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Qaiser T, Lee CY, Vandenberghe M, Yeh J, Gavrielides MA, Hipp J, Scott M, Reischl J. Usability of deep learning and H&E images predict disease outcome-emerging tool to optimize clinical trials. NPJ Precis Oncol 2022; 6:37. [PMID: 35705792 PMCID: PMC9200764 DOI: 10.1038/s41698-022-00275-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 04/27/2022] [Indexed: 11/24/2022] Open
Abstract
Understanding factors that impact prognosis for cancer patients have high clinical relevance for treatment decisions and monitoring of the disease outcome. Advances in artificial intelligence (AI) and digital pathology offer an exciting opportunity to capitalize on the use of whole slide images (WSIs) of hematoxylin and eosin (H&E) stained tumor tissue for objective prognosis and prediction of response to targeted therapies. AI models often require hand-delineated annotations for effective training which may not be readily available for larger data sets. In this study, we investigated whether AI models can be trained without region-level annotations and solely on patient-level survival data. We present a weakly supervised survival convolutional neural network (WSS-CNN) approach equipped with a visual attention mechanism for predicting overall survival. The inclusion of visual attention provides insights into regions of the tumor microenvironment with the pathological interpretation which may improve our understanding of the disease pathomechanism. We performed this analysis on two independent, multi-center patient data sets of lung (which is publicly available data) and bladder urothelial carcinoma. We perform univariable and multivariable analysis and show that WSS-CNN features are prognostic of overall survival in both tumor indications. The presented results highlight the significance of computational pathology algorithms for predicting prognosis using H&E stained images alone and underpin the use of computational methods to improve the efficiency of clinical trial studies.
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Affiliation(s)
- Talha Qaiser
- Precision Medicine and Biosamples, Oncology R&D, AstraZeneca, Cambridge, UK.
| | | | | | - Joe Yeh
- AetherAI, Taipei City, Taiwan
| | | | - Jason Hipp
- Early Oncology, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Marietta Scott
- Precision Medicine and Biosamples, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Joachim Reischl
- Precision Medicine and Biosamples, Oncology R&D, AstraZeneca, Cambridge, UK
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Gavrielides MA, Miller M, Hagemann IS, Abdelal H, Alipour Z, Chen JF, Salari B, Sun L, Zhou H, Seidman JD. Clinical Decision Support for Ovarian Carcinoma Subtype Classification: A Pilot Observer Study With Pathology Trainees. Arch Pathol Lab Med 2021; 144:869-877. [PMID: 31816269 DOI: 10.5858/arpa.2019-0390-oa] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/16/2019] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Clinical decision support (CDS) systems could assist less experienced pathologists with certain diagnostic tasks for which subspecialty training or extensive experience is typically needed. The effect of decision support on pathologist performance for such diagnostic tasks has not been examined. OBJECTIVE.— To examine the impact of a CDS tool for the classification of ovarian carcinoma subtypes by pathology trainees in a pilot observer study using digital pathology. DESIGN.— Histologic review on 90 whole slide images from 75 ovarian cancer patients was conducted by 6 pathology residents using: (1) unaided review of whole slide images, and (2) aided review, where in addition to whole slide images observers used a CDS tool that provided information about the presence of 8 histologic features important for subtype classification that were identified previously by an expert in gynecologic pathology. The reference standard of ovarian subtype consisted of majority consensus from a panel of 3 gynecologic pathology experts. RESULTS.— Aided review improved pairwise concordance with the reference standard for 5 of 6 observers by 3.3% to 17.8% (for 2 observers, increase was statistically significant) and mean interobserver agreement by 9.2% (not statistically significant). Observers benefited the most when the CDS tool prompted them to look for missed histologic features that were definitive for a certain subtype. Observer performance varied widely across cases with unanimous and nonunanimous reference classification, supporting the need for balancing data sets in terms of case difficulty. CONCLUSIONS.— Findings showed the potential of CDS systems to close the knowledge gap between pathologists for complex diagnostic tasks.
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Affiliation(s)
- Marios A Gavrielides
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Meghan Miller
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Ian S Hagemann
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Heba Abdelal
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Zahra Alipour
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Jie-Fu Chen
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Behzad Salari
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Lulu Sun
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Huifang Zhou
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Jeffrey D Seidman
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
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Petrick N, Akbar S, Cha KH, Nofech-Mozes S, Sahiner B, Gavrielides MA, Kalpathy-Cramer J, Drukker K, Martel AL. SPIE-AAPM-NCI BreastPathQ challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment. J Med Imaging (Bellingham) 2021; 8:034501. [PMID: 33987451 PMCID: PMC8107263 DOI: 10.1117/1.jmi.8.3.034501] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 04/13/2021] [Indexed: 12/20/2022] Open
Abstract
Purpose: The breast pathology quantitative biomarkers (BreastPathQ) challenge was a grand challenge organized jointly by the International Society for Optics and Photonics (SPIE), the American Association of Physicists in Medicine (AAPM), the U.S. National Cancer Institute (NCI), and the U.S. Food and Drug Administration (FDA). The task of the BreastPathQ challenge was computerized estimation of tumor cellularity (TC) in breast cancer histology images following neoadjuvant treatment. Approach: A total of 39 teams developed, validated, and tested their TC estimation algorithms during the challenge. The training, validation, and testing sets consisted of 2394, 185, and 1119 image patches originating from 63, 6, and 27 scanned pathology slides from 33, 4, and 18 patients, respectively. The summary performance metric used for comparing and ranking algorithms was the average prediction probability concordance (PK) using scores from two pathologists as the TC reference standard. Results: Test PK performance ranged from 0.497 to 0.941 across the 100 submitted algorithms. The submitted algorithms generally performed well in estimating TC, with high-performing algorithms obtaining comparable results to the average interrater PK of 0.927 from the two pathologists providing the reference TC scores. Conclusions: The SPIE-AAPM-NCI BreastPathQ challenge was a success, indicating that artificial intelligence/machine learning algorithms may be able to approach human performance for cellularity assessment and may have some utility in clinical practice for improving efficiency and reducing reader variability. The BreastPathQ challenge can be accessed on the Grand Challenge website.
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Affiliation(s)
- Nicholas Petrick
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, Maryland, United States
| | - Shazia Akbar
- University of Toronto, Medical Biophysics, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Kenny H. Cha
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, Maryland, United States
| | - Sharon Nofech-Mozes
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- University of Toronto, Department of Laboratory Medicine and Pathobiology, Toronto, Ontario, Canada
| | - Berkman Sahiner
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, Maryland, United States
| | - Marios A. Gavrielides
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, Maryland, United States
| | | | - Karen Drukker
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Anne L. Martel
- University of Toronto, Medical Biophysics, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
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Gavrielides MA, Ronnett BM, Vang R, Sheikhzadeh F, Seidman JD. Selection of Representative Histologic Slides in Interobserver Reproducibility Studies: Insights from Expert Review for Ovarian Carcinoma Subtype Classification. J Pathol Inform 2021; 12:15. [PMID: 34012719 PMCID: PMC8112350 DOI: 10.4103/jpi.jpi_56_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 09/02/2020] [Accepted: 10/28/2020] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND Observer studies in pathology often utilize a limited number of representative slides per case, selected and reported in a nonstandardized manner. Reference diagnoses are commonly assumed to be generalizable to all slides of a case. We examined these issues in the context of pathologist concordance for histologic subtype classification of ovarian carcinomas (OCs). MATERIALS AND METHODS A cohort of 114 OCs consisting of 72 cases with a single representative slide (Group 1) and 42 cases with multiple representative slides (148 slides, 2-6 sections per case, Group 2) was independently reviewed by three experts in gynecologic pathology (case-based review). In a follow-up study, each individual slide was independently reviewed in a randomized order by the same pathologists (section-based review). RESULTS Average interobserver concordance varied from 100% for Group 1 to 64.3% for Group 2 (86.8% across all cases). Across Group 2, 19 cases (45.2%) had at least one slide classified as a different subtype than the subtype assigned from case-based review, demonstrating the impact of intratumoral heterogeneity. Section-based concordance across individual sections from Group 2 was comparable to case-based concordance for those cases indicating diagnostic challenges at the individual section level. Findings demonstrate the increased diagnostic complexity of heterogeneous tumors that require multiple section sampling and its impact on pathologist performance. CONCLUSIONS The proportion of cases with multiple representative slides in cohorts used in validation studies, such as those conducted to evaluate artificial intelligence/machine learning tools, can influence diagnostic performance, and if not accounted for, can cause disparities between research and real-world observations and between research studies. Case selection in validation studies should account for tumor heterogeneity to create balanced datasets in terms of diagnostic complexity.
