| EPSRC Reference: |
EP/N034708/1 |
| Title: |
Computer models for CRLM progression assessment based on histopathological image scans |
| Principal Investigator: |
Zhang, Dr Q |
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| Department: |
Sch of Electronic Eng & Computer Science |
| Organisation: |
Queen Mary University of London |
| Scheme: |
First Grant - Revised 2009 |
| Starts: |
01 August 2017 |
Ends: |
31 January 2019 |
Value (£): |
100,002
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| EPSRC Research Topic Classifications: |
| Med.Instrument.Device& Equip. |
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Summary on Grant Application Form |
Colorectal cancer is one of the most common malignancies worldwide, with over 1 million new cases reported each year. Around 41,600 people were diagnosed with colorectal cancer in 2011 in the UK. It is also the second most common cause of cancer death in the UK, due to the formation of distant metastases. The liver is a primary target organ of metastatic lesions. More than half patients with colorectal cancer eventually develop a Colorectal Liver Metastases (CRLM); the liver metastasis can be present at the time of diagnosis of colorectal cancer or develop after the primary tumour was operated. Surgical resection is the only treatment associated with long-term survival for patients with CRLM. Recent studies have reported 5-year survival rates of 30%-60% for patients with resected CRLMs. Preoperative chemotherapy leads to a decrease in the size of CRLM, enabling resection in up to 16% of the patients with initially unresectable lesions. Among patients with resectable CRLM, the use of preoperative chemotherapy may diminish the magnitude of resection required. Therefore, an effective therapeutic treatment is crucial to control the liver metastasis, dramatically slow down disease progression and improve healing prospects.
In cancer diagnosis and treatment procedures, qualitative histopathological evaluation on microscopic samples from tumour tissue is regarded as the gold standard for confirmative diagnosis of almost all types of cancer. For CRLM, the assessment of pathological tumour regression after preoperative chemotherapy is mostly based on estimating the proportion of tumour cells in relation to the total tumour area (including the latter tumour necrosis, fibrosis and other regressive changes) as well as biologically relevant histology features, in particular the tumour invasion front. These properties determine pathological tumour regression or grade of response to chemotherapy and provide valuable prognostic information on the risk for cancer progression. Currently, this histopathological evaluation is performed by expert pathologists through visual assessment of the tumour slides. This is often time-consuming and expensive due to the large amount of slides to be reviewed and the limited availability of subspecialised liver pathologists. Moreover, visual evaluations are inherently subject to inter- and intra-observer variability, and may be unacceptably inconsistent and imprecise, with negative impact in the actual diagnosis and future treatment planning.
This project aims at developing an intelligent computer system that enables automatic, precise, objective and reproducible assessment of CRLM tumour regression and precise characterisation of the tumour invasion front based on the digital scans of resected CRLM tumour tissue slides, by integrating beyond the state-of-the-art, specifically designed computer vision, image processing and machine learning schemes. The outcome is useful in both clinical and research domains, by providing additional reference in the association between tumour regression and chemotherapy treatment for prognostic purposes in clinical practice, and enabling better understanding on the mechanism of tumour progression in the liver - and hence gain valuable knowledge that can be used to counteract it. In both domains, such solutions are expected to help reduce the costs and manpower consumption in the practises. The proposed methodologies and system can be generalised or extended for similar purposes in the diagnosis or treatment procedures of other types of cancer.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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