The present study used DBT images to apply a radiomic approach aiming to assess if an association between breast cancer Ki-67 expression and radiomic features exists. Our findings show that after applying methods to select the most predictive features to differentiate low versus high Ki-67 expression, it is possible to identify several features with ROC-AUC values from 0.653 to 0.698 to differentiate patients with low versus high Ki-67 expression. We could speculate that with refinements in textural analysis techniques and increased reproducibility radiomic analysis, it may be possible to obtain an estimation of Ki-67 expression directly from DBT images even before biopsy, estimating the activity of the tumour (namely cell proliferation) directly from DBT images obtained at diagnosis. This hypothesis was already developed by a previous pilot study where quantitative radiomic imaging features of breast tumour extracted from DCE-MRI were associated with breast cancer Ki-67 expression . In the present study, we showed that using an imaging modality such as DBT, used in breast cancer screening and diagnosis and more widely available than MRI, it is possible to find an association between quantitative radiomic imaging features and Ki-67 expression. The data reported in the present study are encouraging because they support the hypothesis that radiomic features could predict Ki-67 expression of breast cancer (and hence an element of tumour biology) in a non-invasive manner.
We selected features that simultaneously resulted in an AUC > 0.6 with a minimum p value of 0.05 in differentiating between low and high Ki-67 expression on DBT. This allowed us to identify five features with these requirements, whereas a previous study that was based on DCE-MRI found 13 features associated with Ki-67 expression (similarly meeting p < 0.05 and AUC > 0.6 criteria) . As a whole, the meaning of these features could be that high Ki-67-expressed lesions are more likely to be inherently heterogeneous. The five DBT-based features identified in this study differ from the DCE-MRI-based features, but they reflect intra-tumoural heterogeneity as well. Moreover, the type of information contained in MRI versus DBT images is different due to the different biophysical characteristics of the two imaging modalities.
Our study suggests that tumoural biological aggressiveness related to high Ki-67 expression can be evaluated on DBT, avoiding the need of contrast media injection which is used for MRI. In this study, we did not find features with AUC higher than 0.7; therefore, it seems possible that radiomic features on DBT do not strongly discriminate low and high Ki-67-expressing breast cancers and further evaluation in larger studies is needed. However, future studies could examine the added value of radiomic evaluation (added to other prognostic factors) in tumoural aggressiveness assessment. A previous study  on radiomics and DBT performed in women with dense breast found a correlation between radiomic features and tumour size and oestrogen receptors: three radiomic features (energy, entropy, and dissimilarity) correlated with tumour size and entropy correlated with oestrogen receptor status. This study supports the use of radiomics to assess tumoural characteristics on breast cancer patients on digital breast tomosynthesis in further studies based on larger datasets to extend these exploratory data.
This study has several limitations. The first limitation is that images are from a single vendor and acquired at a single institution; therefore, a multicentric study would be important to assess if these results are valid on a larger scale and could be generalised. The second limitation is that tumour lesions were manually segmented by human readers and reproducibility was not assessed. As a major limitation, we acknowledge that there are no data on reproducibility in this study, although the methods of the present paper were already tested in our previous paper (intra-observer agreement of 0.78) .
The third limitation is that we have few published data regarding DBT and radiomics, and this is one of the first studies exploring associations between radiomic features and Ki-67 expression on DBT; therefore, comparison with previously published data is limited.
However, the results of this study are not sufficient to make decisions in clinical practice because of the relatively limited AUC values found for the features and because of these results need to be replicated by other researchers. In addition, the study design was not intended to create an algorithm or a radiomic nomogram to differentiate low from high Ki-67 expression. We acknowledge the exploratory nature of the present research.
However, this study could be used for future study not only as a proof-of concept investigation but also as a reference for comparisons.
In terms of strengths, whilst noting this as an exploratory study, it is nonetheless the first study to use data from images prospectively acquired with the same technique reducing biases due to image acquisition geometry and reconstruction algorithm typical of DBT prototypes. Also, we used a freely available software enhancing the ability to replicate the research by other independent groups.
In conclusion, this study showed that some quantitative imaging features extracted from DBT acquired in clinical practice are associated with breast cancer Ki-67 expression. Our findings can inform new studies on DBT-based radiomics and breast cancer biology.