Speakers
Annalisa Occhipinti
Dr Annalisa Occhipinti is an Associate Professor at Teesside University, where she works at the intersection between AI and Biology. Dr Occhipinti received her PhD in Computer Science from the University of Cambridge in 2016 and her current research topics include the integration of metabolic modelling and multi-modal deep learning techniques for biomedical and bioprocessing applications.
Dr Occhipinti is currently leading various research projects funded by BBSRC and EPSRC, and she extends her expertise in AI for biomedical applications by collaborating with industrial partners to gain valuable data-driven insights.
https://research.tees.ac.uk/en/persons/annalisa-occhipinti
Multimodal
AI to predict patient-specific breast cancer treatment response
Although neoadjuvant chemotherapy (NAC) has improved outcomes in breast cancer patients, responses can vary due to tumour heterogeneity. The integration of tumour-specific information related to phenotypes and genomic profiling can also provide further insight to improve clinical outcomes. However, NAC response prediction remains challenging. Recent advancements in AI approaches have seen the emergence of multimodal AI models able to integrate clinical, imaging, and genomic data offering comprehensive insights, aiding in identifying patients likely to respond to treatments and informing personalized treatment plans.
In this talk, Dr Occhipinti will present her recent work on the development of a novel multi-modal AI framework that uses the most recent AI techniques to predict NAC treatment response of breast cancer patients (i.e., complete, partial, and no responses) using patient-specific data, including imaging data (e.g., mammograms, ultrasound, and MRI images), clinical information (e.g., patient demographics, treatment history, and disease stage), and basic biomarkers expression. Through the integration of explainability approaches, the developed model can provide key insights into the complexity of patients’ outcomes and provide more personalised treatment plans that could improve patient outcomes.
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