in reducing discordance. Reinforced by an exhaustive literature review, our work defines the forces that shape reader variability. This review article aims to produce a singular authoritative resource outlining reader performance's practical realities within cancer trials, whether they occur within a clinical or an independent central review.
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7
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Rafael-Palou X, Aubanell A, Bonavita I, Ceresa M, Piella G, Ribas V, González Ballester MA. Re-Identification and growth detection of pulmonary nodules without image registration using 3D siamese neural networks. Med Image Anal 2020; 67:101823. [PMID: 33075637 DOI: 10.1016/j.media.2020.101823] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 09/11/2020] [Accepted: 09/22/2020] [Indexed: 10/23/2022]
Abstract
Lung cancer follow-up is a complex, error prone, and time consuming task for clinical radiologists. Several lung CT scan images taken at different time points of a given patient need to be individually inspected, looking for possible cancerogenous nodules. Radiologists mainly focus their attention in nodule size, density, and growth to assess the existence of malignancy. In this study, we present a novel method based on a 3D siamese neural network, for the re-identification of nodules in a pair of CT scans of the same patient without the need for image registration. The network was integrated into a two-stage automatic pipeline to detect, match, and predict nodule growth given pairs of CT scans. Results on an independent test set reported a nodule detection sensitivity of 94.7%, an accuracy for temporal nodule matching of 88.8%, and a sensitivity of 92.0% with a precision of 88.4% for nodule growth detection.
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Affiliation(s)
- Xavier Rafael-Palou
- Eurecat, Centre Tecnològic de Catalunya, eHealth Unit, Barcelona, Spain; BCN MedTech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | | | - Ilaria Bonavita
- Eurecat, Centre Tecnològic de Catalunya, eHealth Unit, Barcelona, Spain
| | - Mario Ceresa
- BCN MedTech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gemma Piella
- BCN MedTech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Vicent Ribas
- Eurecat, Centre Tecnològic de Catalunya, eHealth Unit, Barcelona, Spain
| | - Miguel A González Ballester
- BCN MedTech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; ICREA, Barcelona, Spain
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Cacciamani GE, Nassiri N, Varghese B, Maas M, King KG, Hwang D, Abreu A, Gill I, Duddalwar V. Radiomics and Bladder Cancer: Current Status. Bladder Cancer 2020. [DOI: 10.3233/blc-200293] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE: To systematically review the current literature and discuss the applications and limitations of radiomics and machine-learning augmented radiomics in the management of bladder cancer. METHODS: Pubmed ®, Scopus ®, and Web of Science ® databases were searched systematically for all full-text English-language articles assessing the impact of Artificial Intelligence OR Radiomics OR Machine Learning AND Bladder Cancer AND (staging OR grading OR prognosis) published up to January 2020. RESULTS: Of the 686 articles that were identified, 13 studies met the criteria for quantitative analysis. Staging, Grading and Tumor Classification, Prognosis, and Therapy Response were discussed in 7, 3, 2 and 7 studies, respectively. Data on cost of implementation were not reported. CT and MRI were the most common imaging approaches. CONCLUSION: Radiomics shows potential in bladder cancer detection, staging, grading, and response to therapy, thereby supporting the physician in personalizing patient management. Extension and validation of this promising technology in large multisite prospective trials is warranted to pave the way for its clinical translation.
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Affiliation(s)
- Giovanni E. Cacciamani
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Norris Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Nima Nassiri
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Bino Varghese
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marissa Maas
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kevin G. King
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Darryl Hwang
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Andre Abreu
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Inderbir Gill
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Vinay Duddalwar
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Norris Cancer Center, University of Southern California, Los Angeles, CA, USA
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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Hagiwara A, Fujita S, Ohno Y, Aoki S. Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence. Invest Radiol 2020; 55:601-616. [PMID: 32209816 PMCID: PMC7413678 DOI: 10.1097/rli.0000000000000666] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 01/28/2020] [Indexed: 12/19/2022]
Abstract
Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.