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Affiliation(s)
- Marios A. Gavrielides
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA, (Currently at AstraZeneca, Precision Medicine and Biosamples, Gaithersburg, Maryland, USA)
| | - Brigitte M. Ronnett
- Department of Pathology and Gynecology and Obstetrics, The Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Russell Vang
- Department of Pathology and Gynecology and Obstetrics, The Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Fahime Sheikhzadeh
- Electrical and Computer Engineering Department, University of British Columbia, Vancouver, Canada, (Currently at Roche Diagnostics, San Francisco, California, USA)
| | - Jeffrey D Seidman
- Division of Molecular Genetics and Pathology, Office of In Vitro Diagnostics and Radiological Health, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
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Gavrielides MA, Ronnett BM, Vang R, Barak S, Lee E, Staats PN, Jenson E, Skaria P, Sheikhzadeh F, Miller M, Hagemann IS, Petrick N, Seidman JD. Pathologist Concordance for Ovarian Carcinoma Subtype Classification and Identification of Relevant Histologic Features Using Microscope and Whole Slide Imaging: A Multisite Observer Study. Arch Pathol Lab Med 2021; 145:1516-1525. [PMID: 33635941 DOI: 10.5858/arpa.2020-0579-oa] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/09/2020] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Despite several studies focusing on the validation of whole slide imaging (WSI) across organ systems or subspecialties, the use of WSI for specific primary diagnosis tasks has been underexamined. OBJECTIVE.— To assess pathologist performance for the histologic subtyping of individual sections of ovarian carcinomas using the light microscope and WSI. DESIGN.— A panel of 3 experienced gynecologic pathologists provided reference subtype diagnoses for 212 histologic sections from 109 ovarian carcinomas based on optical microscopy review. Two additional attending pathologists provided diagnoses and also identified the presence of a set of 8 histologic features important for ovarian tumor subtyping. Two experienced gynecologic pathologists and 2 fellows reviewed the corresponding WSI images for subtype classification and feature identification. RESULTS.— Across pathologists specialized in gynecologic pathology, concordance with the reference diagnosis for the 5 major ovarian carcinoma subtypes was significantly higher for a pathologist reading on microscope than each of 2 pathologists reading on WSI. Differences were primarily due to more frequent classification of mucinous carcinomas as endometrioid with WSI. Pathologists had generally low agreement in identifying histologic features important to ovarian tumor subtype classification, with either optical microscopy or WSI. This result suggests the need for refined histologic criteria for identifying such features. Interobserver agreement was particularly low for identifying intracytoplasmic mucin with WSI. Inconsistencies in evaluating nuclear atypia and mitoses with WSI were also observed. CONCLUSIONS.— Further research is needed to specify the reasons for these diagnostic challenges and to inform users and manufacturers of WSI technology.
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Affiliation(s)
- Marios A Gavrielides
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories (Gavrielides and Petrick)
| | - Brigitte M Ronnett
- the Departments of Pathology and Gynecology & Obstetrics, The Johns Hopkins Hospital, Baltimore, Maryland (Ronnett, Vang, Jenson)
| | - Russell Vang
- the Departments of Pathology and Gynecology & Obstetrics, The Johns Hopkins Hospital, Baltimore, Maryland (Ronnett, Vang, Jenson)
| | - Stephanie Barak
- the Department of Pathology, The George Washington University, Washington, District of Columbia (Barak, Lee)
| | - Elsie Lee
- Gavrielides is currently at AstraZeneca, Gaithersburg, Maryland.,the Department of Pathology, The George Washington University, Washington, District of Columbia (Barak, Lee)
| | - Paul N Staats
- the Department of Pathology, University of Maryland School of Medicine, Baltimore (Staats)
| | - Erik Jenson
- Lee is currently at HNL Lab Medicine, Allentown, Pennsylvania.,the Departments of Pathology and Gynecology & Obstetrics, The Johns Hopkins Hospital, Baltimore, Maryland (Ronnett, Vang, Jenson)
| | - Priya Skaria
- the Departments of Pathology and Immunology (Skaria and Hagemann), Washington University School of Medicine, St Louis, Missouri
| | - Fahime Sheikhzadeh
- Jenson is now with Hospital Pathology Associates, Minneapolis/St Paul, Minnesota.,the Electrical and Computer Engineering Department, University of British Columbia, Vancouver, Canada (Sheikhzadeh)
| | - Meghan Miller
- and the Department of Bioengineering, University of Maryland, College Park (Miller)
| | - Ian S Hagemann
- the Departments of Pathology and Immunology (Skaria and Hagemann), Washington University School of Medicine, St Louis, Missouri.,and Obstetrics and Gynecology (Hagemann), Washington University School of Medicine, St Louis, Missouri
| | - Nicholas Petrick
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories (Gavrielides and Petrick)
| | - Jeffrey D Seidman
- and the Division of Molecular Genetics and Pathology, Office of In Vitro Diagnostics and Radiological Health (Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [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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Li Q, Berman BP, Hagio T, Gavrielides MA, Zeng R, Sahiner B, Gong Q, Fang Y, Liu S, Petrick N. Coronary artery calcium quantification using contrast-enhanced dual-energy computed tomography scans in comparison with unenhanced single-energy scans. Phys Med Biol 2018; 63:175006. [PMID: 30101756 PMCID: PMC6183065 DOI: 10.1088/1361-6560/aad9be] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Extracting coronary artery calcium (CAC) scores from contrast-enhanced computed tomography (CT) images using dual-energy (DE) based material decomposition has been shown feasible, mainly through patient studies. However, the quantitative performance of such DE-based CAC scores, particularly per stenosis, is underexamined due to lack of reference standard and repeated scans. In this work we conducted a comprehensive quantitative comparative analysis of CAC scores obtained with DE and compare to conventional unenhanced single-energy (SE) CT scans through phantom studies. Synthetic vessels filled with iodinated blood mimicking material and containing calcium stenoses of different sizes and densities were scanned with a third generation dual-source CT scanner in a chest phantom using a DE coronary CT angiography protocol with three exposures/CTDIvol: auto-mAs/8 mGy (automatic exposure), 160 mAs/20 mGy and 260 mAs/34 mGy and 10 repeats. As a control, a set of vessel phantoms without iodine was scanned using a standard SE CAC score protocol (3 mGy). Calcium volume, mass and Agatston scores were estimated for each stenosis. For DE dataset, image-based three-material decomposition was applied to remove iodine before scoring. Performance of DE-based calcium scores were analyzed on a per-stenosis level and compared to SE-based scores. There was excellent correlation between the DE- and SE-based scores (correlation coefficient r: 0.92-0.98). Percent bias for the calcium volume and mass scores varied as a function of stenosis size and density for both modalities. Precision (coefficient of variation) improved with larger and denser stenoses for both DE- and SE-based calcium scores. DE-based scores (20 mGy and 34 mGy) provided comparable per-stenosis precision to SE-based (3 mGy). Our findings suggest that on a per-stenosis level, DE-based CAC scores from contrast-enhanced CT images can achieve comparable quantification performance to conventional SE-based scores. However, DE-based CAC scoring required more dose compared with SE for high per-stenosis precision so some caution is necessary with clinical DE-based CAC scoring.
