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ECR 2021. Smart Imaging and Cancer Care: Improving Integration

ECR 2021. Smart Imaging and Cancer Care: Improving Integration
Better integration of smart imaging techniques could improve cancer diagnosis and treatment. (Credit: Cancer Research UK Cambridge Centre)

Better integration of intelligent imaging techniques could have a significant impact on improving cancer diagnosis and treatment, according to experts in the field.

The subject was discussed at ECR 2021, namely at an industry symposium by GE Healthcare entitled “Integrated Cancer Care with Intelligent Imaging.” Participants suggested everyone with an interest—from hospitals and professional medical staff to academics, industry and patient groups—must work together to achieve this. Dr. Claire Bloomfield, Chief Executive Officer for National Consortium of Intelligent Medical Imaging University of Oxford, said:

“You need all those different groups involved. You also need to link up primary and tertiary care and to join patient pathways: it is about trying to bridge the gaps and bring the people together.”

Virtual biopsy using MRI radiomics. (Credit: Nature)
Virtual biopsy using MRI radiomics. (Credit: Nature)

This model has been used in the UK’s Lung Health Check program. Dr. Bloomfield explained:

“This is really about trying to support lung cancer screening programs and, uniquely, embedding research into that from the outset. One scoping of this is looking at whether we can integrate PET-CT and biopsy digital pathology data to generate new AI [artificial intelligence] solutions which could entirely remove the need for surgical biopsies.”

Virtual biopsies using radiomics to understand the biochemical signatures within imaging could be a means to predicting outcomes and response to therapies. She added:

“If you remove surgical biopsies globally, this has a significant impact on care and mortality.”

Focusing on Data Integration

Richard Gilbertson, Professor of Oncology and Director of the Cancer Research UK Cambridge Centre and co-lead of the Cambridge Experimental Cancer Medical Centre, agreed that data integration was key. He said:

“We have to look at how we use AI and other approaches mathematically to focus on one result in particular because the relevance will be context-dependent. This might be one point in the disease or when it is associated with another aspect of the disease—and that is an AI deep learning problem.”

He said hyperpolarized imaging (HP) had the advantage of providing a real-time readout, essentially of the biochemistry of a tumor:

“If we put patients on a phase one or two trial at the moment, we still require them to go through two or three cycles of a drug before we get a readout whereas HP can give a biochemical readout within one cycle.”

3D-printed molds of rela tumor (Credit: BioRxiv)
3D-printed molds of real tumor (Credit: bioRxiv)

He also believed radiomics held great potential:

“For renal cancer in the Integrated Cancer Medicine Program, we are using imaging to build 3D molds through 3D printing. The tumor is then resected by the surgeon and can sit in the mold. This allows the individual to orientate the section of that tumor and then do habitat genomics and cross-reference that back to what was seen in the imaging. In something as complex as ovarian cancer, when patients are going for surgery it’s not uncommon to need an upper gastrointestinal surgeon because the tumor is not where it is meant to be. The radiomics approach is to be able to predict not just where the tumor is but what it is likely to do prognostically and what sort of team you will need for management.”

Employing the Science

Challenges to improving the integration of intelligent imaging include exhausted healthcare teams, workforce shortages and inadequate IT systems. Changes have to be practical and deployable, Dr. Bloomfield and Prof Gilbertson agreed. Prof Gilbertson said:

“We want to take the science and allow it to have an impact on real-world problems so we can really bring and rapidly progress it to the advantage of patients.”

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