PurposeUse of mechanical imaging (MI) as complementary to digital mammography (DM), or in simultaneous digital breast tomosynthesis (DBT) and MI – DBTMI, has demonstrated the potential to increase the specificity of breast cancer screening and reduce unnecessary biopsies compared with DM. The aim of this study is to investigate the increase in the radiation dose due to the presence of an MI sensor during simultaneous image acquisition when automatic exposure control is used.ApproachA radiation dose study was conducted on clinically available breast imaging systems with and without an MI sensor present. Our estimations were based on three approaches. In the first approach, exposure values were compared in paired clinical DBT and DBTMI acquisitions in 97 women. In the second approach polymethyl methacrylate (PMMA) phantoms of various thicknesses were used, and the average glandular dose (AGD) values were compared. Finally, a rectangular PMMA phantom with a 45 mm thickness was used, and the AGD values were estimated based on air kerma measurements with an electronic dosemeter.ResultsThe relative increase in exposure estimated from digital imaging and communications in medicine headers when using an MI sensor in clinical DBTMI was 11.9%±10.4. For the phantom measurements of various thicknesses of PMMA, the relative increases in the AGD for DM and DBT measurements were, on average, 10.7%±3.1 and 11.4%±3.0, respectively. The relative increase in the AGD using the electronic dosemeter was 11.2%±<0.001 in DM and 12.2%±<0.001 in DBT. The average difference in dose between the methods was 11.5%±3.3.ConclusionsOur measurements suggest that the use of simultaneous breast radiography and MI increases the AGD by an average of 11.5%±3.3. The increase in dose is within the acceptable values for mammography screening recommended by European guidelines.
The combination of digital breast tomosynthesis (DBT) with other imaging modalities has been investigated in order to improve the detection and diagnosis of breast cancer. Mechanical Imaging (MI) measures the stress over the surface of the compressed breast, using a pressure sensor, during radiographic examination and its response has shown a correlation with the presence of malignant lesions. Thus, the combination of DBT and MI (DBTMI) has shown potential to reduce false positive results in breast cancer screening. However, compared to the conventional DBT exam, the presence of the MI sensor during mammographic image acquisition may cause a slight increase in the radiation dose. This work presents a proposal to reduce the radiation dose in DBTMI exams by removing some projections from the original set and replacing them with synthetic projections generated by a video frame interpolation (VFI) neural network. We compared several DBTMI acquisition arrangements, considering the removal of 16% of the original projections, using a deformable physical breast phantom, and evaluated the quality of the reconstructed images based on the Normalized Root Mean Squared Error (NRMSE). The results showed that, for some arrangements, the slices reconstructed with the addition of synthetic DBTMI projections presented better quality than when they were reconstructed with the reduced set of projections. Further studies must be carried out to optimize the interpolation approach.
This study investigates the impact in terms of radiation dose when performing simultaneous digital breast tomosynthesis (DBT) and mechanical imaging (MI) – DBTMI. DBTMI has demonstrated the potential to increase specificity of cancer detection, and reduce unnecessary biopsies, as compared to digital mammography (DM) screening. The presence of the MI sensor during simultaneous image acquisition may increase the radiation dose when automatic exposure control is used. In this project, a radiation dose study was conducted on clinically available breast imaging systems with and without the MI sensor. We have investigated three approaches to analyse the dose increase in DBTMI, using (i) the estimates of average glandular dose (AGD) reported in DICOM headers of radiography images; (ii) AGD measured by a conventional dosemeter; and (iii) AGD measured by optically stimulated luminescence using NaCl pellets. The relative increase in AGD estimated from DICOM headers when using the MI sensor was on average 10.7% and 12.4%, for DM and DBT measurements, respectively. The relative increase in AGD using the conventional dosemeter was 11.2% in DM mode and 12.2% in DBT mode. The relative increase in AGD using NaCl pellets was 14.6% in DM mode. Our measurements suggest that the use of simultaneous breast radiography and MI increases the AGD by 13% on average. The increase in dose is still below the acceptable values in mammography screening recommended by the European Guidelines.