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Affiliation(s)
- Qin Li
- US Food and Drug Administration, CDRH/OSEL/DIDSR, Silver Spring, MD, United States of America
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Li Q, Liu S, Myers KJ, Gavrielides MA, Zeng R, Sahiner B, Petrick N. Impact of Reconstruction Algorithms and Gender-Associated Anatomy on Coronary Calcium Scoring with CT: An Anthropomorphic Phantom Study. Acad Radiol 2016; 23:1470-1479. [PMID: 27665673 PMCID: PMC5567798 DOI: 10.1016/j.acra.2016.08.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Revised: 07/20/2016] [Accepted: 08/01/2016] [Indexed: 10/21/2022]
Abstract
RATIONALE AND OBJECTIVES Different computed tomography imaging protocols and patient characteristics can impact the accuracy and precision of the calcium score and may lead to inconsistent patient treatment recommendations. The aim of this work was to determine the impact of reconstruction algorithm and gender characteristics on coronary artery calcium scoring based on a phantom study using computed tomography. MATERIALS AND METHODS Four synthetic heart vessels with vessel diameters corresponding to female and male left main and left circumflex arteries containing calcification-mimicking materials (200-1000 HU) were inserted into a thorax phantom and were scanned with and without female breast plates (male and female phantoms, respectively). Ten scans were acquired and were reconstructed at 3-mm slices using filtered-back projection (FBP) and iterative reconstruction with medium and strong denoising (IR3 and IR5) algorithms. Agatston and calcium volume scores were estimated for each vessel. Calcium scores for each vessel and the total calcium score (summation of all four vessels) were compared between the two phantoms to quantify the impact of the breast plates and reconstruction parameters. Calcium scores were also compared among vessels of different diameters to investigate the impact of the vessel size. RESULTS The calcium scores were significantly larger for FBP reconstruction (FBP > IR3>IR5). Agatston scores (calcium volume score) for vessels in the male phantom scans were on average 4.8% (2.9%), 8.2% (7.1%), and 10.5% (9.4%) higher compared to those in the female phantom with FBP, IR3, and IR5, respectively, when exposure was conserved across phantoms. The total calcium scores from the male phantom were significantly larger than those from the female phantom (P <0.05). In general, calcium volume scores were underestimated (up to about 50%) for smaller vessels, especially when scanned in the female phantom. CONCLUSIONS Calcium scores significantly decreased with iterative reconstruction and tended to be underestimated for female anatomy (smaller vessels and presence of breast plates).
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Affiliation(s)
- Qin Li
- U.S. Food and Drug Administration, CDRH/OSEL/DIDSR, 10903 New Hampshire Ave., Bldg. 62 Rm. 4110, Silver Spring, MD 20993.
| | - Songtao Liu
- U.S. Food and Drug Administration, CDRH/OIR/DRH, Silver Spring, Maryland
| | - Kyle J Myers
- U.S. Food and Drug Administration, CDRH/OSEL/DIDSR, 10903 New Hampshire Ave., Bldg. 62 Rm. 4110, Silver Spring, MD 20993
| | - Marios A Gavrielides
- U.S. Food and Drug Administration, CDRH/OSEL/DIDSR, 10903 New Hampshire Ave., Bldg. 62 Rm. 4110, Silver Spring, MD 20993
| | - Rongping Zeng
- U.S. Food and Drug Administration, CDRH/OSEL/DIDSR, 10903 New Hampshire Ave., Bldg. 62 Rm. 4110, Silver Spring, MD 20993
| | - Berkman Sahiner
- U.S. Food and Drug Administration, CDRH/OSEL/DIDSR, 10903 New Hampshire Ave., Bldg. 62 Rm. 4110, Silver Spring, MD 20993
| | - Nicholas Petrick
- U.S. Food and Drug Administration, CDRH/OSEL/DIDSR, 10903 New Hampshire Ave., Bldg. 62 Rm. 4110, Silver Spring, MD 20993
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Athelogou M, Kim HJ, Dima A, Obuchowski N, Peskin A, Gavrielides MA, Petrick N, Saiprasad G, Colditz Colditz D, Beaumont H, Oubel E, Tan Y, Zhao B, Kuhnigk JM, Moltz JH, Orieux G, Gillies RJ, Gu Y, Mantri N, Goldmacher G, Zhang L, Vega E, Bloom M, Jarecha R, Soza G, Tietjen C, Takeguchi T, Yamagata H, Peterson S, Masoud O, Buckler AJ. Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge. Acad Radiol 2016; 23:940-52. [PMID: 27215408 DOI: 10.1016/j.acra.2016.02.018] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Revised: 02/29/2016] [Accepted: 02/29/2016] [Indexed: 11/19/2022]
Abstract
RATIONALE AND OBJECTIVES Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA). MATERIALS AND METHODS The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type. RESULTS Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall. CONCLUSION The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria.
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Affiliation(s)
| | - Hyun J Kim
- UCLA, Center for Computer Vision and Imaging Biomarkers, Dept. of Radiological Sciences David Geffen School of Medicine at UCLA Dept. of Biostatistics Fielding School of Public at UCLA, Los Angeles, USA
| | - Alden Dima
- National Institute of Standards and Technology, Gaithersburg, USA
| | - Nancy Obuchowski
- Quantitative Health Sciences/JJN3, Cleveland Clinic Foundation, Cleveland, USA
| | - Adele Peskin
- National Institute of Standards and Technology, Gaithersburg, USA
| | | | | | - Ganesh Saiprasad
- National Institute of Standards and Technology, Gaithersburg, USA
| | | | | | | | - Yongqiang Tan
- Columbia University Medical Center, Department of Radiology, New York, USA
| | - Binsheng Zhao
- Columbia University Medical Center, Department of Radiology, New York, USA
| | - Jan-Martin Kuhnigk
- Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, Germany
| | - Jan Hendrik Moltz
- Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, Germany
| | | | - Robert J Gillies
- Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Yuhua Gu
- Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Ninad Mantri
- ICON Medical Imaging, Warrington, Pennsylvania, USA
| | | | | | - Emilio Vega
- NYU Langone Medical Center Faculty Practice Radiology, New York, USA
| | - Michael Bloom
- NYU Langone Medical Center Faculty Practice Radiology, New York, USA
| | | | - Grzegorz Soza
- Siemens AG, Healthcare Sector, Computed Tomography, Forchheim, Germany
| | - Christian Tietjen
- Siemens AG, Healthcare Sector, Computed Tomography, Forchheim, Germany
| | | | - Hitoshi Yamagata
- Toshiba Corporation, Toshiba Medical Systems Corporation, Otawara, Japan
| | - Sam Peterson
- Vital Images, Inc. (a Toshiba Medical Systems Group), Minnesota, USA
| | - Osama Masoud
- Vital Images, Inc. (a Toshiba Medical Systems Group), Minnesota, USA
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Li Q, Gavrielides MA, Sahiner B, Myers KJ, Zeng R, Petrick N. Statistical analysis of lung nodule volume measurements with CT in a large-scale phantom study. Med Phys 2016; 42:3932-47. [PMID: 26133594 DOI: 10.1118/1.4921734] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To determine inter-related factors that contribute substantially to measurement error of pulmonary nodule measurements with CT by assessing a large-scale dataset of phantom scans and to quantitatively validate the repeatability and reproducibility of a subset containing nodules and CT acquisitions consistent with the Quantitative Imaging Biomarker Alliance (QIBA) metrology recommendations. METHODS The dataset has about 40 000 volume measurements of 48 nodules (5-20 mm, four shapes, three radiodensities) estimated by a matched-filter estimator from CT images involving 72 imaging protocols. Technical assessment was performed under a framework suggested by QIBA, which aimed to minimize the inconsistency of terminologies and techniques used in the literature. Accuracy and precision of lung nodule volume measurements were examined by analyzing the linearity, bias, variance, root mean square error (RMSE), repeatability, reproducibility, and significant and substantial factors that contribute to the measurement error. Statistical methodologies including linear regression, analysis of variance, and restricted maximum likelihood were applied to estimate the aforementioned metrics. The analysis was performed on both the whole dataset and a subset meeting the criteria proposed in the QIBA Profile document. RESULTS Strong linearity was observed for all data. Size, slice thickness × collimation, and randomness in attachment to vessels or chest wall were the main sources of measurement error. Grouping the data by nodule size and slice thickness × collimation, the standard deviation (3.9%-28%), and RMSE (4.4%-68%) tended to increase with smaller nodule size and larger slice thickness. For 5, 8, 10, and 20 mm nodules with reconstruction slice thickness ≤0.8, 3, 3, and 5 mm, respectively, the measurements were almost unbiased (-3.0% to 3.0%). Repeatability coefficients (RCs) were from 6.2% to 40%. Pitch of 0.9, detail kernel, and smaller slice thicknesses yielded better (smaller) RCs than those from pitch of 1.2, medium kernel, and larger slice thicknesses. Exposure showed no impact on RC. The overall reproducibility coefficient (RDC) was 45%, and reduced to about 20%-30% when the slice thickness and collimation were fixed. For nodules and CT imaging complying with the QIBA Profile (QIBA Profile subset), the measurements were highly repeatable and reproducible in spite of variations in nodule characteristics and imaging protocols. The overall measurement error was small and mostly due to the randomness in attachment. The bias, standard deviation, and RMSE grouped by nodule size and slice thickness × collimation in the QIBA Profile subset were within ±3%, 4%, and 5%, respectively. RCs are within 11% and the overall RDC is equal to 11%. CONCLUSIONS The authors have performed a comprehensive technical assessment of lung nodule volumetry with a matched-filter estimator from CT scans of synthetic nodules and identified the main sources of measurement error among various nodule characteristics and imaging parameters. The results confirm that the QIBA Profile set is highly repeatable and reproducible. These phantom study results can serve as a bound on the clinical performance achievable with volumetric CT measurements of pulmonary nodules.
