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.
In this study, two digital breast tomosynthesis (DBT) systems were evaluated: Siemens Mammomat Inspiration TOMO (Siemens Healthineers, Erlangen, Germany) and GE Senographe Pristina (GE, Buc, France). Along with differences such as angular range and detectors type, the systems use different reconstruction algorithms. One was available for the GE system, based on iterative reconstruction (IR). Two algorithms were available for the Siemens system: TOMO_STANDARD, using filtered back projection (FBP) and EMPIRE, FBP with statistically based artifact reduction. Two commercially available DBT phantoms (CIRS model 020 & 021), with heterogeneous and homogenous background respectively, were used to calculate signal-difference-to-noise-ratio (SDNR) in key structures for varying phantom thickness (30, 45 & 70 mm) and average glandular dose (AGD). Key phantom structures include calcifications and lesion masses of different sizes. Results show a positive correlation between SDNR and AGD except for the EMPIRE algorithm where there was a negative SDNR/AGD trend for one of the microcalcification specks in the heterogeneous phantom. The highest overall SDNR was acquired using the EMPIRE algorithm. Both systems are well within the recommended dose limits but could increase their dose levels in order to achieve higher SDNR. This indicates that there may be room for dose optimization in DBT systems used in screening programs, confirming the importance of continuous evaluation and optimization.
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