KEYWORDS: Digital breast tomosynthesis, Breast cancer, Breast, Cancer, Cancer detection, Mammography, Tomosynthesis, Tumors, Image compression, Artificial intelligence
PurposeThe purpose is to describe the Malmö Breast Tomosynthesis Screening Project from the beginning to where we are now, and thoughts for the future.ApproachIn two acts, we describe the efforts made by our research group to improve breast cancer screening by introducing digital breast tomosynthesis (DBT), all the way from initial studies to a large prospective population-based screening trial and beyond.ResultsOur studies have shown that DBT has significant advantages over digital mammography (DM), the current gold standard method for breast cancer screening in Europe, in many aspects except a major one—the increased radiologist workload introduced with DBT compared with DM. It is foreseen that AI could be a viable solution to overcome this problem.ConclusionsWe have proved that one-view DBT is a highly efficient screening approach with respect to diagnostic performance.
KEYWORDS: Databases, Mammography, Cancer, Breast cancer, Diagnostics, Breast, Breast imaging, Digital breast tomosynthesis, Artificial intelligence, Tumors
PurposeWe describe the design and implementation of the Malmö Breast ImaginG (M-BIG) database, which will support research projects investigating various aspects of current and future breast cancer screening programs. Specifically, M-BIG will provide clinical data to:1.investigate the effect of breast cancer screening on breast cancer prognosis and mortality;2.develop and validate the use of artificial intelligence and machine learning in breast image interpretation; and3.develop and validate image-based radiological breast cancer risk profiles.ApproachThe M-BIG database is intended to include a wide range of digital mammography (DM) and digital breast tomosynthesis (DBT) examinations performed on women at the Mammography Clinic in Malmö, Sweden, from the introduction of DM in 2004 through 2020. Subjects may be included multiple times and for diverse reasons. The image data are linked to extensive clinical, diagnostic, and demographic data from several registries.ResultsTo date, the database contains a total of 451,054 examinations from 104,791 women. During the inclusion period, 95,258 unique women were screened. A total of 19,968 examinations were performed using DBT, whereas the rest used DM.ConclusionsWe describe the design and implementation of the M-BIG database as a representative and accessible medical image database linked to various types of medical data. Work is ongoing to add features and curate the existing data.
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.
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