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Affiliation(s)
- Akifumi Hagiwara
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
| | | | - Yoshiharu Ohno
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Shigeki Aoki
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
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Gong Q, Li Q, Gavrielides MA, Petrick N. Data transformations for statistical assessment of quantitative imaging biomarkers: Application to lung nodule volumetry. Stat Methods Med Res 2020; 29:2749-2763. [PMID: 32133924 DOI: 10.1177/0962280220908619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Variance stabilization is an important step in the statistical assessment of quantitative imaging biomarkers. The objective of this study is to compare the Log and the Box-Cox transformations for variance stabilization in the context of assessing the performance of a particular quantitative imaging biomarker, the estimation of lung nodule volume from computed tomography images. First, a model is developed to generate and characterize repeated measurements typically observed in computed tomography lung nodule volume estimation. Given this model, we derive the parameter of the Box-Cox transformation that stabilizes the variance of the measurements across lung nodule volumes. Second, simulated, phantom, and clinical datasets are used to compare the Log and the Box-Cox transformations. Two metrics are used for quantifying the stability of the measurements across the transformed lung nodule volumes: the coefficient of variation for the standard deviation and the repeatability coefficient. The results for simulated, phantom, and clinical datasets show that the Box-Cox transformation generally had better variance stabilization performance compared to the Log transformation for lung nodule volume estimates from computed tomography scans.
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Affiliation(s)
- Qi Gong
- US Food and Drug Administration, CDRH/OSEL/DIDSR, Silver Spring, MD, USA
| | - Qin Li
- US Food and Drug Administration, CDRH/OSEL/DIDSR, Silver Spring, MD, USA
| | | | - Nicholas Petrick
- US Food and Drug Administration, CDRH/OSEL/DIDSR, Silver Spring, MD, USA
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11
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Gavrielides MA, Li Q, Zeng R, Berman BP, Sahiner B, Gong Q, Myers KJ, DeFilippo G, Petrick N. Discrimination of Pulmonary Nodule Volume Change for Low- and High-contrast Tasks in a Phantom CT Study with Low-dose Protocols. Acad Radiol 2019; 26:937-948. [PMID: 30292564 DOI: 10.1016/j.acra.2018.09.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 08/30/2018] [Accepted: 09/09/2018] [Indexed: 12/20/2022]
Abstract
RATIONALE AND OBJECTIVES The quantitative assessment of volumetric CT for discriminating small changes in nodule size has been under-examined. This phantom study examined the effect of imaging protocol, nodule size, and measurement method on volume-based change discrimination across low and high object to background contrast tasks. MATERIALS AND METHODS Eight spherical objects ranging in diameter from 5.0 mm to 5.75 mm and 8.0 mm to 8.75 mm with 0.25 mm increments were scanned within an anthropomorphic phantom with either foam-background (high-contrast task, ∼1000 HU object to background difference)) or gelatin-background (low-contrast task, ∼50 to 100 HU difference). Ten repeat acquisitions were collected for each protocol with varying exposures, reconstructed slice thicknesses and reconstruction kernels. Volume measurements were obtained using a matched-filter approach (MF) and a publicly available 3D segmentation-based tool (SB). Discrimination of nodule sizes was assessed using the area under the ROC curve (AUC). RESULTS Using a low-dose (1.3 mGy), thin-slice (≤1.5 mm) protocol, changes of 0.25 mm in diameter were detected with AU = 1.0 for all baseline sizes for the high-contrast task regardless of measurement method. For the more challenging low-contrast task and same protocol, MF detected changes of 0.25 mm from baseline sizes ≥5.25 mm and volume changes ≥9.4% with AUC≥0.81 whereas corresponding results for SB were poor (AUC within 0.49-0.60). Performance for SB was improved, but still inconsistent, when exposure was increased to 4.4 mGy. CONCLUSION The reliable discrimination of small changes in pulmonary nodule size with low-dose, thin-slice CT protocols suitable for lung cancer screening was dependent on the inter-related effects of nodule to background contrast and measurement method.
