̽��ֱ�� of Cambridge - Evis Sala

̽��ֱ��women helping to change the story of ovarian cancer

lw355 — Mon, 24 Jan 2022 13:40:02 +0000

Every patient with cancer has a story to tell of their journey through diagnosis and treatment. We meet a group of women who are at the centre of pioneering research in Cambridge that’s changing the outcome of ovarian cancer – helping to create treatments that are as unique as their stories.

Collaboration could enable cancer patients to get faster and more personalised treatment

cjb250 — Mon, 29 Nov 2021 00:01:15 +0000

Building on research supported by ̽��ֱ��Mark Foundation for Cancer Research and Cancer Research UK, the collaboration aims to address the problems of fragmented or siloed data and disconnected patient information, which is challenging for clinicians to manage effectively and can prevent cancer patients receiving optimal treatment.

“Thanks to ever-improving technologies, we now generate increasing amounts of complex data for each patient with cancer,” said Professor Richard Gilbertson, Director of the Cancer Research UK Cambridge Centre, and Head of the Department of Oncology at the ̽��ֱ�� of Cambridge. "These include multiple imaging scans, digital pathology, genomic data, advanced blood tests and treatment information. Bringing all this data together to make precise and informed decisions for patients can be hard. We often do this inefficiently and miss important connections between the data."

This new application would be designed using advanced software engineering and machine learning methods to integrate a variety of patient data including clinical, imaging and genomic data - from diagnosis through every stage of treatment - into one single location. ̽��ֱ��aim is to offer all medical teams involved in a patient’s cancer care - medical oncologists, clinical oncologists, surgeons, radiologists, pathologists, clinical nurse specialists and more - simultaneous access to the necessary data and information to allow the medical team to plan the best, most personalised treatment for each of their patients.

̽��ֱ��application is expected to be evaluated for ovarian cancer initially in Cambridge and the goal is to evaluate it across the UK, and beyond. Ovarian cancer is often difficult to treat as most patients present with advanced disease. Although initially 70-80% of patients will respond well to chemotherapy, ultimately most develop chemotherapy resistance leading to treatment failure. ̽��ֱ��application may help clinicians have better visibility on how the patient respond to treatment, thus helping them more effectively identify when treatment may require adjustment. If the application is successfully developed, our vision is for it to be expanded for use in breast and kidney cancer patients.

“Healthcare professionals can struggle to easily find and interpret the many different types of patient data information they need to make the best clinical decisions,” said Dr Ben Newton, GM Oncology at GE Healthcare. “Bringing these multiple data streams into a single interface could enable clinicians to make fast, informed and highly personalised treatment decisions throughout a patient’s cancer care pathway.”

Two Addenbrooke’s cancer clinicians aiming to evaluate the application to help patients are consultant oncologist Professor James Brenton, professor of Ovarian Cancer Medicine and a senior group leader at the Cancer Research UK Cambridge Institute; and consultant radiologist Professor Evis Sala, professor of Oncological Imaging, ̽��ֱ�� of Cambridge.

“Aggregating and analysing the substantial amounts of data available would help address an unmet need. Ovarian cancer is an important and complex disease with poor outcomes, and we believe this application would help us deal with its complexity. Eventually, we hope to be able to better understand the disease and therefore improve treatment and outcomes for patients,” says Professor Brenton, who co-leads the Mark Foundation Institute for Integrated Cancer Medicine (MFICM) at the ̽��ֱ�� of Cambridge.

“If we can aggregate and integrate relevant data along the care pathway, and visualize the output, it may ultimately lead to clinicians making better-informed decisions and better care.” adds Professor Sala who also co-leads the MFICM at the ̽��ֱ�� of Cambridge.

“ ̽��ֱ��team aims to transform the delivery of cancer patient care by integrating multiple data streams together into a single platform that can be accessed simultaneously by clinicians, patients and multi-disciplinary teams from tertiary and regional hospitals.”

̽��ֱ��development work will be underpinned by GE Healthcare’s Edison platform to integrate data from diverse sources, such as electronic health records and radiology information systems, imaging and other medical device data.

GE Healthcare, the ̽��ֱ�� of Cambridge and Cambridge ̽��ֱ�� Hospitals have agreed to collaborate on developing an application aiming to improve cancer care, with Cambridge providing clinical expertise and data to support GE Healthcare’s development and evaluation of an AI-enhanced application that integrates cancer patient data from multiple sources into a single interface.

Ovarian cancer is an important and complex disease with poor outcomes, and we believe this application would help us deal with its complexity

James Brenton

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‘Virtual biopsies’ could replace tissue biopsies in future thanks to technique developed by Cambridge scientists

cjb250 — Wed, 06 Jan 2021 09:47:55 +0000

̽��ֱ��research published in European Radiology shows that combining computed tomography (CT) scans with ultrasound images creates a visual guide for doctors to ensure they sample the full complexity of a tumour with fewer targeted biopsies.

