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Tomic H, Bjerkén A, Hellgren G, Johnson K, Förnvik D, Zackrisson S, Tingberg A, Dustler M, Bakic PR. Development and evaluation of a method for tumor growth simulation in virtual clinical trials of breast cancer screening. J Med Imaging (Bellingham) 2022; 9:033503. [PMID: 35685119 PMCID: PMC9168969 DOI: 10.1117/1.jmi.9.3.033503] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 05/12/2022] [Indexed: 09/27/2023] Open
Abstract
Purpose: Image-based analysis of breast tumor growth rate may optimize breast cancer screening and diagnosis by suggesting optimal screening intervals and guide the clinical discussion regarding personalized screening based on tumor aggressiveness. Simulation-based virtual clinical trials (VCTs) can be used to evaluate and optimize medical imaging systems and design clinical trials. This study aimed to simulate tumor growth over multiple screening rounds. Approach: This study evaluates a preliminary method for simulating tumor growth. Clinical data on tumor volume doubling time (TVDT) was used to fit a probability distribution ("clinical fit") of TVDTs. Simulated tumors with TVDTs sampled from the clinical fit were inserted into 30 virtual breasts ("simulated cohort") and used to simulate mammograms. Based on the TVDT, two successive screening rounds were simulated for each virtual breast. TVDTs from clinical and simulated mammograms were compared. Tumor sizes in the simulated mammograms were measured by a radiologist in three repeated sessions to estimate TVDT. Results: The mean TVDT was 297 days (standard deviation, SD, 169 days) in the clinical fit and 322 days (SD, 217 days) in the simulated cohort. The mean estimated TVDT was 340 days (SD, 287 days). No significant difference was found between the estimated TVDTs from simulated mammograms and clinical TVDT values ( p > 0.5 ). No significant difference ( p > 0.05 ) was observed in the reproducibility of the tumor size measurements between the two screening rounds. Conclusions: The proposed method for tumor growth simulation has demonstrated close agreement with clinical results, supporting potential use in VCTs of temporal breast imaging.
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Affiliation(s)
- Hanna Tomic
- Lund University, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Skåne University Hospital, Radiation Physics, Malmö, Sweden
| | - Anna Bjerkén
- Lund University, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Skåne University Hospital, Radiation Physics, Malmö, Sweden
| | - Gustav Hellgren
- Lund University, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Skåne University Hospital, Radiation Physics, Malmö, Sweden
| | - Kristin Johnson
- Lund University, Diagnostic Radiology, Department of Translational Medicine, Malmö, Sweden
- Skåne University Hospital, Department of Medical Imaging and Physiology, Malmö, Sweden
| | - Daniel Förnvik
- Lund University, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Skåne University Hospital, Radiation Physics, Malmö, Sweden
| | - Sophia Zackrisson
- Lund University, Diagnostic Radiology, Department of Translational Medicine, Malmö, Sweden
- Skåne University Hospital, Department of Medical Imaging and Physiology, Malmö, Sweden
| | - Anders Tingberg
- Lund University, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Skåne University Hospital, Radiation Physics, Malmö, Sweden
| | - Magnus Dustler
- Lund University, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Lund University, Diagnostic Radiology, Department of Translational Medicine, Malmö, Sweden
| | - Predrag R. Bakic
- Lund University, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Lund University, Diagnostic Radiology, Department of Translational Medicine, Malmö, Sweden
- University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
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Bliznakova K. The advent of anthropomorphic three-dimensional breast phantoms for X-ray imaging. Phys Med 2020; 79:145-161. [DOI: 10.1016/j.ejmp.2020.11.025] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 11/16/2020] [Accepted: 11/19/2020] [Indexed: 10/22/2022] Open
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Bliznakova K, Dukov N, Feradov F, Gospodinova G, Bliznakov Z, Russo P, Mettivier G, Bosmans H, Cockmartin L, Sarno A, Kostova-Lefterova D, Encheva E, Tsapaki V, Bulyashki D, Buliev I. Development of breast lesions models database. Phys Med 2019; 64:293-303. [PMID: 31387779 DOI: 10.1016/j.ejmp.2019.07.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 07/01/2019] [Accepted: 07/22/2019] [Indexed: 12/11/2022] Open
Abstract
PURPOSE We present the development and the current state of the MaXIMA Breast Lesions Models Database, which is intended to provide researchers with both segmented and mathematical computer-based breast lesion models with realistic shape. METHODS The database contains various 3D images of breast lesions of irregular shapes, collected from routine patient examinations or dedicated scientific experiments. It also contains images of simulated tumour models. In order to extract the 3D shapes of the breast cancers from patient images, an in-house segmentation algorithm was developed for the analysis of 50 tomosynthesis sets from patients diagnosed with malignant and benign lesions. In addition, computed tomography (CT) scans of three breast mastectomy cases were added, as well as five whole-body CT scans. The segmentation algorithm includes a series of image processing operations and region-growing techniques with minimal interaction from the user, with the purpose of finding and segmenting the areas of the lesion. Mathematically modelled computational breast lesions, also stored in the database, are based on the 3D random walk approach. RESULTS The MaXIMA Imaging Database currently contains 50 breast cancer models obtained by segmentation of 3D patient breast tomosynthesis images; 8 models obtained by segmentation of whole body and breast cadavers CT images; and 80 models based on a mathematical algorithm. Each record in the database is supported with relevant information. Two applications of the database are highlighted: inserting the lesions into computationally generated breast phantoms and an approach in generating mammography images with variously shaped breast lesion models from the database for evaluation purposes. Both cases demonstrate the implementation of multiple scenarios and of an unlimited number of cases, which can be used for further software modelling and investigation of breast imaging techniques. The created database interface is web-based, user friendly and is intended to be made freely accessible through internet after the completion of the MaXIMA project. CONCLUSIONS The developed database will serve as an imaging data source for researchers, working on breast diagnostic imaging and on improving early breast cancer detection techniques, using existing or newly developed imaging modalities.