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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
| | - 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
| | - Berkman Sahiner
- 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
| | - Kyle J Myers
- 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
| | - Rongping Zeng
- 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
| | - 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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Zeng R, Gavrielides MA, Petrick N, Sahiner B, Li Q, Myers KJ. Estimating local noise power spectrum from a few FBP-reconstructed CT scans. Med Phys 2016; 43:568. [DOI: 10.1118/1.4939061] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
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Buckler AJ, Danagoulian J, Johnson K, Peskin A, Gavrielides MA, Petrick N, Obuchowski NA, Beaumont H, Hadjiiski L, Jarecha R, Kuhnigk JM, Mantri N, McNitt-Gray M, Moltz JH, Nyiri G, Peterson S, Tervé P, Tietjen C, von Lavante E, Ma X, St Pierre S, Athelogou M. Inter-Method Performance Study of Tumor Volumetry Assessment on Computed Tomography Test-Retest Data. Acad Radiol 2015; 22:1393-408. [PMID: 26376841 PMCID: PMC4609285 DOI: 10.1016/j.acra.2015.08.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Revised: 07/31/2015] [Accepted: 08/07/2015] [Indexed: 11/20/2022]
Abstract
RATIONALE AND OBJECTIVES Tumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semiautomated lung tumor volume measurement algorithms from clinical thoracic computed tomography data sets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Computed Tomography Volumetry Profile. MATERIALS AND METHODS Industry and academic groups participated in a challenge study. Intra-algorithm repeatability and inter-algorithm reproducibility were estimated. Relative magnitudes of various sources of variability were estimated using a linear mixed effects model. Segmentation boundaries were compared to provide a basis on which to optimize algorithm performance for developers. RESULTS Intra-algorithm repeatability ranged from 13% (best performing) to 100% (least performing), with most algorithms demonstrating improved repeatability as the tumor size increased. Inter-algorithm reproducibility was determined in three partitions and was found to be 58% for the four best performing groups, 70% for the set of groups meeting repeatability requirements, and 84% when all groups but the least performer were included. The best performing partition performed markedly better on tumors with equivalent diameters greater than 40 mm. Larger tumors benefitted by human editing but smaller tumors did not. One-fifth to one-half of the total variability came from sources independent of the algorithms. Segmentation boundaries differed substantially, not ony in overall volume but also in detail. CONCLUSIONS Nine of the 12 participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the present study was not designed to explicitly evaluate algorithm profile conformance. Change in tumor volume can be measured with confidence to within ±14% using any of these nine algorithms on tumor sizes greater than 10 mm. No partition of the algorithms was able to meet the QIBA requirements for interchangeability down to 10 mm, although the partition comprising best performing algorithms did meet this requirement for a tumor size of greater than approximately 40 mm.
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Affiliation(s)
| | | | | | - Adele Peskin
- National Institute of Standards and Technology, Boulder, Colorado
| | | | | | | | | | - Lubomir Hadjiiski
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Rudresh Jarecha
- Perceptive Informatics, Sundew Properties SEZ Pvt Ltd Mindspace, Hyderabad, Andhra Pradesh, India
| | - Jan-Martin Kuhnigk
- Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, Germany
| | | | - Michael McNitt-Gray
- Department of Radiology, University of California at Los Angeles, Los Angeles, California
| | - Jan H Moltz
- Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, Germany
| | | | | | | | - Christian Tietjen
- Siemens AG, Healthcare Sector, Imaging and Therapy Division, Forchheim, Germany
| | | | - Xiaonan Ma
- Elucid Bioimaging Inc., 225 Main Street, Wenham, MA 01984
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Treanor D, Gallas BD, Gavrielides MA, Hewitt SM. Evaluating whole slide imaging: A working group opportunity. J Pathol Inform 2015; 6:4. [PMID: 25774315 PMCID: PMC4355829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2014] [Accepted: 10/27/2014] [Indexed: 10/26/2022] Open
Affiliation(s)
- Darren Treanor
- Leeds Teaching Hospitals NHS Trust and University of Leeds, Leeds, England
| | - Brandon D. Gallas
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Marios A. Gavrielides
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Stephen M. Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, USA,Corresponding author
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Li Q, Gavrielides MA, Zeng R, Myers KJ, Sahiner B, Petrick N. Volume estimation of low-contrast lesions with CT: a comparison of performances from a phantom study, simulations and theoretical analysis. Phys Med Biol 2015; 60:671-88. [PMID: 25555240 DOI: 10.1088/0031-9155/60/2/671] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Measurements of lung nodule volume with multi-detector computed tomography (MDCT) have been shown to be more accurate and precise compared to conventional lower dimensional measurements. Quantifying the size of lesions is potentially more difficult when the object-to-background contrast is low as with lesions in the liver. Physical phantom and simulation studies are often utilized to analyze the bias and variance of lesion size estimates because a ground truth or reference standard can be established. In addition, it may also be useful to derive theoretical bounds as another way of characterizing lesion sizing methods. The goal of this work was to study the performance of a MDCT system for a lesion volume estimation task with object-to-background contrast less than 50 HU, and to understand the relation among performances obtained from phantom study, simulation and theoretical analysis. We performed both phantom and simulation studies, and analyzed the bias and variance of volume measurements estimated by a matched-filter-based estimator. We further corroborated results with a theoretical analysis to estimate the achievable performance bound, which was the Cramer-Rao's lower bound (CRLB) of minimum variance for the size estimates. Results showed that estimates of non-attached solid small lesion volumes with object-to-background contrast of 31-46 HU can be accurate and precise, with less than 10.8% in percent bias and 4.8% in standard deviation of percent error (SPE), in standard dose scans. These results are consistent with theoretical (CRLB), computational (simulation) and empirical phantom bounds. The difference between the bounds is rather small (for SPE less than 1.9%) indicating that the theoretical- and simulation-based performance bounds can be good surrogates for physical phantom studies.
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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, MD 20993, USA
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Gallas BD, Gavrielides MA, Conway CM, Ivansky A, Keay TC, Cheng WC, Hipp J, Hewitt SM. Evaluation environment for digital and analog pathology: a platform for validation studies. J Med Imaging (Bellingham) 2014; 1:037501. [PMID: 26158076 DOI: 10.1117/1.jmi.1.3.037501] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2014] [Revised: 10/10/2014] [Accepted: 10/13/2014] [Indexed: 11/14/2022] Open
Abstract
We present a platform for designing and executing studies that compare pathologists interpreting histopathology of whole slide images (WSIs) on a computer display to pathologists interpreting glass slides on an optical microscope. eeDAP is an evaluation environment for digital and analog pathology. The key element in eeDAP is the registration of the WSI to the glass slide. Registration is accomplished through computer control of the microscope stage and a camera mounted on the microscope that acquires real-time images of the microscope field of view (FOV). Registration allows for the evaluation of the same regions of interest (ROIs) in both domains. This can reduce or eliminate disagreements that arise from pathologists interpreting different areas and focuses on the comparison of image quality. We reduced the pathologist interpretation area from an entire glass slide (10 to [Formula: see text]) to small ROIs ([Formula: see text]). We also made possible the evaluation of individual cells. We summarize eeDAP's software and hardware and provide calculations and corresponding images of the microscope FOV and the ROIs extracted from the WSIs. The eeDAP software can be downloaded from the Google code website (project: eeDAP) as a MATLAB source or as a precompiled stand-alone license-free application.