Capturing the patchwork of different types of cancer cell within a tumour – known as tumour heterogeneity – is critical for selecting the best treatment because genetically-different cells may respond differently to treatment.

Most cancer patients undergo one or several biopsies to confirm diagnosis and plan their treatment. But because this is an invasive clinical procedure, there is an urgent need to reduce the number of biopsies taken and to make sure biopsies accurately sample the genetically-different cells in the tumour, particularly for ovarian cancer patients.

High grade serous ovarian (HGSO) cancer, the most common type of ovarian cancer, is referred to as a ‘silent killer’ because early symptoms can be difficult to pick up. By the time the cancer is diagnosed, it is often at an advanced stage, and survival rates have not changed much over the last 20 years.

But late diagnosis isn’t the only problem. HGSO tumours tend to have a high level of tumour heterogeneity and patients with more genetically-different patches of cancer cells tend to have a poorer response to treatment.

Professor Evis Sala from the Department of Radiology, co-lead CRUK Cambridge Centre Advanced Cancer Imaging Programme, leads a multi-disciplinary team of radiologists, physicists, oncologists and computational scientists using innovative computing techniques to reveal tumour heterogeneity from standard medical images. This new study, led by Professor Sala, involved a small group of patients with advanced ovarian cancer who were due to have ultrasound-guided biopsies prior to starting chemotherapy.

For the study, the patients first had a standard-of-care CT scan. A CT scanner uses x-rays and computing to create a 3D image of the tumour from multiple image ‘slices’ through the body.

̽��ֱ��researchers then used a process called radiomics – using high-powered computing methods to analyse and extract additional information from the data-rich images created by the CT scanner – to identify and map distinct areas and features of the tumour. ̽��ֱ��tumour map was then superimposed on the ultrasound image of the tumour and the combined image used to guide the biopsy procedure.

By taking targeted biopsies using this method, the research team reported that the diversity of cancer cells within the tumour was successfully captured.

Co-first author Dr Lucian Beer, from the Department of Radiology and CRUK Cambridge Centre Ovarian Cancer Programme, said of the results: “Our study is a step forward to non-invasively unravel tumour heterogeneity by using standard-of-care CT-based radiomic tumour habitats for ultrasound-guided targeted biopsies.”

Co-first author Paula Martin-Gonzalez, from the Cancer Research UK Cambridge Institute and CRUK Cambridge Centre Ovarian Cancer Programme, added: “We will now be applying this method in a larger clinical study.”

Professor Sala said: “This study provides an important milestone towards precision tissue sampling. We are truly pushing the boundaries in translating cutting edge research to routine clinical care.”

Fiona Barve (56) is a science teacher who lives near Cambridge. She was diagnosed with ovarian cancer in 2017 after visiting her doctor with abdominal pain. She was diagnosed with stage 4 ovarian cancer and immediately underwent surgery and a course of chemotherapy. Since March 2019 she has been cancer free and is now back to teaching three days a week.

“I was diagnosed at a late stage and I was fortunate my surgery, which I received within four weeks of being diagnosed, and chemotherapy worked for me. I feel lucky to be around,” said Barve.

“When you are first undergoing the diagnosis of cancer, you feel as if you are on a conveyor belt, every part of the journey being extremely stressful. This new enhanced technique will reduce the need for several procedures and allow patients more time to adjust to their circumstances. It will enable more accurate diagnosis with less invasion of the body and mind. This can only be seen as positive progress.”

This feasibility study, involving researchers from the Department of Radiology, CRUK Cambridge Institute, Addenbrooke’s Hospital, Cambridge ̽��ֱ�� Hospitals NHS Foundation Trust, and collaborators at Canon, was facilitated through the CRUK Cambridge Centre Integrated Cancer Medicine programme.

̽��ֱ��goal of Integrated Cancer Medicine is to revolutionise cancer treatment using complex data integration. Combining and integrating patient data from multiple sources – blood tests, biopsies, medical imaging, and genetic tests – can inform and predict the best treatment decisions for each individual patient.

̽��ֱ��study was funded by Cancer Research UK and ̽��ֱ��Mark Foundation for Cancer Research.

Reference
Lucian Beer, Paula Martin-Gonzalez et al. Ultrasound-guided targeted biopsies of distinct CT based radiomic tumour habitats: proof of concept. European Radiology; 14 Dec 2020; DOI: 10.1007/s00330-020-07560-8

A new advanced computing technique using routine medical scans to enable doctors to take fewer, more accurate tumour biopsies, has been developed by cancer researchers at the ̽��ֱ�� of Cambridge. This is an important step towards precision tissue sampling for cancer patients to help select the best treatment. In future the technique could even replace clinical biopsies with ‘virtual biopsies’, sparing patients invasive procedures.

This study provides an important milestone towards precision tissue sampling. We are truly pushing the boundaries in translating cutting edge research to routine clinical care

Evis Sala

Image showing individual and combined scans

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Harnessing AI in the fight against COVID-19

sc604 — Thu, 04 Jun 2020 01:00:00 +0000

An open-source artificial intelligence (AI) tool, combining chest imaging data with laboratory and clinical data, is being developed by Cambridge researchers to support the rapid diagnosis and triaging of patients with COVID-19 in the UK.

