No 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 ( ). No significant difference ( ) 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.

Artificial intelligence (AI) and mechanical imaging (MI) have been used in separate studies in breast imaging. They have individually shown great possibilities within the field of mammography, but the use of the two techniques together have never been evaluated. The artificial intelligence application used in this work was Transpara, a deep learning convolutional neural network. It distinguishes patterns in the mammographic images and provides scores of individual findings and the whole mammographic examination, which indicates a level of suspicion for breast cancer. Mechanical imaging is a surface stress measurement, that provides information of the mechanical structure of the underlying tissue.Since malignant tumours often express a higher relative pressure compared to the surrounding tissue in the breast, mechanical imaging is comparable with palpation but could provide even more information of the mechanical structures.The purpose of this work was to study if the combination of the two methods could be used to directly detect breast cancer. Screening images of 118 women were analysed in Transpara, and the pressure distribution measurement of the same women was obtained from a previous study on MI. For 46 cases, there was compression pressure present over the AI-findings, and these were chosen to be included in the analysis. Locations of findings with the highest level of suspicion and the corresponding locations in the pressure measurement were used to calculate the mean relative pressure over a finding. The cases were divided into three groups by diagnosis; biopsy-proven cancer, biopsy-proven benign and non-biopsied, very likely benign. The increased pressure was then compared among these three groups and the two groups of cancer and healthy, to evaluate if the increased pressure over Transpara scores of women diagnosed with cancer was different from those diagnosed as healthy. The correlation between increased pressure and Transpara score was evaluated for each group, to evaluate if the two methods found the same indications for breast cancer.The results of this study indicated that there probably are differences in increased pressure between cases with breast cancer and healthy, but it remains to further evaluated for a larger material. A significant and relatively strong correlation between the relative pressure increase over an AI-finding and the Transpara scores was established in the group with cancer, but the other groups showed no correlation.This study indicates that MI combined with AI can potentially be used to improve the performance of mammography screening. It suggests that AI and MI find independent markers that coincide in breast cancer. Therefore, the two methods have the potential of lowering the recall rate in mammography, but this needs to be further evaluated.

No abstract