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Affiliation(s)
- Brandon D Gallas
- FDA/CDRH/OSEL , Division of Imaging, Diagnostics, and Software Reliability, 10903 New Hampshire Avenue, Building 62, Room 3124, Silver Spring, Maryland 20993-0002, United States
| | - Marios A Gavrielides
- FDA/CDRH/OSEL , Division of Imaging, Diagnostics, and Software Reliability, 10903 New Hampshire Avenue, Building 62, Room 3124, Silver Spring, Maryland 20993-0002, United States
| | - Catherine M Conway
- National Cancer Institute , National Institutes of Health, Center for Cancer Research, Laboratory of Pathology, 10 Center Drive, MSC 1500, Bethesda, Maryland 20892, United States
| | - Adam Ivansky
- FDA/CDRH/OSEL , Division of Imaging, Diagnostics, and Software Reliability, 10903 New Hampshire Avenue, Building 62, Room 3124, Silver Spring, Maryland 20993-0002, United States
| | - Tyler C Keay
- FDA/CDRH/OSEL , Division of Imaging, Diagnostics, and Software Reliability, 10903 New Hampshire Avenue, Building 62, Room 3124, Silver Spring, Maryland 20993-0002, United States
| | - Wei-Chung Cheng
- FDA/CDRH/OSEL , Division of Imaging, Diagnostics, and Software Reliability, 10903 New Hampshire Avenue, Building 62, Room 3124, Silver Spring, Maryland 20993-0002, United States
| | - Jason Hipp
- National Cancer Institute , National Institutes of Health, Center for Cancer Research, Laboratory of Pathology, 10 Center Drive, MSC 1500, Bethesda, Maryland 20892, United States
| | - Stephen M Hewitt
- National Cancer Institute , National Institutes of Health, Center for Cancer Research, Laboratory of Pathology, 10 Center Drive, MSC 1500, Bethesda, Maryland 20892, United States
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Gallas BD, Cheng WC, Gavrielides MA, Ivansky A, Keay T, Wunderlich A, Hipp J, Hewitt SM. eeDAP: An Evaluation Environment for Digital and Analog Pathology. Proc SPIE Int Soc Opt Eng 2014; 9037:903709. [PMID: 28845079 PMCID: PMC5568810 DOI: 10.1117/12.2044443] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
PURPOSE The purpose of this work is to present a platform for designing and executing studies that compare pathologists interpreting histopathology of whole slide images (WSI) on a computer display to pathologists interpreting glass slides on an optical microscope. METHODS Here we present eeDAP, an evaluation environment for digital and analog pathology. The key element in eeDAP is the registration of the WSI to the glass slide. Registration is accomplished through computer control of the microscope stage and a camera mounted on the microscope that acquires images of the real time microscope view. Registration allows for the evaluation of the same regions of interest (ROIs) in both domains. This can reduce or eliminate disagreements that arise from pathologists interpreting different areas and focuses the comparison on image quality. RESULTS We reduced the pathologist interpretation area from an entire glass slide (≈10-30 mm)2 to small ROIs <(50 um)2. We also made possible the evaluation of individual cells. CONCLUSIONS We summarize eeDAP's software and hardware and provide calculations and corresponding images of the microscope field of view and the ROIs extracted from the WSIs. These calculations help provide a sense of eeDAP's functionality and operating principles, while the images provide a sense of the look and feel of studies that can be conducted in the digital and analog domains. The eeDAP software can be downloaded from code.google.com (project: eeDAP) as Matlab source or as a precompiled stand-alone license-free application.
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Affiliation(s)
- Brandon D Gallas
- Division of Imaging and Applied Mathematics, OSEL/CDRH/FDA, Silver Spring, MD
| | - Wei-Chung Cheng
- Division of Imaging and Applied Mathematics, OSEL/CDRH/FDA, Silver Spring, MD
| | | | - Adam Ivansky
- Division of Imaging and Applied Mathematics, OSEL/CDRH/FDA, Silver Spring, MD
| | - Tyler Keay
- Division of Imaging and Applied Mathematics, OSEL/CDRH/FDA, Silver Spring, MD
| | - Adam Wunderlich
- Division of Imaging and Applied Mathematics, OSEL/CDRH/FDA, Silver Spring, MD
| | - Jason Hipp
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Stephen M Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
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Petrick N, Kim HJG, Clunie D, Borradaile K, Ford R, Zeng R, Gavrielides MA, McNitt-Gray MF, Lu ZQJ, Fenimore C, Zhao B, Buckler AJ. Comparison of 1D, 2D, and 3D nodule sizing methods by radiologists for spherical and complex nodules on thoracic CT phantom images. Acad Radiol 2014; 21:30-40. [PMID: 24331262 DOI: 10.1016/j.acra.2013.09.020] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2013] [Revised: 09/23/2013] [Accepted: 09/25/2013] [Indexed: 01/11/2023]
Abstract
RATIONALE AND OBJECTIVES To estimate and statistically compare the bias and variance of radiologists measuring the size of spherical and complex synthetic nodules. MATERIALS AND METHODS This study did not require the institutional review board approval. Six radiologists estimated the size of 10 synthetic nodules embedded within an anthropomorphic thorax phantom from computed tomography scans at 0.8- and 5-mm slice thicknesses. The readers measured the nodule size using unidimensional (1D) longest in-slice dimension, bidimensional (2D) area from longest in-slice and longest perpendicular dimension, and three-dimensional (3D) semiautomated volume. Intercomparisons of bias (difference between average and true size) and variance among methods were performed after converting the 2D and 3D estimates to a compatible 1D scale. RESULTS The relative biases of radiologists with the 3D tool were -1.8%, -0.4%, -0.7%, -0.4%, and -1.6% for 10-mm spherical, 20-mm spherical, 20-mm elliptical, 10-mm lobulated, and 10-mm spiculated nodules compared to 1.4%, -0.1%, -26.5%, -7.8%, and -39.8% for 1D. The three-dimensional measurements were significantly less biased than 1D for elliptical, lobulated, and spiculated nodules. The relative standard deviations for 3D were 7.5%, 3.9%, 3.6%, 9.7%, and 8.3% compared to 5.7%, 2.6%, 20.3%, 5.3%, and 16.4% for 1D. Unidimensional sizing was significantly less variable than 3D for the lobulated nodule and significantly more variable for the ellipsoid and spiculated nodules. Three-dimensional bias and variability were smaller for thin 0.8-mm slice data compared to thick 5.0-mm data. CONCLUSIONS The study shows that radiologist-controlled 3D volumetric lesion sizing can not only achieve smaller bias but also achieve similar or smaller variability compared to 1D sizing, especially for complex lesion shapes.
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Gavrielides MA, Li Q, Zeng R, Myers KJ, Sahiner B, Petrick N. Minimum detectable change in lung nodule volume in a phantom CT study. Acad Radiol 2013; 20:1364-70. [PMID: 24119348 DOI: 10.1016/j.acra.2013.08.019] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2013] [Revised: 08/28/2013] [Accepted: 08/29/2013] [Indexed: 11/30/2022]
Abstract
RATIONALE AND OBJECTIVES The change in volume of lung nodules is being examined as a measure of response to treatment. The aim of this study was to determine the minimum detectable change in nodule volume with the use of computed tomography. MATERIALS AND METHODS Four different layouts of synthetic nodules with different shapes but with the same size (5, 8, 9, or 10 mm) for each layout were placed within an anthropomorphic phantom and scanned with a 16-detector-row computed tomography scanner using multiple imaging parameters. Nodule volume estimates were determined using a previously developed matched-filter estimator. Analysis of volume change was then conducted as a detection problem. For each nodule size, the pooled distribution of volume estimates was shifted by a percentage c to simulate a changing nodule, while accounting for standard deviation. The value of c resulting in a prespecified area under the receiver operating characteristic curve (AUC) was deemed the minimum detectable change for that AUC value. RESULTS Both nodule size at baseline and choice of slice collimation protocol had an effect on the value of minimum detectable growth. For AUC = 0.95, the minimum detectable nodule growth in volume when using the thin-slice collimation protocol (16 × 0.75 mm) was 17%, 19%, and 15% for nodule sizes of 5, 8, and 9 mm, respectively. CONCLUSIONS Our results indicate that an approximate bound for detectable nodule growth in subcentimeter nodules may be relatively small, on the order of 20% or less in volume for a thin-slice CT acquisition protocol.
