Radiomics Panc

• Research type

  Research Study

• Full title

  Development and validation of predictive and prognostic cancer biomarkers using Radiomics

• IRAS ID

  228163

• Contact name

  Harpreet Wasan

• Contact email

  h.wasan@imperial.ac.uk

• Sponsor organisation

  Imperial College London

• Clinicaltrials.gov Identifier

  19HH5507,

• Duration of Study in the UK

  3 years, 0 months, 1 days

• Research summary

  Clinical imaging has been recognised as one of the great advances in modern medicine. In the field of cancer medicine, imaging has enabled oncologists to diagnose and stage solid tumours by mapping the location of primary tumour and metastases. Imaging has become the key to assessing treatment response, disease recurrence and relapse. These advances have facilitated the development of processes for comprehensive quantification of tumour phenotypes by applying a large number of quantitative imaging features. Historically, the size of tumours became the main parameter for staging and response and from which response– assessment criteria (RECIST) were developed. Functional imaging modalities have developed but by and large remain research tools despite trying to incorporate them into main stream.
  Radiomics is defined as the quantitative analysis of large sets of imaging data at its most basic level and reconstructing this in algorithms that are not visual, but are asses-sable and reproducible. In this proposal, we would like to investigate the relationship between radiomics features derived from our in-house software tool TEXLab and its clinical utility in terms of relevant patient outcomes.

  We may be able to identify associations with global and prognostically relevant gene/gene product alterations, to possibly identify features that predict biological tumour behaviour and can be taken forward into the multidisciplinary team setting for treatment stratification. We intend to use routine pre-operative/pre-chemotherapy computed tomography (CT) images of at least 100 patients and their tumour samples if available for radiomics features purposes that predict prognosis. To obtain in-depth scientific understanding, the features will be compared to known useful clinical parameters and matched with archival information if available. This will provide the first comprehensive analysis of its kind in this patient population.
  Following primary analyses the study will then be expanded to further test and validate our findings with an expansion dataset of approximately 200 patients

• REC name

  West Midlands - South Birmingham Research Ethics Committee

• REC reference

  19/WM/0373

• Date of REC Opinion

  2 Jan 2020

• REC opinion

  Favourable Opinion