With 1.6 million deaths in 2012, is lung cancer the leading cause of cancer-related death worldwide. Lung cancer can be divided into two main sub-types. Small cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC), with NSCLC making up about 85% of all cases. For a long time chemotherapy has been the only line of treatment for NSCLC patients. Immunotherapy by the drug pembrolizumab is a novel range of treatment that aims to improve the body's own immune system in order to allow it to successfully fight the cancer. Unfortunately, not all patient respond to this treatment and patient selection plays therefore an important role. PD-L1 expression is currently the only biomarker that is used to estimate the efficacy of treatment with pembrolizumab. The amount of PD-L1 expression is measured in the Tumor Proportion Score (TPS). This is a ratio between cells that show membrane staining and cells that do not show this membrane staining. Before the treatment with pembrolizumab it is required that the TPS is estimated. Estimating the TPS is a difficult and time-consuming task for pathologists. Furthermore, pathologist scoring suffers from interobserver variability. This results in a need for a reliable, robust automated method to estimate the TPS. In this work, three conceptually different neural networks are presented aiming for the automatic classification and location of PD-L1-positive and PD-L1-negative tumor cells and immune cells.
AI-assisted PD-L1 scoring in non-small-cell lung cancer
Master thesis 2020.