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Mohammadi S, Ghaderi S, Mohammadi M, Ghaznavi H, Mohammadian K. Breast percent density changes in digital mammography pre- and post-radiotherapy. J Med Radiat Sci 2024. [PMID: 38571377 DOI: 10.1002/jmrs.788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 03/20/2024] [Indexed: 04/05/2024] Open
Abstract
INTRODUCTION Breast cancer (BC), the most frequently diagnosed malignancy among women worldwide, presents a public health challenge and affects mortality rates. Breast-conserving therapy (BCT) is a common treatment, but the risk from residual disease necessitates radiotherapy. Digital mammography monitors treatment response by identifying post-operative and radiotherapy tissue alterations, but accurate assessment of mammographic density remains a challenge. This study used OpenBreast to measure percent density (PD), offering insights into changes in mammographic density before and after BCT with radiation therapy. METHODS This retrospective analysis included 92 female patients with BC who underwent BCT, chemotherapy, and radiotherapy, excluding those who received hormonal therapy or bilateral BCT. Percent/percentage density measurements were extracted using OpenBreast, an automated software that applies computational techniques to density analyses. Data were analysed at baseline, 3 months, and 15 months post-treatment using standardised mean difference (SMD) with Cohen's d, chi-square, and paired sample t-tests. The predictive power of PD changes for BC was measured based on the receiver operating characteristic (ROC) curve analysis. RESULTS The mean age was 53.2 years. There were no significant differences in PD between the periods. Standardised mean difference analysis revealed no significant changes in the SMD for PD before treatment compared with 3- and 15-months post-treatment. Although PD increased numerically after radiotherapy, ROC analysis revealed optimal sensitivity at 15 months post-treatment for detecting changes in breast density. CONCLUSIONS This study utilised an automated breast density segmentation tool to assess the changes in mammographic density before and after BC treatment. No significant differences in the density were observed during the short-term follow-up period. However, the results suggest that quantitative density assessment could be valuable for long-term monitoring of treatment effects. The study underscores the necessity for larger and longitudinal studies to accurately measure and validate the effectiveness of quantitative methods in clinical BC management.
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Affiliation(s)
- Sana Mohammadi
- Department of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Sadegh Ghaderi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahdi Mohammadi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid Ghaznavi
- Department of Radiology, Faculty of Paramedical Sciences, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Kamal Mohammadian
- Department of Radiation Oncology, Hamadan University of Medical Sciences, Mahdieh Center, Hamadan, Iran
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Diagnostic performance of tomosynthesis, digital mammography and a dedicated digital specimen radiography system versus pathological assessment of excised breast lesions. Radiol Oncol 2022; 56:461-470. [PMID: 36226804 PMCID: PMC9784367 DOI: 10.2478/raon-2022-0036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/06/2022] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND The aim of the study was to compare the performance of full-field digital mammography (FFDM), digital breast tomosynthesis and a dedicated digital specimen radiography system (SRS) in consecutive patients, and to compare the margin status of resected lesions versus pathological assessment. PATIENTS AND METHODS Resected tissue specimens from consecutive patients who underwent intraoperative breast specimen assessment following wide local excision or oncoplastic breast conservative surgery were examined by FFDM, tomosynthesis and SRS. Two independent observers retrospectively evaluated the visibility of lesions, size, margins, spiculations, calcifications and diagnostic certainty, and chose the best performing method in a blinded manner. RESULTS We evaluated 216 specimens from 204 patients. All target malignant lesions were removed with no tumouron-ink. One papilloma had positive microscopic margins and one patient underwent reoperation owing to extensive in situ components. There were no significant differences in measured lesion size among the three methods. However, tomosynthesis was the most accurate modality when compared with the final pathological report. Both observers reported that tomosynthesis had significantly better lesion visibility than SRS and FFDM, which translated into a significantly greater diagnostic certainty. Tomosynthesis was superior to the other two methods in identifying spiculations and calcifications. Both observers reported that tomosynthesis was the best performing method in 76.9% of cases. The interobserver reproducibilities of lesion visibility and diagnostic certainty were high for all three methods. CONCLUSIONS Tomosynthesis was superior to SRS and FFDM for detecting and evaluating the target lesions, spiculations and calcifications, and was therefore more reliable for assessing complete excision of breast lesions.