Tumor growth rate estimations can provide useful information about tumor progression and aggressiveness. Understanding the breast cancer progression and aggressiveness could aid with personalized screening/follow-up, treatment options, and prognosis. This paper reports a preliminary estimation of the tumor volume doubling time (TVDT) for cancers detected during the Malmö Breast Tomosynthesis Screening Trial (MBTST). The trial included 14 848 women in whom 139 cancers were detected. Out of those, 101 spiculated or circumscribed masses, had prior images available, making them suitable for tumor growth evaluation. In the preliminary analysis of images from 30 women, tumor size was measured in mammograms from MBTST and prior images. The analyzed cases were selected among women with visible tumors in two consecutive screening exams. The tumor size was measured in two orthogonal directions. The average of the two measurements was used in the analysis. The mean time and the corresponding standard deviation (SD) between the two consecutive mammograms were 744 ± 73 days. The mean TVDT and SD were 637 ± 428 days (range 159-2373 days). Future work will include the analysis of a larger number of women and a stratification of TVDT related to screening intervals.
KEYWORDS: Tumors, Mammography, Breast, Breast cancer, Clinical trials, Statistical analysis, Error analysis, Computer simulations, Digital breast tomosynthesis, Cancer
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.
KEYWORDS: Clinical trials, Breast, Tumor growth modeling, Breast cancer, Systems modeling, Mammography, Imaging systems, Data modeling, Medical imaging, Computing systems
Image-based analysis of breast tumour growth rate may help optimize breast cancer screening and diagnosis. It may improve the identification of aggressive tumours and suggest optimal screening intervals. Virtual clinical trial (VCT) is a simulation-based method used to evaluate and optimize medical imaging systems and design clinical trials. Our work is motivated by desire to simulate multiple screening rounds with growing tumours. We have developed a model to simulate tumours with various growth rates; this study aims at evaluating the model. We used clinical data on tumour volume doubling times (TVDT) from our previous study, to fit a probability distribution (“clinical fit”). Growing tumours were inserted into 30 virtual breasts (“simulated cohort”). Based on the clinical fit we simulated two successive screening rounds for each virtual breast. TVDT from clinical and simulated images were compared. Tumour size was measured from simulated mammograms by a radiologist in three repeated sessions, to estimate TVDT (“estimated TVDT”). Reproducibility of measured sizes decreased slightly for small tumours. The mean TVDT from the clinical fit was 297±169 days, whereas the simulated cohort had 322±217 days, and the average estimated TVDT 340 ± 287 days. The median difference between the simulated and estimated TVDT was 12 days (4% of the mean clinical TVDT). Comparisons between other data sets suggest no significant difference (p>0.5). The proposed tumour growth model suggested close agreement with clinical results, supporting potential use in VCTs of temporal breast imaging.
Artificial intelligence (AI) applications are increasingly seeing use in breast imaging, particularly to assist in or automate the reading of mammograms. Another novel technique is mechanical imaging (MI) which estimates the relative stiffness of suspicious breast abnormalities by measuring the distribution of pressure on the compressed breast. This study investigates the feasibility of combining AI and MI information in breast imaging to provide further diagnostic information. Forty-six women recalled from screening were included in the analysis. Mammograms with findings scored on a suspiciousness scale by an AI tool, and corresponding pressure distributions were collected for each woman. The cases were divided into three groups by diagnosis; biopsy-proven cancer, biopsy-proven benign and non-biopsied, very likely benign. For all three groups, the relative increase of pressure at the location of the finding marked most suspicious by the AI software was recorded. A significant correlation between the relative pressure increase at the AI finding and the AI score was established in the group with cancer (p=0.043), but neither group of healthy women showed such a correlation. This study suggests that AI and MI indicate independent markers for breast cancer. The combination of these two methods has the potential to increase the accuracy of mammography screening, but further research is needed.
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