  Lesion volume is an important predictor for prognosis in breast cancer. We
make a step towards a more accurate lesion volume measurement on digital
mammograms by developing a model that allows to estimate lesion volumes on
processed mammograms, which are the images routinely used by radiologists in
clinical practice as well as in breast cancer screening and are available in
medical centers. Processed mammograms are obtained from raw mammograms, which
are the X-ray data coming directly from the scanner, by applying certain
vendor-specific non-linear transformations. At the core of our volume
estimation method is a physics-based algorithm for measuring lesion volumes on
raw mammograms. We subsequently extend this algorithm to processed mammograms
via a deep learning image-to-image translation model that produces synthetic
raw mammograms from processed mammograms in a multi-vendor setting. We assess
the reliability and validity of our method using a dataset of 1778 mammograms
with an annotated mass. Firstly, we investigate the correlations between lesion
volumes computed from mediolateral oblique and craniocaudal views, with a
resulting Pearson correlation of 0.93 [95% confidence interval (CI) 0.92 -
0.93]. Secondly, we compare the resulting lesion volumes from true and
synthetic raw data, with a resulting Pearson correlation of 0.998 [95% CI 0.998
- 0.998] . Finally, for a subset of 100 mammograms with a malign mass and
concurrent MRI examination available, we analyze the agreement between lesion
volume on mammography and MRI, resulting in an intraclass correlation
coefficient of 0.81 [95% CI 0.73 - 0.87] for consistency and 0.78 [95% CI 0.66
- 0.86] for absolute agreement. In conclusion, we developed an algorithm to
measure mammographic lesion volume that reached excellent reliability and good
validity, when using MRI as ground truth.