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Lu L, Liang Y, Schwartz LH, Zhao B. Reliability of Radiomic Features Across Multiple Abdominal CT Image Acquisition Settings: A Pilot Study Using ACR CT Phantom. Tomography 2019; 5:226-231. [PMID: 30854461 PMCID: PMC6403036 DOI: 10.18383/j.tom.2019.00005] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
We studied the reliability of radiomic features on abdominal computed tomography (CT) images reconstructed with multiple CT image acquisition settings using the ACR (American College of Radiology) CT Phantom. Twenty-four sets of CT images of the ACR CT phantom were attained from a GE Discovery 750HD scanner using 24 different image acquisition settings, combinations of 4 tube currents (25, 50, 100, 200 Effective mAs), 3 slice thicknesses (1.25, 2.5, 5 mm), and 2 convolution kernels (STANDARD and SOFT). Polyethylene (-95 HU) and acrylic (120 HU) of the phantom model were selected for calculating real feature value; a noise-free, computer-generated phantom image series that reproduced the 2 objects and the background was used for calculating reference feature value. Feature reliability was defined as the degree of predicting reference feature value from real feature value. Radiomic features mean, std, skewness, kurtosis, gray-level co-occurrence matrix (GLCM)-energy, GLCM-contrast, GLCM-correlation, GLCM-homogeneity were investigated. The value of R2 ≥ 0.85 was considered to be of high reliability. The reliability of mean and std were high across all image acquisition settings. At 200 Effective mAs, all features except GLCM-homogeneity showed high reliability, whereas at 25 Effective mAs, most features (except mean and std) showed low reliability. From high to low, reliability was ranked in the following order: mean, std, skewness, kurtosis, GLCM-energy, correlation, contrast and homogeneity. CT image acquisition settings affected the reliability of radiomic features. High reliable features were attained from images reconstructed at high tube current and thick slice thickness.
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Affiliation(s)
- Lin Lu
- Department of Radiology, Columbia University Medical Center, New York, NY
| | - Yongguang Liang
- Department of Radiology, Columbia University Medical Center, New York, NY
| | | | - Binsheng Zhao
- Department of Radiology, Columbia University Medical Center, New York, NY
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13
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Ohno Y, Koyama H, Seki S, Kishida Y, Yoshikawa T. Radiation dose reduction techniques for chest CT: Principles and clinical results. Eur J Radiol 2018; 111:93-103. [PMID: 30691672 DOI: 10.1016/j.ejrad.2018.12.017] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 12/06/2018] [Accepted: 12/16/2018] [Indexed: 11/19/2022]
Abstract
Computer tomography plays a major role in the evaluation of thoracic diseases, especially since the advent of the multidetector-row CT (MDCT) technology. However, the increase use of this technique has raised some concerns about the resulting radiation dose. In this review, we will present the various methods allowing limiting the radiation dose exposure resulting from chest CT acquisitions, including the options of image filtering and iterative reconstruction (IR) algorithms. The clinical applications of reduced dose protocols will be reviewed, especially for lung nodule detection and diagnosis of pulmonary thromboembolism. The performance of reduced dose protocols for infiltrative lung disease assessment will also be discussed. Lastly, the influence of using IR algorithms on computer-aided detection and volumetry of lung nodules, as well as on quantitative and functional assessment of chest diseases will be presented and discussed.
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Affiliation(s)
- Yoshiharu Ohno
- Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Japan; Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Japan.
| | | | - Shinichiro Seki
- Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Japan; Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Japan
| | - Yuji Kishida
- Division of Radiology, Department of Radiology, Kobe University Graduate School of Medicine, Japan
| | - Takeshi Yoshikawa
- Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Japan; Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Japan
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14
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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).