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Affiliation(s)
- Marios A Gavrielides
- Division of Imaging and Applied Mathematics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Bldg. 62, Rm.4114, Silver Spring, MD 20993.
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Keay T, Conway CM, O'Flaherty N, Hewitt SM, Shea K, Gavrielides MA. Reproducibility in the automated quantitative assessment of HER2/neu for breast cancer. J Pathol Inform 2013; 4:19. [PMID: 23967384 PMCID: PMC3746414 DOI: 10.4103/2153-3539.115879] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2013] [Accepted: 06/04/2013] [Indexed: 11/16/2022] Open
Abstract
Background: With the emerging role of digital imaging in pathology and the application of automated image-based algorithms to a number of quantitative tasks, there is a need to examine factors that may affect the reproducibility of results. These factors include the imaging properties of whole slide imaging (WSI) systems and their effect on the performance of quantitative tools. This manuscript examines inter-scanner and inter-algorithm variability in the assessment of the commonly used HER2/neu tissue-based biomarker for breast cancer with emphasis on the effect of algorithm training. Materials and Methods: A total of 241 regions of interest from 64 breast cancer tissue glass slides were scanned using three different whole-slide images and were analyzed using two different automated image analysis algorithms, one with preset parameters and another incorporating a procedure for objective parameter optimization. Ground truth from a panel of seven pathologists was available from a previous study. Agreement analysis was used to compare the resulting HER2/neu scores. Results: The results of our study showed that inter-scanner agreement in the assessment of HER2/neu for breast cancer in selected fields of view when analyzed with any of the two algorithms examined in this study was equal or better than the inter-observer agreement previously reported on the same set of data. Results also showed that discrepancies observed between algorithm results on data from different scanners were significantly reduced when the alternative algorithm that incorporated an objective re-training procedure was used, compared to the commercial algorithm with preset parameters. Conclusion: Our study supports the use of objective procedures for algorithm training to account for differences in image properties between WSI systems.
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Affiliation(s)
- Tyler Keay
- Division of Imaging and Applied Mathematics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
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Gavrielides MA, Zeng R, Myers KJ, Sahiner B, Petrick N. Benefit of overlapping reconstruction for improving the quantitative assessment of CT lung nodule volume. Acad Radiol 2013; 20:173-80. [PMID: 23085408 DOI: 10.1016/j.acra.2012.08.014] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2012] [Revised: 08/13/2012] [Accepted: 08/21/2012] [Indexed: 11/19/2022]
Abstract
RATIONALE AND OBJECTIVES The aim of this study was to quantify the effect of overlapping reconstruction on the precision and accuracy of lung nodule volume estimates in a phantom computed tomographic (CT) study. MATERIALS AND METHODS An anthropomorphic phantom was used with a vasculature insert on which synthetic lung nodules were attached. Repeated scans of the phantom were acquired using a 64-slice CT scanner. Overlapping and contiguous reconstructions were performed for a range of CT imaging parameters (exposure, slice thickness, pitch, reconstruction kernel) and a range of nodule characteristics (size, density). Nodule volume was estimated with a previously developed matched-filter algorithm. RESULTS Absolute percentage bias across all nodule sizes (n = 2880) was significantly lower when overlapping reconstruction was used, with an absolute percentage bias of 6.6% (95% confidence interval [CI], 6.4-6.9), compared to 13.2% (95% CI, 12.7-13.8) for contiguous reconstruction. Overlapping reconstruction also showed a precision benefit, with a lower standard percentage error of 7.1% (95% CI, 6.9-7.2) compared with 15.3% (95% CI, 14.9-15.7) for contiguous reconstructions across all nodules. Both effects were more pronounced for the smaller, subcentimeter nodules. CONCLUSIONS These results support the use of overlapping reconstruction to improve the quantitative assessment of nodule size with CT imaging.
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Affiliation(s)
- Marios A Gavrielides
- Division of Imaging and Applied Mathematics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, 10903 New Hampshire Avenue, Building 62, Room 4114, Silver Spring, MD 20993, USA.
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Keller B, Chen W, Gavrielides MA. Quantitative assessment and classification of tissue-based biomarker expression with color content analysis. Arch Pathol Lab Med 2012; 136:539-50. [PMID: 22540303 DOI: 10.5858/arpa.2011-0195-oa] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
CONTEXT The use of computer aids has been suggested as a way to reduce interobserver variability that is known to exist in the interpretation of immunohistochemical staining in pathology. Such computer aids should be automated in their usage but also they should be trained in an automated and reproducible fashion. OBJECTIVE To present a computer aid for the quantitative analysis of tissue-based biomarkers, based on color content analysis. DESIGN The developed system incorporates an automated algorithm to allow retraining based on the color properties of different training sets. The algorithm first generates a color palette containing the colors present in a training subset. Based on the palette, color histograms are derived and are used as feature vectors to a pattern recognition system, which returns an output proportional to biomarker continuous expression or a categorical classification. The method was evaluated on a database of HER2/neu digital breast cancer slides, for which expression scores from a pathologist panel were available. The system was retrained and evaluated on different transformations of the database, including compression, blurring, and changes in illumination, to examine its robustness to different imaging conditions frequently met in digital pathology. RESULTS Results showed high agreement between the results of the algorithm and the truth from the pathologist panel as well as robustness to image transformations. CONCLUSIONS The results of the study are encouraging for the potential of this method as a computer aid to assess biomarker expression in a consistent and reproducible manner.
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Affiliation(s)
- Brad Keller
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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Zeng R, Petrick N, Gavrielides MA, Myers KJ. Approximations of noise covariance in multi-slice helical CT scans: impact on lung nodule size estimation. Phys Med Biol 2011; 56:6223-42. [PMID: 21896963 DOI: 10.1088/0031-9155/56/19/005] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Multi-slice computed tomography (MSCT) scanners have become popular volumetric imaging tools. Deterministic and random properties of the resulting CT scans have been studied in the literature. Due to the large number of voxels in the three-dimensional (3D) volumetric dataset, full characterization of the noise covariance in MSCT scans is difficult to tackle. However, as usage of such datasets for quantitative disease diagnosis grows, so does the importance of understanding the noise properties because of their effect on the accuracy of the clinical outcome. The goal of this work is to study noise covariance in the helical MSCT volumetric dataset. We explore possible approximations to the noise covariance matrix with reduced degrees of freedom, including voxel-based variance, one-dimensional (1D) correlation, two-dimensional (2D) in-plane correlation and the noise power spectrum (NPS). We further examine the effect of various noise covariance models on the accuracy of a prewhitening matched filter nodule size estimation strategy. Our simulation results suggest that the 1D longitudinal, 2D in-plane and NPS prewhitening approaches can improve the performance of nodule size estimation algorithms. When taking into account computational costs in determining noise characterizations, the NPS model may be the most efficient approximation to the MSCT noise covariance matrix.
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Affiliation(s)
- Rongping Zeng
- US Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging and Applied Mathematics, 10903 New Hampshire Ave., Silver Spring, MD 20993, USA.
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Gavrielides MA, Gallas BD, Lenz P, Badano A, Hewitt SM. Observer variability in the interpretation of HER2/neu immunohistochemical expression with unaided and computer-aided digital microscopy. Arch Pathol Lab Med 2011. [PMID: 21284444 DOI: 10.1043/1543-2165-135.2.233] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
CONTEXT Observer variability in digital microscopy and the effect of computer-aided digital microscopy are underexamined areas in need of further research, considering the increasing use and future role of digital imaging in pathology. A reduction in observer variability using computer aids could enhance the statistical power of studies designed to determine the utility of new biomarkers and accelerate their incorporation in clinical practice. OBJECTIVES To quantify interobserver and intraobserver variability in immunohistochemical analysis of HER2/neu with digital microscopy and computer-aided digital microscopy, and to test the hypothesis that observer agreement in the quantitative assessment of HER2/neu immunohistochemical expression is increased with the use of computer-aided microscopy. DESIGN A set of 335 digital microscopy images extracted from 64 breast cancer tissue slides stained with a HER2 antibody, were read by 14 observers in 2 reading modes: the unaided mode and the computer-aided mode. In the unaided mode, HER2 images were displayed on a calibrated color monitor with no other information, whereas in the computer-aided mode, observers were shown a HER2 image along with a corresponding feature plot showing computer-extracted values of membrane staining intensity and membrane completeness for the particular image under examination and, at the same time, mean feature values of the different HER2 categories. In both modes, observers were asked to provide a continuous score of HER2 expression. RESULTS Agreement analysis performed on the output of the study showed significant improvement in both interobserver and intraobserver agreement when the computer-aided reading mode was used to evaluate preselected image fields. CONCLUSION The role of computer-aided digital microscopy in reducing observer variability in immunohistochemistry is promising.
