Radiomic features from CT to predict lung cancer

CSC Lead: Anil

The survival rates from non-small-cell lung cancer is associated with TNM grading system (tumour, nodal involvement and metastatic extent), and also protein expression, however the only way to evaluate both is using histology results from an invasive biopsy investigation. Biopsies are also limited in that they may not fully character the tumour and spread of disease. The aim of this project is to use AI to extract RFs from the lymph nodes and lung cancer and correlate them with histology, to assess the RF ability to predict histology and patient outcomes. This will result in a non-invasive tool to build multivariate predictive models and stratify patient treatments.

Clinical lead(s): TBC

Project Plan
1. Meeting of all persons involved to determine AI specifications.

2. Setting technical and system requirements for AI model.

3. Dataset curation (retrospective).

4.Model training

5. Model testing

6. Implementation

7. Audit

References
Wang et al 2020
Ninatti et al 2020
Bashir et al 2019
Xu et al 2019
Lambin et al 2012
Wilson et al 2017
Xia et al 2019
Weikert et al 2019
Tsitsias et al 2020