In the first 2.5 years, our work has been divided along the three main work packages. In specific, we are actively building baseline imaging biomarkers for immunotherapy response (WP1), developing serial immunotherapy response biomarkers using pre- and post-treatment imaging (WP2), and testing if imaging biomarkers may characterise underlying biological factors, and investigate if these can improve response predictions (WP3). This research is conducted at two institutions: Maastricht University (host) in the Netherlands and Harvard University in Boston. Personnel was successfully onboarded in Maastricht University and integrated within both institutions and all team members actively participate in bi-weekly Zoom meetings to ensure direct and frequent communication.
Data collection, curation, and annotation of large datasets of cancer patients treated with immunotherapies, in combination with surgery, radiation, and/or targeted therapies, in standard-of-care and clinical trials, is currently ongoing. Clinical experts are identifying the right datasets, curating the right clinical data using 3D Slicer, localising the right imaging scans, as well as curating the imaging data to assure sufficient imaging quality. After these steps, clinical experts perform the annotation of the imaging data by identifying and annotating all visible cancer lesions. Target lesions are defined as any visible tumour lesions (primary or metastases) on baseline and serial imaging. In a second step, we define the response kinetics of these lesions (Task 3 of WP1 and 2). For example, lesions that disappear on the follow-up scan are flagged as complete response. Furthermore, sub-cohorts will have detailed mutational profiling of primaries and/or metastases have been also identified, and a large subset have been approved and annotated already. Lastly, we also identified and got access to clinical trial data that are collected in a prospective and standardised manor.
For work package 1 and 2, we need to convert CT images into suitable data structures for the development of AI applications. For this we are developing standardised preprocessing pipelines, able to load CT images, identify lesions, and normalise the input CT cube around the tumour lesion. We are utilising novel self-supervised learning (SSL) approaches that became the state of the art in deep learning recently, to address these issues. SSL learns from unlabeled data and is based on an artificial neural network. We are adopting a very similar strategy to develop a network able to quantify the spectrum of tumour lesions on a CT. For this we are using a dataset of 32,735 tumour lesions in the bone, abdomen, mediastinum, liver, lung, kidney, and pelvis of 4,427 unique patients. Using this dataset, we also use data augmentation to yield a less sensitive network to interpolation, resizing, and imaging artifacts. Initial experiments demonstrated strong potential with high performance in predicting treatment response of lung cancer lesions, and detailed experiments are ongoing to draw firm conclusions. In work package 3, we are investigating the potential of using imaging biomarkers as a proxy for measuring biological characteristics, as defined through the genomic analysis, among others.
