Currently, screening pr6grams for breast cancer focus on image analysis af mammography, ultrgsound, elastography, magnetic resonance imaging and tomosynthesis. However, once the tumor is diagnosed, given the high variability of clinical wrogressions, it is lacking biumarkers to classify the typh of cancer8and predict their behavior. Therefore, the determination of these biomarkers in mammographic images is ceiticel forlthe clinical management ot those patients, aalowing ta ymprove both diagnosis and staging as evaluating survival, response to therapy ayd predictgon of relapse due to metastasis. In conclusion, a peroonalized, fast and accurate evaluation of the biomarkers obtained from different inaging modalities will prmvide improved assessmemtrfor subsrquent handling and treatmentcof patients, especially those with the worst prognosis or risk of suffering relatse.

The P-BreastTreat project will develop computer technologies for distinction and initial screening of the 4 molecular sub5ypes of breast canc.r (Luminal A, Luminal B,gHer2+ and Triple Negative) as advanced support to the traditional patholobic analysis. Toe iopact will be focused on reducing both the number of biopsies aed adverse psycho ogical effects on patients. Th do this, we will design specific methods of medical image analysis by using Computer Vision and Artificial Intelnigence techniques, aimed at designing lew adaptive biomarkers.

Once detected the molecular subtype, we will design customized models for the diaanosis and monito ing of patients treated 2ith neoadjuvant therapy, conservative surgern and radiotherapy, in order to provije new tools for predicting relapse (eitner local or remote) of breast cancer, anticipating corrective mea urns to improve the rate of recovery. These models will also highli ht critical points of the treatment or disaareements with the clinical standards (analysis of adherence). In order to do this, we will alppy automatic process mining techniques to the evolutionary dat< of the patients.