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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
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15
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Gavrielides MA, Berman BP, Supanich M, Schultz K, Li Q, Petrick N, Zeng R, Siegelman J. Quantitative assessment of nonsolid pulmonary nodule volume with computed tomography in a phantom study. Quant Imaging Med Surg 2017; 7:623-635. [PMID: 29312867 DOI: 10.21037/qims.2017.12.07] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background To assess the volumetric measurement of small (≤1 cm) nonsolid nodules with computed tomography (CT), focusing on the interaction of state of the art iterative reconstruction (IR) methods and dose with nodule densities, sizes, and shapes. Methods Twelve synthetic nodules [5 and 10 mm in diameter, densities of -800, -630 and -10 Hounsfield units (HU), spherical and spiculated shapes] were scanned within an anthropomorphic phantom. Dose [computed tomography scan dose index (CTDIvol)] ranged from standard (4.1 mGy) to below screening levels (0.3 mGy). Data was reconstructed using filtered back-projection and two state-of-the-art IR methods (adaptive and model-based). Measurements were extracted with a previously validated matched filter-based estimator. Analysis of accuracy and precision was based on evaluation of percent bias (PB) and the repeatability coefficient (RC) respectively. Results Density had the most important effect on measurement error followed by the interaction of density with nodule size. The nonsolid -630 HU nodules had high accuracy and precision at levels comparable to solid (-10 HU) nonsolid, regardless of reconstruction method and with CTDIvol as low as 0.6 mGy. PB was <5% and <11% for the 10- and 5-mm in nominal diameter -630 HU nodules respectively, and RC was <5% and <12% for the same nodules. For nonsolid -800 HU nodules, PB increased to <11% and <30% for the 10- and 5-mm nodules respectively, whereas RC increased slightly overall but varied widely across dose and reconstruction algorithms for the 5-mm nodules. Model-based IR improved measurement accuracy for the 5-mm, low-density (-800, -630 HU) nodules. For other nodules the effect of reconstruction method was small. Dose did not affect volumetric accuracy and only affected slightly the precision of 5-mm nonsolid nodules. Conclusions Reasonable values of both accuracy and precision were achieved for volumetric measurements of all 10-mm nonsolid nodules, and for the 5-mm nodules with -630 HU or higher density, when derived from scans acquired with below screening dose levels as low as 0.6 mGy and regardless of reconstruction algorithm.
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Affiliation(s)
- Marios A Gavrielides
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, , Office of In Vitro Diagnostics and Radiological Health, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Benjamin P Berman
- Division of Radiological Health, Office of In Vitro Diagnostics and Radiological Health, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Mark Supanich
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, Illinois, USA
| | - Kurt Schultz
- Toshiba Medical Research Institute USA, Inc., Center for Medical Research and Development, Illinois, USA
| | - Qin Li
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, , Office of In Vitro Diagnostics and Radiological Health, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Nicholas Petrick
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, , Office of In Vitro Diagnostics and Radiological Health, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Rongping Zeng
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, , Office of In Vitro Diagnostics and Radiological Health, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Jenifer Siegelman
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachussetts, USA
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16
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Zhou W, Montoya J, Gutjahr R, Ferrero A, Halaweish A, Kappler S, McCollough C, Leng S. Lung nodule volume quantification and shape differentiation with an ultra-high resolution technique on a photon-counting detector computed tomography system. J Med Imaging (Bellingham) 2017; 4:043502. [PMID: 29181429 DOI: 10.1117/1.jmi.4.4.043502] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 11/01/2017] [Indexed: 01/07/2023] Open
Abstract
An ultra-high resolution (UHR) mode, with a detector pixel size of [Formula: see text] relative to isocenter, has been implemented on a whole body research photon-counting detector (PCD) computed tomography (CT) system. Twenty synthetic lung nodules were scanned using UHR and conventional resolution (macro) modes and reconstructed with medium and very sharp kernels. Linear regression was used to compare measured nodule volumes from CT images to reference volumes. The full-width-at-half-maximum of the calculated curvature histogram for each nodule was used as a shape index, and receiver operating characteristic analysis was performed to differentiate sphere- and star-shaped nodules. Results showed a strong linear relationship between measured nodule volumes and reference volumes for both modes. The overall volume estimation was more accurate using UHR mode and the very sharp kernel, having 4.8% error compared with 10.5% to 12.6% error in the macro mode. The improvement in volume measurements using the UHR mode was more evident for small nodule sizes or star-shaped nodules. Images from the UHR mode with the very sharp kernel consistently demonstrated the best performance [[Formula: see text]] for separating star- from sphere-shaped nodules, showing advantages of UHR mode on a PCD CT scanner for lung nodule characterization.
