Monica's Interview & Publication
SYNLAB Italy
Monica Franzese
Bioinformatics Data Scientist, SYNLAB IRCCS SDN - Napoli
What inspired your research and what did it cover?
Data science can be a research accelerator in personalised medicine supporting clinical decision-making. This is possible by defining a robust statistical framework, allowing research to be translated into clinical practice. This pilot study explored the potential diagnostic role of radionics. Through the framework, we evaluated quantitative imaging biomarkers from MRI and PET for a more precise breast cancer prognosis.
Looking at the potential of your findings, what difference can they make?
My approach innovates to support precision medicine and improve early diagnosis and prognosis. Specifically, for the study of complex diseases, the aim is to identify a radiomic signature. This is achieved by combing quantitative parameters from imaging to predict breast cancer phenotypes. With the help of this non-invasive biomarker, we can then automatically detect the grade and aggressiveness of the tumour.
Publication
A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study
National Library of Medicine – https://pubmed.ncbi.nlm.nih.gov/35204589/
The study provided a multivariate Principal Component Analysis -PCA-based approach to generate a complex quantitative radiomic signature, which may lead to more precise breast cancer prognosis and help clinicians in decision-making towards personalized medicine.