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Affiliation(s)
- Marios A Gavrielides
- Division of Imaging and Applied Mathematics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland 20993, USA.
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Gavrielides MA, Gallas BD, Lenz P, Badano A, Hewitt SM. Observer variability in the interpretation of HER2/neu immunohistochemical expression with unaided and computer-aided digital microscopy. Arch Pathol Lab Med 2011; 135:233-42. [PMID: 21284444 DOI: 10.5858/135.2.233] [Citation(s) in RCA: 90] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
CONTEXT Observer variability in digital microscopy and the effect of computer-aided digital microscopy are underexamined areas in need of further research, considering the increasing use and future role of digital imaging in pathology. A reduction in observer variability using computer aids could enhance the statistical power of studies designed to determine the utility of new biomarkers and accelerate their incorporation in clinical practice. OBJECTIVES To quantify interobserver and intraobserver variability in immunohistochemical analysis of HER2/neu with digital microscopy and computer-aided digital microscopy, and to test the hypothesis that observer agreement in the quantitative assessment of HER2/neu immunohistochemical expression is increased with the use of computer-aided microscopy. DESIGN A set of 335 digital microscopy images extracted from 64 breast cancer tissue slides stained with a HER2 antibody, were read by 14 observers in 2 reading modes: the unaided mode and the computer-aided mode. In the unaided mode, HER2 images were displayed on a calibrated color monitor with no other information, whereas in the computer-aided mode, observers were shown a HER2 image along with a corresponding feature plot showing computer-extracted values of membrane staining intensity and membrane completeness for the particular image under examination and, at the same time, mean feature values of the different HER2 categories. In both modes, observers were asked to provide a continuous score of HER2 expression. RESULTS Agreement analysis performed on the output of the study showed significant improvement in both interobserver and intraobserver agreement when the computer-aided reading mode was used to evaluate preselected image fields. CONCLUSION The role of computer-aided digital microscopy in reducing observer variability in immunohistochemistry is promising.
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Affiliation(s)
- Marios A Gavrielides
- Division of Imaging and Applied Mathematics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland 20993, USA.
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Gavrielides MA, Zeng R, Kinnard LM, Myers KJ, Petrick N. Information-theoretic approach for analyzing bias and variance in lung nodule size estimation with CT: a phantom study. IEEE Trans Med Imaging 2010; 29:1795-807. [PMID: 20562039 DOI: 10.1109/tmi.2010.2052466] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
This work is a part of our more general effort to probe the interrelated factors impacting the accuracy and precision of lung nodule measurement tasks. For such a task a low-bias size estimator is needed so that the true effect of factors such as acquisition and reconstruction parameters, nodule characteristics and others can be assessed. Towards this goal, we have developed a matched filter based on an adaptive model of the object acquisition and reconstruction process. Our model derives simulated reconstructed data of nodule objects (templates) which are then matched to computed tomography data produced from imaging the actual nodule in a phantom study using corresponding imaging parameters. This approach incorporates the properties of the imaging system and their effect on the discrete 3-D representation of the object of interest. Using a sum of absolute differences cost function, the derived matched filter demonstrated low bias and variance in the volume estimation of spherical synthetic nodules ranging in density from -630 to +100 HU and in size from 5 to 10 mm. This work could potentially lead to better understanding of sources of error in the task of lung nodule size measurements and may lead to new techniques to account for those errors.
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Affiliation(s)
- Marios A Gavrielides
- Division of Imaging and Applied Mathematics (DIAM), Office of Science and Engineering Laboratories (OSEL), Center for Devices and Radiological Health (CDRH), U.S. Food and Drug Administration (FDA), Silver Spring, MD 20993, USA.
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Gavrielides MA, Kinnard LM, Myers KJ, Peregoy J, Pritchard WF, Zeng R, Esparza J, Karanian J, Petrick N. A resource for the assessment of lung nodule size estimation methods: database of thoracic CT scans of an anthropomorphic phantom. Opt Express 2010; 18:15244-55. [PMID: 20640011 PMCID: PMC3408907 DOI: 10.1364/oe.18.015244] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
A number of interrelated factors can affect the precision and accuracy of lung nodule size estimation. To quantify the effect of these factors, we have been conducting phantom CT studies using an anthropomorphic thoracic phantom containing a vasculature insert to which synthetic nodules were inserted or attached. Ten repeat scans were acquired on different multi-detector scanners, using several sets of acquisition and reconstruction protocols and various nodule characteristics (size, shape, density, location). This study design enables both bias and variance analysis for the nodule size estimation task. The resulting database is in the process of becoming publicly available as a resource to facilitate the assessment of lung nodule size estimation methodologies and to enable comparisons between different methods regarding measurement error. This resource complements public databases of clinical data and will contribute towards the development of procedures that will maximize the utility of CT imaging for lung cancer screening and tumor therapy evaluation.
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Affiliation(s)
- Marios A Gavrielides
- Division of Imaging and Mathematics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA.
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Meyer CR, Armato SG, Fenimore CP, McLennan G, Bidaut LM, Barboriak DP, Gavrielides MA, Jackson EF, McNitt-Gray MF, Kinahan PE, Petrick N, Zhao B. Quantitative imaging to assess tumor response to therapy: common themes of measurement, truth data, and error sources. Transl Oncol 2009; 2:198-210. [PMID: 19956379 PMCID: PMC2781075 DOI: 10.1593/tlo.09208] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2009] [Revised: 08/10/2009] [Accepted: 08/11/2009] [Indexed: 12/25/2022] Open
Abstract
RATIONALE Early detection of tumor response to therapy is a key goal. Finding measurement algorithms capable of early detection of tumor response could individualize therapy treatment as well as reduce the cost of bringing new drugs to market. On an individual basis, the urgency arises from the desire to prevent continued treatment of the patient with a high-cost and/or high-risk regimen with no demonstrated individual benefit and rapidly switch the patient to an alternative efficacious therapy for that patient. In the context of bringing new drugs to market, such algorithms could demonstrate efficacy in much smaller populations, which would allow phase 3 trials to achieve statistically significant decisions with fewer subjects in shorter trials. MATERIALS AND METHODS This consensus-based article describes multiple, image modality-independent means to assess the relative performance of algorithms for measuring tumor change in response to therapy. In this setting, we describe specifically the example of measurement of tumor volume change from anatomic imaging as well as provide an overview of other promising generic analytic methods that can be used to assess change in heterogeneous tumors. To support assessment of the relative performance of algorithms for measuring small tumor change, data sources of truth are required. RESULTS Very short interval clinical imaging examinations and phantom scans provide known truth for comparative evaluation of algorithms. CONCLUSIONS For a given category of measurement methods, the algorithm that has the smallest measurement noise and least bias on average will perform best in early detection of true tumor change.
