University of Cambridge > Talks.cam > AI in Medical Imaging Research Seminar > AI in Medical Imaging Research Seminar – Nikita Sushentsev – "Time-series Radiomics: top or flop? - A new framework for longitudinal medical image analysis"

AI in Medical Imaging Research Seminar – Nikita Sushentsev – "Time-series Radiomics: top or flop? - A new framework for longitudinal medical image analysis"

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If you have a question about this talk, please contact Ines Machado.

You are invited to attend the second seminar of a series to be held monthly (please see details below). The aim of which is to exchange information and ideas on AI applied to imaging.

The Cancer Research UK Cambridge Centre and the Department of Radiology at Addenbrooke’s are pleased to announce the launch of a seminar series to support campus-wide efforts to engage with the fast-developing field of Artificial Intelligence (AI) applied to medical imaging. The seminars will take place on a regular basis (monthly) with the aim to exchange information and ideas on AI applied to imaging.

The second event will take place on the 25th of October at 10am in the Berridge room on L5 at the Department of Radiology, Addenbrooke’s hospital.

We are delighted to introduce our second speaker Nikita Sushentsev. Nikita is a Gates Scholar and PhD student at the Department of Radiology, University of Cambridge. His research, supervised by Prof Tristan Barrett and Prof Ferdia Gallagher, is primarily focused on prostate cancer metabolic imaging using hyperpolarised 13C-MRI. His parallel interest, however, concerns the development of MRI -derived radiomic models for improving the prediction of prostate cancer progression on active surveillance. Working in close collaboration with machine learning experts Prof Leonardo Rundo (University of Salerno) and Dr Oleg Blyuss (Queen Mary University of London), Nikita has pioneered the use of conventional radiomics in prostate cancer active surveillance, showing its potential for improving the use of MRI in this clinical setting. In this talk, he will present work related to the development of a new framework of time-series radiomics, which enables longitudinal assessment of tumour imaging features derived from multiple scans and shows superior performance compared to traditional methods.

We hope to host seminars on a monthly basis during term times open to all Biomedical Campus staff, students and affiliates. The seminars are intended for a multidisciplinary audience. Presentations last approximately 30 minutes, after which there is plenty of time for questions and discussions. If you have any questions or are interested in presenting your research work, please email Dr Dimitri Kessler (dak50@cam.ac.uk) or Dr Ines Machado (im549@cam.ac.uk).

This is a hybrid event so you can also join via Zoom:

https://zoom.us/j/99050467573?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz09 Meeting ID: 990 5046 7573 Passcode: 617729

This talk is part of the AI in Medical Imaging Research Seminar series.

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