Have a look at my PhD thesis (2nd Chapter) where I summarized and compared the mammographic
parenchymal texture techniques (through Machine Learning) used in various applications such as, microcalcification detection,
mass characterization and tissue characterization. Emphasis is given on techniques that
have been used for risk assessment application on screening mammograms. Performance is
evaluated and compared by Receiver Operative Characteristic curve analysis on one of the most
commonly used database available in public domain, such as mini-MIAS and DDSM.
(from Chapter 4-7) I also compared DDSM dataset with other case control study such as Nijmegen, HRT, and ER specific risk mammgram data.
I hope it helps you.