Localized Probability of Mammographic Masking
Creators: name: University Of California Office Of The President
Publisher: name: University of California Office of the President
publisherIdentifier: https://ror.org/00dmfq477
publisherIdentifierScheme: ROR
Year: 2024

description: Interval cancers, diagnosed in the time after a normal screening examination and before the subsequent screen, are common in young women with dense breast, and as a general rule, have characteristics of rapid growth, and are frequently of advanced stage at the time of discovery. It has been reported that 68% of the 12-month interval cancer cases have high density breast. Importantly, this means that 32% of interval cancers are in women with low breast density. Thus breast density alone is not the entire picture. The quality of the density can obscure cancers as well. We call this quality texture. Furthermore, breast density and texture varies through all breasts. Describing the probability of a cancer hiding in a woman’s breast based on the average density and texture most certainly draws attention away from smaller regions of high masking probability, yet this is the only way masking has been describe to date. In this proposal, we create a method to report to the radiologist the local probability of masking throughout the breast. This approach personalizes the search for cancer by providing a means to target high masking regions of the breast with follow-up screening that removes the masking effect for that woman’s specific type of breast tissue. Having localized regions of high breast density with complex tissue texture in an otherwise lower density breasts is common. However, the frequency and size of these high masking areas are not known for women undergoing mammography. The studies that objectively assess the probability of local masking are lacking. 
 
 
 
 
 
 
 
 The question(s) or central hypotheses of the research: The central hypothesis of this proposal is that the water, lipid, and protein content of breast lesions varies sufficiently according to lesion type to provide clinically useful diagnostic information. 
 
 
 
 
 
 
 
 The general methodology: We plan to develop a method that can directly measure masking probability by superimposing cancer-like shapes into a woman’s mammogram and determine what is the minimum size and density of these objects that can be detected. Using this information, we can determine the probability for local regions in the mammogram masking a cancer. We will express this as a 0 to 100% detectability score. We will then calibrate this score in interval cancers in women and study what breast tissue qualities are then associated with a low detectability (high masking.) We will then review screening mammograms from 1000 women to estimate how common it is to have high masking regions of different sizes. Our group is uniquely qualified for this research since we have virtually all the software elements already developed – they just need to be used together. Lastly, when successful, this technique could be rapidly implemented since it doesn’t alter the way mammograms are acquired. 
 
 
 
 
 
 
 
 Innovative elements of the project: Our group is one of the few groups in the world that has validated an accurate and precise way to measure localized breast density. We also have developed a library of breast texture measures. A better understanding of the local tissue properties can be gained in terms of objective tissue quality measures such as localized volumetric breast density and breast texture. Once understood, adjuvant imaging modes that specifically address the particular masking effect for individual women could be used to target the high masking probability regions. Selectively probing of high masking areas may also reduce the time, cost, dose, and stress related to call backs.