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Affiliation(s)
- Wei Zhou
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Juan Montoya
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Ralf Gutjahr
- Technical University of Munich, CAMP, Garching (Munich), Germany.,Siemens Healthcare, Forchheim, Germany
| | - Andrea Ferrero
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | | | | | - Cynthia McCollough
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Shuai Leng
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
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17
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Devaraj A, van Ginneken B, Nair A, Baldwin D. Use of Volumetry for Lung Nodule Management: Theory and Practice. Radiology 2017; 284:630-644. [DOI: 10.1148/radiol.2017151022] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Anand Devaraj
- From the Department of Radiology, Royal Brompton Hospital, Sydney St, London SW3 6NP, England (A.D.); Department of of Radiology and Nuclear Medicine, Radboud UMC, Nijmegen, the Netherlands (B.v.G.); Department of Radiology, Guy’s & St Thomas’ NHS Foundation Trust, London, England (A.N.); and Department of Respiratory Medicine, Nottingham University Hospitals and University of Nottingham, Nottingham, England
| | - Bram van Ginneken
- From the Department of Radiology, Royal Brompton Hospital, Sydney St, London SW3 6NP, England (A.D.); Department of of Radiology and Nuclear Medicine, Radboud UMC, Nijmegen, the Netherlands (B.v.G.); Department of Radiology, Guy’s & St Thomas’ NHS Foundation Trust, London, England (A.N.); and Department of Respiratory Medicine, Nottingham University Hospitals and University of Nottingham, Nottingham, England
| | - Arjun Nair
- From the Department of Radiology, Royal Brompton Hospital, Sydney St, London SW3 6NP, England (A.D.); Department of of Radiology and Nuclear Medicine, Radboud UMC, Nijmegen, the Netherlands (B.v.G.); Department of Radiology, Guy’s & St Thomas’ NHS Foundation Trust, London, England (A.N.); and Department of Respiratory Medicine, Nottingham University Hospitals and University of Nottingham, Nottingham, England
| | - David Baldwin
- From the Department of Radiology, Royal Brompton Hospital, Sydney St, London SW3 6NP, England (A.D.); Department of of Radiology and Nuclear Medicine, Radboud UMC, Nijmegen, the Netherlands (B.v.G.); Department of Radiology, Guy’s & St Thomas’ NHS Foundation Trust, London, England (A.N.); and Department of Respiratory Medicine, Nottingham University Hospitals and University of Nottingham, Nottingham, England
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18
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Li Q, Liang Y, Huang Q, Zong M, Berman B, Gavrielides MA, Schwartz LH, Zhao B, Petrick N. Volumetry of low-contrast liver lesions with CT: Investigation of estimation uncertainties in a phantom study. Med Phys 2017; 43:6608. [PMID: 27908157 DOI: 10.1118/1.4967776] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To evaluate the performance of lesion volumetry in hepatic CT as a function of various imaging acquisition parameters. METHODS An anthropomorphic abdominal phantom with removable liver inserts was designed for this study. Two liver inserts, each containing 19 synthetic lesions with varying diameter (6-40 mm), shape, contrast (10-65 HU), and both homogenous and mixed-density were designed to have background and lesion CT values corresponding to arterial and portal-venous phase imaging, respectively. The two phantoms were scanned using two commercial CT scanners (GE 750 HD and Siemens Biograph mCT) across a set of imaging protocols (four slice thicknesses, three effective mAs, two convolution kernels, two pitches). Two repeated scans were collected for each imaging protocol. All scans were analyzed using a matched-filter estimator for volume estimation, resulting in 6080 volume measurements across all of the synthetic lesions in the two liver phantoms. A subset of portal venous phase scans was also analyzed using a semi-automatic segmentation algorithm, resulting in about 900 additional volume measurements. Lesions associated with large measurement error (quantified by root mean square error) for most imaging protocols were considered not measurable by the volume estimation tools and excluded for the statistical analyses. Imaging protocols were grouped into distinct imaging conditions based on ANOVA analysis of factors for repeatability testing. Statistical analyses, including overall linearity analysis, grouped bias analysis with standard deviation evaluation, and repeatability analysis, were performed to assess the accuracy and precision of the liver lesion volume biomarker. RESULTS Lesions with lower contrast and size ≤10 mm were associated with