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Abdel Fattah NMA, Zahran MH, Fawzy RK, Abdel Hamid AEDM, Maghraby HK. The impact of Digital Breast Tomosynthesis on BIRADS categorization of mammographic non-mass findings. ALEXANDRIA JOURNAL OF MEDICINE 2021. [DOI: 10.1080/20905068.2021.1916244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
Affiliation(s)
| | - Mohamed H. Zahran
- Department of Radio-diagnosisand Intervention, Faculty of Medicine, University of Alexandria, Egypt
| | - Rawya K. Fawzy
- Department of Radio-diagnosis, Medical Research Institute, University of Alexandria, Egypt
| | | | - Hala K. Maghraby
- Department of Pathology, Medical Research Institute, University of Alexandria, Egypt
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Mishra J, Kumar B, Targhotra M, Sahoo PK. Advanced and futuristic approaches for breast cancer diagnosis. FUTURE JOURNAL OF PHARMACEUTICAL SCIENCES 2020. [DOI: 10.1186/s43094-020-00113-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Breast cancer is the most frequent cancer and one of the most common causes of death in women, impacting almost 2 million women each year. Tenacity or perseverance of breast cancer in women is very high these days with an extensive increasing rate of 3 to 5% every year. Along with hurdles faced during treatment of breast tumor, one of the crucial causes of delay in treatment is invasive and poor diagnostic techniques for breast cancer hence the early diagnosis of breast tumors will help us to improve its management and treatment in the initial stage.
Main body
Present review aims to explore diagnostic techniques for breast cancer that are currently being used, recent advancements that aids in prior detection and evaluation and are extensively focused on techniques that are going to be future of breast cancer detection with better efficiency and lesser pain to patients so that it helps to a physician to prevent delay in treatment of cancer. Here, we have discussed mammography and its advanced forms that are the need of current era, techniques involving radiation such as radionuclide methods, the potential of nanotechnology by using nanoparticle in breast cancer, and how the new inventions such as breath biopsy, and X-ray diffraction of hair can simply use as a prominent method in breast cancer early and easy detection tool.
Conclusion
It is observed significantly that advancement in detection techniques is helping in early diagnosis of breast cancer; however, we have to also focus on techniques that will improve the future of cancer diagnosis in like optical imaging and HER2 testing.
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Mota AM, Clarkson MJ, Almeida P, Matela N. An Enhanced Visualization of DBT Imaging Using Blind Deconvolution and Total Variation Minimization Regularization. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:4094-4101. [PMID: 32746152 DOI: 10.1109/tmi.2020.3013107] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Digital Breast Tomosynthesis (DBT) presents out-of-plane artifacts caused by features of high intensity. Given observed data and knowledge about the point spread function (PSF), deconvolution techniques recover data from a blurred version. However, a correct PSF is difficult to achieve and these methods amplify noise. When no information is available about the PSF, blind deconvolution can be used. Additionally, Total Variation (TV) minimization algorithms have achieved great success due to its virtue of preserving edges while reducing image noise. This work presents a novel approach in DBT through the study of out-of-plane artifacts using blind deconvolution and noise regularization based on TV minimization. Gradient information was also included. The methodology was tested using real phantom data and one clinical data set. The results were investigated using conventional 2D slice-by-slice visualization and 3D volume rendering. For the 2D analysis, the artifact spread function (ASF) and Full Width at Half Maximum (FWHMMASF) of the ASF were considered. The 3D quantitative analysis was based on the FWHM of disks profiles at 90°, noise and signal to noise ratio (SNR) at 0° and 90°. A marked visual decrease of the artifact with reductions of FWHMASF (2D) and FWHM90° (volume rendering) of 23.8% and 23.6%, respectively, was observed. Although there was an expected increase in noise level, SNR values were preserved after deconvolution. Regardless of the methodology and visualization approach, the objective of reducing the out-of-plane artifact was accomplished. Both for the phantom and clinical case, the artifact reduction in the z was markedly visible.
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Mall S, Brennan PC, Mello-Thoms C. Can a Machine Learn from Radiologists' Visual Search Behaviour and Their Interpretation of Mammograms-a Deep-Learning Study. J Digit Imaging 2019; 32:746-760. [PMID: 31410677 PMCID: PMC6737161 DOI: 10.1007/s10278-018-00174-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
Abstract
Visual search behaviour and the interpretation of mammograms have been studied for errors in breast cancer detection. We aim to ascertain whether machine-learning models can learn about radiologists' attentional level and the interpretation of mammograms. We seek to determine whether these models are practical and feasible for use in training and teaching programmes. Eight radiologists of varying experience levels in reading mammograms reviewed 120 two-view digital mammography cases (59 cancers). Their search behaviour and decisions were captured using a head-mounted eye-tracking device and software allowing them to record their decisions. This information from radiologists was used to build an ensembled machine-learning model using top-down hierarchical deep convolution neural network. Separately, a model to determine type of missed cancer (search, perception or decision-making) was also built. Analysis and comparison of variants of these models using different convolution networks with and without transfer learning were also performed. Our ensembled deep-learning network architecture can be trained to learn about radiologists' attentional level and decisions. High accuracy (95%, p value ≅ 0 [better than dumb/random model]) and high agreement between true and predicted values (kappa = 0.83) in such modelling can be achieved. Transfer learning techniques improve by < 10% with the performance of this model. We also show that spatial convolution neural networks are insufficient in determining the type of missed cancers. Ensembled hierarchical deep convolution machine-learning models are plausible in modelling radiologists' attentional level and their interpretation of mammograms. However, deep convolution networks fail to characterise the type of false-negative decisions.
