e-Cancer

e-Cancer is measuring the size of lung cancer tumours and detecting changes in lung cancer size

Area of work

Lung Cancer

Imaging modality

CT

Size of data set

200

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Project lead

Professor Anand Devaraj

Industry partners

Number of NHS partners

1

Unmet need

Most early-stage lung cancers can be treated with surgery; however, many patients are diagnosed with advanced-stage cancer and their treatment options are limited to anti-cancer drugs. Although lung cancer treatment is decided through analysis of the cancer cells, response to treatment and progression of the disease is assessed by taking images of the lungs using CT scans (computerized tomography or CAT scans). Specialist doctors compare images of the tumour at diagnosis to those taken during and after treatment, measuring any change in size. This information is used to determine whether the treatment is effective, if a different treatment should be used, or if treatment should be stopped.

Currently, tumour size is assessed using CT scan images and a manual electronic measuring tool that is unable to capture the true size of the tumour reliably and accurately. Previous data has shown there are significant differences in how tumours are measured when images are interpreted by different doctors. Brainomix has developed an e-Cancer, using artificial intelligence methods, to provide automated, accurate and reliable measurement of the volume of lung cancer tumours using CT scan images. Our preliminary data show that e-Cancer is as least as good as a specialist doctor in measuring the size of lung cancer tumours.

Outcomes

The aim of this project is to develop and gain further evidence for e-Cancer, in measuring the size of lung cancer tumours and detecting changes in lung cancer size indicating response to treatment of progression of the disease. The data generated from this pilot project will provide and allow Brainomix to apply for further funding for larger-scale studies to further develop the e-Cancer as a tool that will help doctors to accurately and reliably interpret CT scans, and guide treatment decisions in lung cancer care.

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