View clinical trials related to Cancer Liver.
Filter by:The objective of this study is to determine the feasibility (main aim) and effectiveness (secondary aim) of a prehabilitation program in patients with head and neck cancer or liver cancer. Participating patients will participate in a 3-to-6 week rehabilitation program consisting of training and nutritional, smoking cessation and psychosocial counselling.
This is a multi-centre, prospective cohort study, aiming to evaluate a blood test in early detection of the four common cancers, based on the investigation of the circulating tumour DNA (ctDNA). Primary objective: To evaluate the performance characteristics of the blood ctDNA test in early detecting cancers. Secondary objectives: - To evaluate the performance characteristics of the test in determining the origin of tumour, as compared to the findings of the imaging diagnostic tests. - To determine the risk of cancers development in the high-risk population, as compared to that in the moderate-risk group.
DCE-CT of thoracic tumors as an early biomarker for treatment monitoring in comparison with morphologic criteria. 1. Rationale of the clinical investigation For the evaluation of response to anti-tumoral therapy in thoracic tumors, merely morphologic information is often not sufficient for early response evaluation as dimensions of the oncologic lesions are not changing during the first weeks of treatment. To be able to measure functional changes, dynamic contrast-enhanced CT (DCE-CT) seems promising as a biomarker for early therapy monitoring. Having an early biomarker for treatment monitoring will allow to increase patients' prognosis if a non-responder is earlier detected, will optimize the use of expensive treatments, is expected to shorten hospitalization and shorten absence at work, and to decrease side-effects of (adjuvant) medication. 2. Objective of the study 2.1.Primary objectives The primary objective is to investigate the potential of functional imaging (i.e. DCE-CT), as analyzed by the Hyperfusion analytic software, as an early biomarker for the evaluation of therapy response in primary thoracic malignancy. 2.2.Secondary objectives There are two secondary objectives: 1. To define internal system parameters and perfusion parameter thresholds that maximize the accuracy of the outcomes and to define the correct category (PD, SD, PR, CR); and 2. To compare the predicted categorization to the assessed RECIST1.1 categorization. 3. Endpoints 3.1.Primary Endpoint The primary endpoint is to directly compare the biomarker of the HF analysis software at week 3 (+- 1 week) and week 8 (+- 3 weeks) with the eventually reported Progression-Free Survival (PFS) intervals and Overall Survival (OS) in this study. PFS intervals are determined by the clinician and are based on RECIST1.1 and additional clinical and biochemical progression markers. The focus will be on evaluating the accuracy of the prediction as well as how early the prediction was correct. 3.2.Secondary Endpoints There are two secondary endpoints corresponding to the two secondary objectives. 1. The internal parameters for the HF biomarker, e.g. magnitude of the Ktrans decrease, and the change in volume of unhealthy tissue, need to be determined to define the classification (PD, SD, PR and CR) by the HF analysis software. These parameters are optimized to optimally predict the classification according to PFS and OS. This will be done by splitting the data into a train and test set to ensure generalization. 2. The classification of the HF analysis software will be compared to the purely morphological classification by RECIST1.1 to identify correlation. Furthermore, some cases will be investigated where the HF analysis performs noticeably better or worse than RECIST1.1 in predicting PFS and OS. Finally, the difference in time to the first correct prediction is compared between HF and RECIST1.1. 4.Study Design This prospective study is part of the clinical β-phase. We aim to test pre-release versions of the Hyperfusion.ai software under real-world working conditions in a hospital (clinical) setting. It is important to note, though, that the results of the software analysis will not be used by interpreting physicians to alter clinical judgement during the course of the clinical trial. A prospective study including 100 inoperable patients in UZ Gent suffering from primary thoracic malignancy (≥15mm diameter) will be conducted. For this study, in total 3 CT scan examinations of the thorax will be performed (a venous CT examination of the thorax in combination with a DCE-CT scan of the tumoral region). All patients will be recruited from the pulmonology department. Oncologic patients are clinically referred with certain intervals for a clinically indicated CT scan (being part of standard care). In the study, two clinical CT examinations that are performed standard of care (baseline CT examination and CT examination at week 8 (+- 3 weeks) after start of systemic therapy) will be executed by also adding a DCE-image of the lung adenocarcinoma to this examination. This DCE-image is performed during the waiting time before the venous/morphologic phase. Consequently, from a clinical point-of-view, the time to scan remains exactly the same. With regard to the contrast agent, an identical amount is injected as is the case in standard of care, but the contrast bolus is split in two parts - see also addendum with DCE protocol. In this study there is one additional CT-examination (DCE-scan of the thoracic malignancy in combination with venous CT scan of the thorax) at week 3 (± 1 week).
International registry for cancer patients evaluating the feasibility and clinical utility of an Artificial Intelligence-based precision oncology clinical trial matching tool, powered by a virtual tumor boards (VTB) program, and its clinical impact on pts with advanced cancer to facilitate clinical trial enrollment (CTE), as well as the financial impact, and potential outcomes of the intervention.
There is no evidence available about which molecular profiling methods are currently used for cancer patients in Austrian clinical practice. The construction of the registry proposed as a completely independent research endeavor, will be helpful for scientific evaluation and the establishment of highly credible data.
Currently, appropriate patients undergo MRI imaging with immobilization and sequences optimized for diagnostic radiology purposes. Using a mutual information algorithm, these images are then registered to a treatment planning CT obtained with custom immobilization to minimize intra-and inter-treatment motion and positional variation. This image registration process is time-consuming and introduces additional layers of geometric uncertainty into what should be a highly precise treatment planning process. However, it is necessary, since radiation dose calculations cannot be performed on MRI data due to the lack of crucial density information. The investigator envisions CT-less treatment planning, using only MRI, due to superior imaging characteristics, fully integrated into the radiation oncology clinic. This study will begin this process.