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Affiliation(s)
- Kristina Bliznakova
- Laboratory of Computer Simulations in Medicine, Technical University of Varna, Varna, Bulgaria.
| | - Nikolay Dukov
- Laboratory of Computer Simulations in Medicine, Technical University of Varna, Varna, Bulgaria
| | - Firgan Feradov
- Laboratory of Computer Simulations in Medicine, Technical University of Varna, Varna, Bulgaria
| | - Galja Gospodinova
- Laboratory of Computer Simulations in Medicine, Technical University of Varna, Varna, Bulgaria
| | - Zhivko Bliznakov
- Laboratory of Computer Simulations in Medicine, Technical University of Varna, Varna, Bulgaria
| | - Paolo Russo
- Dipartimento di Fisica "Ettore Pancini", Universita' di Napoli Federico II and INFN Sezione di Napoli, Napoli, Italy
| | - Giovanni Mettivier
- Dipartimento di Fisica "Ettore Pancini", Universita' di Napoli Federico II and INFN Sezione di Napoli, Napoli, Italy
| | - Hilde Bosmans
- Department of Radiology, Katholieke University of Leuven, Leuven, Belgium
| | - Lesley Cockmartin
- Department of Radiology, Katholieke University of Leuven, Leuven, Belgium
| | - Antonio Sarno
- Dipartimento di Fisica "Ettore Pancini", Universita' di Napoli Federico II and INFN Sezione di Napoli, Napoli, Italy
| | | | - Elitsa Encheva
- Radiotherapy Department, University Hospital "St. Marina", Medical University of Varna, Varna, Bulgaria
| | - Virginia Tsapaki
- Medical Physics Department, Konstantopoulio General Hospital, Nea Ionia, Attiki, Greece
| | - Daniel Bulyashki
- Surgery Department, University Hospital "St. Marina", Medical University of Varna, Varna, Bulgaria
| | - Ivan Buliev
- Laboratory of Computer Simulations in Medicine, Technical University of Varna, Varna, Bulgaria
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Models of breast lesions based on three-dimensional X-ray breast images. Phys Med 2019; 57:80-87. [DOI: 10.1016/j.ejmp.2018.12.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Revised: 11/30/2018] [Accepted: 12/17/2018] [Indexed: 02/08/2023] Open
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Ikejimba LC, Glick SJ, Choudhury KR, Samei E, Lo JY. Assessing task performance in FFDM, DBT, and synthetic mammography using uniform and anthropomorphic physical phantoms. Med Phys 2017; 43:5593. [PMID: 27782687 DOI: 10.1118/1.4962475] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
PURPOSE The purpose of this study is to quantify the differences in detectability between full field digital mammography (FFDM), digital breast tomosynthesis (DBT), and synthetic mammography (SM) for challenging, low contrast signals, in the context of both a uniform and an anthropomorphic, textured phantom. METHODS Images of the phantoms were acquired using a Hologic Selenia Dimensions system. Images were taken at 50%, 100%, and 200% of the dose delivered under automatic exposure control (AEC). Low-contrast disks, created using an inkjet printer with iodine-doped ink, were inserted into the phantom. The disks varied in diameter from 210 to 630 μm, and in local contrast from 1.1% to 2.8% in regular increments. Human observers located the disks in a 4 alternative forced choice experiment. Proportion correct (PC) was computed as the number of correct localizations out of the total number of tries. RESULTS Overall, scores from FFDM and DBT were consistently greater than scores from SM. At an exposure corresponding to the AEC setting, mean PC scores for the largest disks with the uniform phantom were 0.80 for FFDM, 0.83 for DBT, and 0.66 for SM, with the same rank ordering at other doses. Scores were similar but lower for the nonuniform background. At an exposure twice the AEC setting, however, the difference between uniform and nonuniform scores was most pronounced for DBT alone. Differences between scores for FFDM and SM were statistically significant, while those between FFDM and DBT were not. Scores were used to compute the minimum contrast level needed to reach 62.5% detection rate. The minimum contrast for SM was 36%-81% higher compared to FFDM or DBT, in either background. CONCLUSIONS This study shows that an anthropomorphic phantom and lesions inserts may be used to conduct a reader study. Detectability was significantly lower for synthetic mammography than for FFDM or DBT, for all conditions. Additionally, observer performance was consistently lower for the anthropomorphic phantom, indicating the greater challenge due to anatomical background. Because of this, it may be important to use realistic phantoms in observer studies in order to draw conclusions that are more clinically relevant.
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Affiliation(s)
- Lynda C Ikejimba
- Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Diagnostic and Radiological Health, FDA, Silver Spring, Maryland 20993
| | - Stephen J Glick
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Diagnostic and Radiological Health, FDA, Silver Spring, Maryland 20993
| | - Kingshuk Roy Choudhury
- Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705
| | - Ehsan Samei
- Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705; Department of Biomedical Engineering, Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina 27705; and Department of Physics, Duke University, Durham, North Carolina 27705
| | - Joseph Y Lo
- Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Department of Biomedical Engineering, Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina 27705
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