higher measurement error and were excluded from further analysis. Lesion size, contrast, imaging slice thickness, dose, and scanner were found to be factors substantially influencing volume estimation. Twenty-four distinct repeatable imaging conditions were determined as protocols for each scanner with a fixed slice thickness and dose. For the matched-filter estimation approach, strong linearity was observed for all imaging data for lesions ≥20 mm. For the Siemens scanner with 50 mAs effective dose at 0.6 mm slice thickness, grouped bias was about -10%. For all other repeatable imaging conditions with both scanners, grouped biases were low (-3%-3%). There was a trend of increasing standard deviation with decreasing dose. For each fixed dose, the standard deviations were similar among the three larger slice thicknesses (1.25, 2.5, 5 mm for GE, 1.5, 3, 5 mm for Siemens). Repeatability coefficients ranged from about 8% to 75% and showed similar trend to grouped standard deviation. For the segmentation approach, the results led to similar conclusions for both lesion characteristic factors and imaging factors but with increasing magnitude in all the error metrics assessed. CONCLUSIONS Results showed that liver lesion volumetry was strongly dependent on lesion size, contrast, acquisition dose, and their interactions. The overall performances were similar for images reconstructed with larger slice thicknesses, clinically used pitches, kernels, and doses. Conditions that yielded repeatable measurements were identified and they agreed with the Quantitative Imaging Biomarker Alliance's (QIBA) profile requirements in general. The authors' findings also suggest potential refinements to these guidelines for the tumor volume biomarker, especially for soft-tissue lesions.
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Affiliation(s)
- Qin Li
- Division of Imaging, Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland 20993
| | - Yongguang Liang
- Department of Radiology, Columbia University Medical Center, New York, New York 10032
| | - Qiao Huang
- Department of Radiology, Columbia University Medical Center, New York, New York 10032
| | - Min Zong
- Department of Radiology, Columbia University Medical Center, New York, New York 10032
| | - Benjamin Berman
- Division of Imaging, Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland 20993
| | - Marios A Gavrielides
- Division of Imaging, Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland 20993
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Medical Center, New York, New York 10032
| | - Binsheng Zhao
- Department of Radiology, Columbia University Medical Center, New York, New York 10032
| | - Nicholas Petrick
- Division of Imaging, Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland 20993
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Jha AK, Frey E. No-gold-standard evaluation of image-acquisition methods using patient data. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2017; 10136. [PMID: 28596636 DOI: 10.1117/12.2255902] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Several new and improved modalities, scanners, and protocols, together referred to as image-acquisition methods (IAMs), are being developed to provide reliable quantitative imaging. Objective evaluation of these IAMs on the clinically relevant quantitative tasks is highly desirable. Such evaluation is most reliable and clinically decisive when performed with patient data, but that requires the availability of a gold standard, which is often rare. While no-gold-standard (NGS) techniques have been developed to clinically evaluate quantitative imaging methods, these techniques require that each of the patients be scanned using all the IAMs, which is expensive, time consuming, and could lead to increased radiation dose. A more clinically practical scenario is where different set of patients are scanned using different IAMs. We have developed an NGS technique that uses patient data where different patient sets are imaged using different IAMs to compare the different IAMs. The technique posits a linear relationship, characterized by a slope, bias, and noise standard-deviation term, between the true and measured quantitative values. Under the assumption that the true quantitative values have been sampled from a unimodal distribution, a maximum-likelihood procedure was developed that estimates these linear relationship parameters for the different IAMs. Figures of merit can be estimated using these linear relationship parameters to evaluate the IAMs on the basis of accuracy, precision, and overall reliability. The proposed technique has several potential applications such as in protocol optimization, quantifying difference in system performance, and system harmonization using patient data.
