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International projects source: SICRIS

A comprehensive CAD system based on radiologic- and pathologic-image biomarkers for diagnosis and prognosis of breast cancer relapse

Researchers (1)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  55177  PhD Umut Arioz  Telecommunications  Researcher  2022 - 2024  35 
Organisations (3)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0334  University Medical Centre Maribor  Maribor  5054150000  22,768 
2.  0552  University of Maribor  Maribor  5089638000  454 
3.  3356  SFERA IT Storitve d.o.o. (Slovene)  Maribor  3367797 
Abstract
Breast cancer (BC) incidence in women produces more than 600,000 deaths each year. The primary cause of death in BC patients is metastasis, whereby cancer cells spread from their primary site of origin and grow in adjacent or distant sites. Distant metastasis produced due to the relapse of the illness is incurable, underscoring the inadequacy of our understanding of its mechanisms. The first step for fighting against disease progression is screening programs for BC focused on image analysis of mammography, MRI and tomosynthesis. Once the tumour has been diagnosed and given the high variability of clinical progressions, another problem arises: classifying the cancer type and determining the proper treatment for specific cancer. Moreover, in BC, the immune response from the tumour microenvironment has played an essential role in tumour evolution. To evaluate the tumour and its microenvironment, one technique garnered a lot of attention in the last years: Whole Slide Imaging (WSI). This technique replaces the use of the microscope for classical diagnosis. Still, it has also been used for developing biomarkers that allow the analysis of tumours and classification of cancer subtypes and the study of the immune tumour microenvironment. The use of WSI has applications for predicting the probability of relapse for distant metastasis. Now, for the first time, BosomShield proposes to join the two disciplines (pathological and radiological imaging) in a software that will analyze these images to classify the cancer subtypes and predict (together with the complete clinical history of the patient) the probability of relapse for distant metastasis. Besides, BosomShield will provide high-level training in BC research to young researchers by offering the necessary transferable skills for thriving careers underpinned using diverse disciplines, digital radiology and pathology, biomedical, AI, privacy and software development.
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