Using Artificial Intelligence to Help Predict Treatment Response in Patients with Blood Cancer
Lead Research Organisation:
University of Liverpool
Department Name: Electrical Engineering and Electronics
Abstract
Cancer is a leading cause of death worldwide. Blood cancer is the fifth most common cancer in the UK. Patients who are diagnosed with blood cancers, such as myeloma and lymphoma, undergo treatments like chemotherapy and radiotherapy to help them survive cancer. Although most patients can benefit from them to varying degrees, different patients respond differently to the same treatment. No single treatment is totally suitable for everyone.
Therefore, it is essential to find optimal treatments for individual patients to improve treatment outcomes, reduce their pain and discomfort, and minimize costs for the NHS. A highly demanded solution is a personalized medicine decision-making tool to address these challenges and streamline clinical services.
The project's objective is to develop and evaluate new artificial intelligence (AI) tools for predicting patients' responses to treatments more accurately than is currently possible. This test has the potential to guide doctors on which treatments are most likely to benefit individual cancer patients.
Positron Emission Tomography/Computed Tomography (PET/CT) images are recognised as having predictive significance in evaluating a tumor's prognosis. Using state-of-the-art AI technology, this interdisciplinary project will leverage experimental data primarily provided by The Clatterbridge Cancer Centre NHS Foundation Trust (CCC) and the Liverpool University Hospitals NHS Foundation Trusts (LUHFT). The large-scale, multidimensional data will include PET/CT image data, clinical symptoms, and demographic data collected by healthcare partners.
New AI tools, utilising both deep learning and statistical learning, will be developed and evaluated by using these unique multimodal data to achieve efficient and accurate clinical decision-making for predicting treatment outcomes. We will ensure that the new AI tools are both accurate and explainable, making them trustworthy. These tools will be evaluated by our clinical partners at CCC and LUHFT.
Therefore, it is essential to find optimal treatments for individual patients to improve treatment outcomes, reduce their pain and discomfort, and minimize costs for the NHS. A highly demanded solution is a personalized medicine decision-making tool to address these challenges and streamline clinical services.
The project's objective is to develop and evaluate new artificial intelligence (AI) tools for predicting patients' responses to treatments more accurately than is currently possible. This test has the potential to guide doctors on which treatments are most likely to benefit individual cancer patients.
Positron Emission Tomography/Computed Tomography (PET/CT) images are recognised as having predictive significance in evaluating a tumor's prognosis. Using state-of-the-art AI technology, this interdisciplinary project will leverage experimental data primarily provided by The Clatterbridge Cancer Centre NHS Foundation Trust (CCC) and the Liverpool University Hospitals NHS Foundation Trusts (LUHFT). The large-scale, multidimensional data will include PET/CT image data, clinical symptoms, and demographic data collected by healthcare partners.
New AI tools, utilising both deep learning and statistical learning, will be developed and evaluated by using these unique multimodal data to achieve efficient and accurate clinical decision-making for predicting treatment outcomes. We will ensure that the new AI tools are both accurate and explainable, making them trustworthy. These tools will be evaluated by our clinical partners at CCC and LUHFT.
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/S023445/1 | 01/04/2019 | 30/09/2027 | |||
| 2889845 | Studentship | EP/S023445/1 | 01/10/2023 | 30/09/2027 | Ruojun Zhang |