Lung cancer is the leading cause of cancer death in the Netherlands. For years chemotherapy was the only (palliative) treatment, with a short survival of only months. Since the introduction of... Show moreLung cancer is the leading cause of cancer death in the Netherlands. For years chemotherapy was the only (palliative) treatment, with a short survival of only months. Since the introduction of immunotherapy in 2015, this survival has increased significantly, with the first results showing a survival of even a few years. However, the response rate is relatively low, the treatment is expensive and the (low percentage of) side effects are severe. Therefore a biomarker is needed to predict which patients would benefit of immunotherapy.This thesis is about the search for a new biomarker. With the use of the RNA of platelets, proteins, tumor markers in blood and a an electronic nose for exhaled breath, we tried to find a non-invasive biomarker for the prediction of response on immunotherapy and for the (future) use in clinical practice, some of which are promising. Show less
This thesis consists of two sections. In Section I, (pre)clinical research investigating novel targets for pre- and intraoperative molecular imaging of pancreatic cancer are discussed. In Section... Show moreThis thesis consists of two sections. In Section I, (pre)clinical research investigating novel targets for pre- and intraoperative molecular imaging of pancreatic cancer are discussed. In Section II, various studies are described which lay the groundwork for further investigation into response monitoring and prediction in rectal cancer using various imaging modalities. Show less
Introduction: Predicting checkpoint inhibitors treatment outcomes in melanoma is a relevant task, due to the unpredictable and potentially fatal toxicity and high costs for society. However,... Show moreIntroduction: Predicting checkpoint inhibitors treatment outcomes in melanoma is a relevant task, due to the unpredictable and potentially fatal toxicity and high costs for society. However, accurate biomarkers for treatment outcomes are lacking. Radiomics are a technique to quantitatively capture tumour characteristics on readily available computed tomography (CT) imaging. The purpose of this study was to investigate the added value of radiomics for predicting clinical benefit from checkpoint inhibitors in melanoma in a large, multicenter cohort.Methods: Patients who received first-line anti-PD1 +/- anti-CTLA4 treatment for advanced cutaneous melanoma were retrospectively identified from nine participating hospitals. For every patient, up to five representative lesions were segmented on baseline CT, and radiomics features were extracted. A machine learning pipeline was trained on the radiomics features to predict clinical benefit, defined as stable disease for more than 6 months or response per RECIST 1.1 criteria. This approach was evaluated using a leave-one-centre-out cross vali-dation and compared to a model based on previously discovered clinical predictors. Lastly, a combination model was built on the radiomics and clinical model.Results: A total of 620 patients were included, of which 59.2% experienced clinical benefit. The radiomics model achieved an area under the receiver operator characteristic curve (AUROC) of 0.607 [95% CI, 0.562-0.652], lower than that of the clinical model (AUROC=0.646 [95% CI, 0.600-0.692]). The combination model yielded no improvement over the clinical model in terms of discrimination (AUROC=0.636 [95% CI, 0.592-0.680]) or calibration. The output of the radiomics model was significantly correlated with three out of five input variables of the clinical model (p < 0.001). Discussion: The radiomics model achieved a moderate predictive value of clinical benefit, which was statistically significant. However, a radiomics approach was unable to add value to a simpler clinical model, most likely due to the overlap in predictive information learned by both models. Future research should focus on the application of deep learning, spectral CT -derived radiomics, and a multimodal approach for accurately predicting benefit to checkpoint inhibitor treatment in advanced melanoma.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Show less