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Affiliation(s)
- Abhinav K Jha
- Department of Radiology, Johns Hopkins University, Baltimore, MD USA
| | - Eric Frey
- Department of Radiology, Johns Hopkins University, Baltimore, MD USA
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20
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Han D, Heuvelmans MA, Oudkerk M. Volume versus diameter assessment of small pulmonary nodules in CT lung cancer screening. Transl Lung Cancer Res 2017; 6:52-61. [PMID: 28331824 DOI: 10.21037/tlcr.2017.01.05] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Currently, lung cancer screening by low-dose chest CT is implemented in the United States for high-risk persons. A disadvantage of lung cancer screening is the large number of small-to-intermediate sized lung nodules, detected in around 50% of all participants, the large majority being benign. Accurate estimation of nodule size and growth is essential in the classification of lung nodules. Currently, manual diameter measurements are the standard for lung cancer screening programs and routine clinical care. However, European screening studies using semi-automated volume measurements have shown higher accuracy and reproducibility compared to diameter measurements. In addition to this, with the optimization of CT scan techniques and reconstruction parameters, as well as advances in segmentation software, the accuracy of nodule volume measurement can be improved even further. The positive results of previous studies on volume and diameter measurements of lung nodules suggest that manual measurements of nodule diameter may be replaced by semi-automated volume measurements in the (near) future.
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Affiliation(s)
- Daiwei Han
- University of Groningen, University Medical Center Groningen, Center for Medical Imaging-North East Netherlands, Groningen, the Netherlands
| | - Marjolein A Heuvelmans
- University of Groningen, University Medical Center Groningen, Center for Medical Imaging-North East Netherlands, Groningen, the Netherlands
| | - Matthijs Oudkerk
- University of Groningen, University Medical Center Groningen, Center for Medical Imaging-North East Netherlands, Groningen, the Netherlands
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21
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Comparative evaluation of newly developed model-based and commercially available hybrid-type iterative reconstruction methods and filter back projection method in terms of accuracy of computer-aided volumetry (CADv) for low-dose CT protocols in phantom study. Eur J Radiol 2016; 85:1375-82. [PMID: 27423675 DOI: 10.1016/j.ejrad.2016.05.001] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 05/03/2016] [Accepted: 05/04/2016] [Indexed: 12/21/2022]
Abstract
PURPOSE To directly compare the capability of three reconstruction methods using, respectively, forward projected model-based iterative reconstruction (FIRST), adaptive iterative dose reduction using three dimensional processing (AIDR 3D) and filter back projection (FBP) for radiation dose reduction and accuracy of computer-aided volumetry (CADv) measurements on chest CT examination in a phantom study. MATERIALS AND METHODS An anthropomorphic thoracic phantom with 30 simulated nodules of three density types (100, -630, and -800 HU) and five different diameters was scanned with an area-detector CT at tube currents of 270, 200, 120, 80, 40, 20, and 10mA. Each scanned data set was reconstructed as thin-section CT with three methods, and all simulated nodules were measured with CADv software. For comparison of the capability for CADv at each tube current, Tukey's HSD test was used to compare the percentage of absolute measurement errors for all three reconstruction methods. Absolute percentage measurement errors were then compared by means of Dunett's test for each tube current at 270mA (standard tube current). RESULTS Mean absolute measurement errors of AIDR 3D and FIRST methods for each nodule type were significantly lower than those of the FBP method at 20mA and 10mA (p<0.05). In addition, absolute measurement errors of the FBP method at 20mA and 10mA was significantly higher than that at 270mA for all nodule types (p<0.05). CONCLUSION The FIRST and AIDR 3D methods are more effective than the FBP method for radiation dose reduction, while yielding better measurement accuracy of CADv for chest CT examination.