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Affiliation(s)
- Charles R Meyer
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
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Masmoudi H, Hewitt SM, Petrick N, Myers KJ, Gavrielides MA. Automated quantitative assessment of HER-2/neu immunohistochemical expression in breast cancer. IEEE Trans Med Imaging 2009; 28:916-925. [PMID: 19164073 PMCID: PMC7238291 DOI: 10.1109/tmi.2009.2012901] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
The expression of the HER-2/neu (HER2) gene, a member of the epidermal growth factor receptor family, has been shown to be a valuable prognostic indicator for breast cancer. However, interobserver variability has been reported in the evaluation of HER2 with immunohistochemistry. It has been suggested that automated computer-based evaluation can provide a consistent and objective evaluation of HER2 expression. In this manuscript, we present an automated method for the quantitative assessment of HER2 using digital microscopy. The method processes microscopy images from tissue slides with a multistage algorithm, including steps of color pixel classification, nuclei segmentation, and cell membrane modeling, and extracts quantitative, continuous measures of cell membrane staining intensity and completeness. A minimum cluster distance classifier merges the features to classify the slides into HER2 categories. An evaluation based on agreement analysis with pathologist-derived HER2 scores, showed good agreement with the provided truth. Agreement varied within the different classes with highest agreement (up to 90%) for positive (3+) slides, and lowest agreement (72%-78%) for equivocal (2+) slides which contained ambiguous scoring. The developed automated method has the potential to be used as a computer aid for the immunohistochemical evaluation of HER2 expression with the objective of increasing observer reproducibility.
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Affiliation(s)
- Hela Masmoudi
- Department of Electrical and Computer Engineering, The George Washington University, Washington, DC 20052, USA
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Abstract
Lung nodule volumetry is used for nodule diagnosis, as well as for monitoring tumor response to therapy. Volume measurement precision and accuracy depend on a number of factors, including image-acquisition and reconstruction parameters, nodule characteristics, and the performance of algorithms for nodule segmentation and volume estimation. The purpose of this article is to provide a review of published studies relevant to the computed tomographic (CT) volumetric analysis of lung nodules. A number of underexamined areas of research regarding volumetric accuracy are identified, including the measurement of nonsolid nodules, the effects of pitch and section overlap, and the effect of respiratory motion. The need for public databases of phantom scans, as well as of clinical data, is discussed. The review points to the need for continued research to examine volumetric accuracy as a function of a multitude of interrelated variables involved in the assessment of lung nodules. Understanding and quantifying the sources of volumetric measurement error in the assessment of lung nodules with CT would be a first step toward the development of methods to minimize that error through system improvements and to correctly account for any remaining error.
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Affiliation(s)
- Marios A Gavrielides
- National Institute of Biomedical Imaging and Bioengineering/Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993-0002, USA.
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Raimondo F, Gavrielides MA, Karayannopoulou G, Lyroudia K, Pitas I, Kostopoulos I. Automated evaluation of Her-2/neu status in breast tissue from fluorescent in situ hybridization images. IEEE Trans Image Process 2005; 14:1288-99. [PMID: 16190465 DOI: 10.1109/tip.2005.852806] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The evaluation of fluorescent in situ hybridization (FISH) images is one of the most widely used methods to determine Her-2/neu status of breast samples, a valuable prognostic indicator. Conventional evaluation is a difficult task since it involves manual counting of dots in multiple images. In this paper, we present a multistage algorithm for the automated classification of FISH images from breast carcinomas. The algorithm focuses not only on the detection of FISH dots per image, but also on combining results from multiple images taken from a slice for overall case classification. The algorithm includes mainly two stages for nuclei and dot detection respectively. The dot segmentation consists of a top-hat filtering stage followed by template matching to separate real signals from noise. Nuclei segmentation includes a nonlinearity correction step, global thresholding to identify candidate regions, and a geometric rule to distinguish between holes within a nucleus and holes between nuclei. Finally, the marked watershed transform is used to segment cell nuclei with markers detected as regional maxima of the distance transform. Combining the two stages allows the measurement of FISH signals ratio per cell nucleus and the collective classification of cases as positive or negative. The system was evaluated with receiver operating characteristic analysis and the results were encouraging for the further development of this method.
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Affiliation(s)
- Francesco Raimondo
- Artificial Intelligence and Information Analysis Laboratory, Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.
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Gavrielides MA, Lo JY, Floyd CE. Parameter optimization of a computer-aided diagnosis scheme for the segmentation of microcalcification clusters in mammograms. Med Phys 2002; 29:475-83. [PMID: 11998828 DOI: 10.1118/1.1460874] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Our purpose in this study is to develop a parameter optimization technique for the segmentation of suspicious microcalcification clusters in digitized mammograms. In previous work, a computer-aided diagnosis (CAD) scheme was developed that used local histogram analysis of overlapping subimages and a fuzzy rule-based classifier to segment individual microcalcifications, and clustering analysis for reducing the number of false positive clusters. The performance of this previous CAD scheme depended on a large number of parameters such as the intervals used to calculate fuzzy membership values and on the combination of membership values used by each decision rule. These parameters were optimized empirically based on the performance of the algorithm on the training set. In order to overcome the limitations of manual training and rule generation, the segmentation algorithm was modified in order to incorporate automatic parameter optimization. For the segmentation of individual microcalcifications, the new algorithm used a neural network with fuzzy-scaled inputs. The fuzzy-scaled inputs were created by processing the histogram features with a family of membership functions, the parameters of which were automatically extracted from the distribution of the feature values. The neural network was trained to classify feature vectors as either positive or negative. Individual microcalcifications were segmented from positive subimages. After clustering, another neural network was trained to eliminate false positive clusters. A database of 98 images provided training and testing sets to optimize the parameters and evaluate the CAD scheme, respectively. The performance of the algorithm was evaluated with a FROC analysis. At a sensitivity rate of 93.2%, there was an average of 0.8 false positive clusters per image. The results are very comparable with those taken using our previously published rule-based method. However, the new algorithm is more suited to generalize its performance on a larger population, depends on two monotonic outputs making its evaluation much easier and can be trained in an automatic way making practical its application on a large database.
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Affiliation(s)
- Marios A Gavrielides
- Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA.
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Abstract
We have developed a multistage computer-aided diagnosis (CAD) scheme for the automated segmentation of suspicious microcalcification clusters in digital mammograms. The scheme consisted of three main processing steps. First, the breast region was segmented and its high-frequency content was enhanced using unsharp masking. In the second step, individual microcalcifications were segmented using local histogram analysis on overlapping subimages. For this step, eight histogram features were extracted for each subimage and were used as input to a fuzzy rule-based classifier that identified subimages containing microcalcifications and assigned the appropriate thresholds to segment any microcalcifications within them. The final step clustered the segmented microcalcifications and extracted the following features for each cluster: the number of microcalcifications, the average distance between microcalcifications, and the average number of times pixels in the cluster were segmented in the second step. Fuzzy logic rules incorporating the cluster features were designed to remove nonsuspicious clusters, defined as those with typically benign characteristics. A database of 98 images, with 48 images containing one or more microcalcification clusters, provided training and testing sets to optimize the parameters and evaluate the CAD scheme, respectively. The results showed a true positive rate of 93.2% and an average of 0.73 false positive clusters per image. A comparison of our results with other reported segmentation results on the same database showed comparable sensitivity and at the same time an improved false positive rate. The performance of the CAD scheme is encouraging for its use as an automatic tool for efficient and accurate diagnosis of breast cancer.
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Affiliation(s)
- M A Gavrielides
- Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA.
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Kallergi M, Gavrielides MA, He L, Berman CG, Kim JJ, Clark RA. Simulation model of mammographic calcifications based on the American College of Radiology Breast Imaging Reporting and Data System, or BIRADS. Acad Radiol 1998; 5:670-9. [PMID: 9787837 DOI: 10.1016/s1076-6332(98)80561-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
RATIONALE AND OBJECTIVES The authors developed and evaluated a method for the simulation of calcification clusters based on the guidelines of the Breast Imaging Reporting and Data System of the American College of Radiology. They aimed to reproduce accurately the relative and absolute size, shape, location, number, and intensity of real calcifications associated with both benign and malignant disease. MATERIALS AND METHODS Thirty calcification clusters were simulated by using the proposed model and were superimposed on real, negative mammograms digitized at 30 microns and 16 bits per pixel. The accuracy of the simulation was evaluated by three radiologists in a blinded study. RESULTS No statistically significant difference was observed in the observers' evaluation of the simulated clusters and the real clusters. The observers' classification of the cluster types seemed to be a good approximation of the intended types from the simulation design. CONCLUSION This model can provide simulated calcification clusters with well-defined morphologic, distributional, and contrast characteristics for a variety of applications in digital mammography.
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Affiliation(s)
- M Kallergi
- Department of Radiology, University of South Florida, Tampa 33612-4799, USA
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