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Affiliation(s)
- Suneeta Mall
- Medical Image Optimisation and Perception Research Group (MIOPeG), Faculty of Medicine and Health, University of Sydney, 75 East Street, Lidcombe, NSW, 2141, Australia.
| | - Patrick C Brennan
- Medical Image Optimisation and Perception Research Group (MIOPeG), Faculty of Medicine and Health, University of Sydney, 75 East Street, Lidcombe, NSW, 2141, Australia
| | - Claudia Mello-Thoms
- Medical Image Optimisation and Perception Research Group (MIOPeG), Faculty of Medicine and Health, University of Sydney, 75 East Street, Lidcombe, NSW, 2141, Australia
- Department of Radiology, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52242, USA
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Mall S, Noakes J, Kossoff M, Lee W, McKessar M, Goy A, Duncombe J, Roberts M, Giuffre B, Miller A, Bhola N, Kapoor C, Shearman C, DaCosta G, Choi S, Sterba J, Kay M, Bruderlin K, Winarta N, Donohue K, Macdonell-Scott B, Klijnsma F, Suzuki K, Brennan P, Mello-Thoms C. Can digital breast tomosynthesis perform better than standard digital mammography work-up in breast cancer assessment clinic? Eur Radiol 2018; 28:5182-5194. [PMID: 29846804 DOI: 10.1007/s00330-018-5473-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 03/24/2018] [Accepted: 04/10/2018] [Indexed: 11/30/2022]
Affiliation(s)
- S Mall
- Faculty of Health Sciences, University of Sydney, 75 East Street, Room M204, Lidcombe, New South Wales, Australia.
| | - J Noakes
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - M Kossoff
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - W Lee
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - M McKessar
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - A Goy
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - J Duncombe
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - M Roberts
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - B Giuffre
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - A Miller
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - N Bhola
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - C Kapoor
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - C Shearman
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - G DaCosta
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - S Choi
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - J Sterba
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - M Kay
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - K Bruderlin
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - N Winarta
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - K Donohue
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - B Macdonell-Scott
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - F Klijnsma
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - K Suzuki
- Northern Sydney & Central Coast BreastScreen, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - P Brennan
- Faculty of Health Sciences, University of Sydney, 75 East Street, Room M204, Lidcombe, New South Wales, Australia
| | - C Mello-Thoms
- Faculty of Health Sciences, University of Sydney, 75 East Street, Room M204, Lidcombe, New South Wales, Australia
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Mall S, Lewis S, Brennan P, Noakes J, Mello‐Thoms C. The role of digital breast tomosynthesis in the breast assessment clinic: a review. J Med Radiat Sci 2017; 64:203-211. [PMID: 28374502 PMCID: PMC5587657 DOI: 10.1002/jmrs.230] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2016] [Revised: 02/17/2017] [Accepted: 02/26/2017] [Indexed: 01/22/2023] Open
Abstract
Mammography has long been considered as the primary technique in breast cancer detection and assessment. Despite low specificity, mammography has been preferred over other contemporary techniques such as magnetic resonance imaging (MRI), computed tomography (CT) and ultrasonography (US) due to superior sensitivity and significant health economic benefits. The development of a new technique, a limited angle cone beam pseudo-three-dimensional tomosynthesis, digital breast tomosynthesis (DBT), has gained momentum. Several preliminary studies and ongoing trials are showing evidence of the benefits of DBT in improving lesion visibility, accuracy of cancer detection and observer performance. This raises the possibility of adoption of DBT in the breast cancer assessment clinic, wherein confirming or dismissing the presence of malignancy (at the potential site identified during screening) is of utmost importance. Identification of suspected malignancy in terms of lesion characteristics and location is also essential in assessment. In this literature review, we evaluate the role of DBT for use in breast cancer assessment and its future in biopsy.
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Affiliation(s)
- Suneeta Mall
- Faculty of Health SciencesUniversity of SydneyLidcombeNew South WalesAustralia
| | - Sarah Lewis
- Faculty of Health SciencesUniversity of SydneyLidcombeNew South WalesAustralia
| | - Patrick Brennan
- Faculty of Health SciencesUniversity of SydneyLidcombeNew South WalesAustralia
| | - Jennie Noakes
- Northern Sydney & Central Coast BreastScreenRoyal North Shore HospitalSt. LeonardsNew South WalesAustralia
| | - Claudia Mello‐Thoms
- Faculty of Health SciencesUniversity of SydneyLidcombeNew South WalesAustralia
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