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22
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Gavrielides MA, Li Q, Zeng R, Myers KJ, Sahiner B, Petrick N. Volume estimation of multidensity nodules with thoracic computed tomography. J Med Imaging (Bellingham) 2016; 3:013504. [PMID: 26844235 DOI: 10.1117/1.jmi.3.1.013504] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2015] [Accepted: 12/18/2015] [Indexed: 11/14/2022] Open
Abstract
This work focuses on volume estimation of "multidensity" lung nodules in a phantom computed tomography study. Eight objects were manufactured by enclosing spherical cores within larger spheres of double the diameter but with a different density. Different combinations of outer-shell/inner-core diameters and densities were created. The nodules were placed within an anthropomorphic phantom and scanned with various acquisition and reconstruction parameters. The volumes of the entire multidensity object as well as the inner core of the object were estimated using a model-based volume estimator. Results showed percent volume bias across all nodules and imaging protocols with slice thicknesses [Formula: see text] ranging from [Formula: see text] to 6.6% for the entire object (standard deviation ranged from 1.5% to 7.6%), and within [Formula: see text] to 5.7% for the inner-core measurement (standard deviation ranged from 2.0% to 17.7%). Overall, the estimation error was larger for the inner-core measurements, which was expected due to the smaller size of the core. Reconstructed slice thickness was found to substantially affect volumetric error for both tasks; exposure and reconstruction kernel were not. These findings provide information for understanding uncertainty in volumetry of nodules that include multiple densities such as ground glass opacities with a solid component.
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Affiliation(s)
- Marios A Gavrielides
- U.S. Food and Drug Administration , Division of Imaging, Diagnostics, and Software Reliability (DIDSR), Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, 10903 New Hampshire Avenue, Building 62, Room 4126, Silver Spring, Maryland 20993, United States
| | - Qin Li
- U.S. Food and Drug Administration , Division of Imaging, Diagnostics, and Software Reliability (DIDSR), Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, 10903 New Hampshire Avenue, Building 62, Room 4126, Silver Spring, Maryland 20993, United States
| | - Rongping Zeng
- U.S. Food and Drug Administration , Division of Imaging, Diagnostics, and Software Reliability (DIDSR), Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, 10903 New Hampshire Avenue, Building 62, Room 4126, Silver Spring, Maryland 20993, United States
| | - Kyle J Myers
- U.S. Food and Drug Administration , Division of Imaging, Diagnostics, and Software Reliability (DIDSR), Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, 10903 New Hampshire Avenue, Building 62, Room 4126, Silver Spring, Maryland 20993, United States
| | - Berkman Sahiner
- U.S. Food and Drug Administration , Division of Imaging, Diagnostics, and Software Reliability (DIDSR), Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, 10903 New Hampshire Avenue, Building 62, Room 4126, Silver Spring, Maryland 20993, United States
| | - Nicholas Petrick
- U.S. Food and Drug Administration , Division of Imaging, Diagnostics, and Software Reliability (DIDSR), Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, 10903 New Hampshire Avenue, Building 62, Room 4126, Silver Spring, Maryland 20993, United States
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