̽��ֱ��team, led by Professors Carola-Bibiane Schönlieb and Evis Sala, brings together expertise in AI for imaging with expertise in radiology and clinical applications from Addenbrooke’s and Papworth Hospitals, as well as collaborators from the UK, China, Austria and Italy, to develop a prediction model that can rapidly and reliably diagnose and suggest a prognosis to doctors.

Reverse-transcription polymerase chain reaction (RT-PCR) tests are currently the most common tool used to diagnose COVID-19, but they are only up to 70% sensitive, meaning there are up to 30% false negatives.

While chest X-rays and CT scans provide valuable diagnostic and monitoring information that can complement laboratory and clinical data, it is a complex task typically done by radiologists, whose expertise is often in high demand. Fast and accurate diagnosis of patients in order to limit disease spread, together with the rapid determination of whether a patient is likely to recover, require intensive care unit (ICU) admission, or intensive ventilation, is key to allocating resources and to improving patient outcomes.

“AI offers huge potential to support agile clinical decision making, ensuring patients receive the most appropriate support and leading to better patient outcomes,” said Sala, who is based at the Department of Radiology.

Recent studies have suggested that using AI could have a meaningful impact on the management of patients with COVID-19. AI tools such as deep learning can offer automated image analysis and integration with clinical data to help clinicians make more informed decisions for treatment.

However, good quality data and computing power are required to train and optimise predictive AI models and data availability is a major bottleneck when developing new systems. Coupled with this, the lack of standardisation of datasets makes it challenging to reuse existing AI tools in a different country than the one that it was trained for. Most current AI tools have been developed on small, locally collected datasets. Data that is being collected in hospitals all over the world varies in what is being collected and how the data is processed. Therefore, an effort for developing a widely applicable tool for COVID-19 hospital support must be open source so it can be adapted to different environments; be based on a serious data sharing and data curation, data cleaning and standardisation effort; and be developed with mathematical, statistical and engineering expertise to develop robust and translatable tools.

To address these challenges, the team from the Cambridge Centre for Mathematical Imaging in Healthcare (CMIH) is developing a flexible, open-source AI tool that could be used by hospitals worldwide. Drawing on their history of global research collaboration and expertise in data governance, the team is gathering datasets from Austria, China, Italy and the UK for their work. Data scientists and clinicians are working in close collaboration, following standard protocols to identify bias during development. “Rigorous mathematical models play a key role in mitigating bias and improving the efficacy of the prediction model as they follow universal rules with mathematical guarantees,” said Schönlieb.

Using deep learning approaches along with mathematical and statistical analysis methods, the new tool will be accompanied by a comprehensive algorithmic strategy that will allow fine-tuning for datasets with different characteristics and implementation in different countries. ̽��ֱ��team are hoping to launch the AIX-COV-NET tool within the next 12 to 18 months. ̽��ֱ��project has recently received funding from the EU-funded Innovative Medicines Initiative and Intel.

“Our team’s strength is the close dialogue we have between clinicians and data scientists, and the passion we all bring for advancing AI solutions for COVID-19,” said Schönlieb, from Cambridge's Department of Applied Mathematics and Theoretical Physics.

“AI offers huge potential to support agile clinical decision making, ensuring patients receive the most appropriate support and leading to better patient outcomes,” said Sala.

̽��ֱ��core project team is comprised of data scientists and clinicians from across Cambridge and is led by Professor Carola-Bibiane Schönlieb, Director of the Centre for Mathematical Imaging in Healthcare, and Professor Evis Sala, Professor of Oncological Imaging, ̽��ֱ�� of Cambridge & Honorary Consultant Radiologist, Addenbrooke’s Hospital, Cambridge ̽��ֱ�� Hospitals NHS Foundation Trust. ̽��ֱ��team is supported by an AI and image analysis team, drawn from subject experts across Cambridge and around the world, a clinical team comprised of colleagues from hospitals in Cambridge, London and Vienna, and a support team based in the Faculty of Mathematics. Partner institutions include hospitals in Wuhan, China; Milan, Italy; and Madrid, Spain, and universities in Manchester, Vienna and London.

̽��ֱ�� ̽��ֱ�� of Cambridge has an impressive record of achievement in multidisciplinary research and innovation. ̽��ֱ��CMIH is a collaboration between mathematics, engineering, physics and biomedical scientists and clinicians and is one of five centres to receive investment from the Engineering and Physical Sciences Research Council (EPSRC). A key aim of this partnership is the delivery of high quality, multidisciplinary research that will help overcome some of the major challenges facing the NHS.

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AI assisted COVID-19 diagnostic and prognostic tool could improve resource allocation and patient outcomes.

AI offers huge potential to support agile clinical decision making, ensuring patients receive the most appropriate support and leading to better patient outcomes

Evis Sala

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