Active clinical trials for "Carcinoma, Renal Cell"

Merus is providing single patient/named access to the HER2/HER3 bispecific antibody, MCLA-128, to patients with advanced NRG1-fusion positive solid tumor under this early access program who are ineligible for an ongoing MCLA-128 clinical trial or have other considerations that prevent access to MCLA-128 through an existing clinical trial. Participating sites will be added as they apply for and are approved for the EAP. A medical doctor must decide whether the potential benefit outweighs the risk of receiving an investigational therapy based on the individual's medical history and program eligibility criteria.

This expanded access study is designed to provide RAD001 to patients with MRCC who are without satisfactory treatment alternatives, until RAD001 becomes commercially available.

The primary objective of this protocol is to provide access to SU011248 treatment for patients with metastatic RCC who are ineligible for participation in ongoing SU011248 clinical studies and have the potential to derive clinical benefit from treatment with SU011248 based on the judgment of the investigator.

This is an Expanded Access Program (EAP) available to patients who have advanced cancers, who have failed or progressed on standard of care systemic therapy and do not qualify for ongoing clinical trials.

The purpose of this Expanded Access Program (EAP) is to enable the use of 89Zr-DFO-girentuximab with positron emission tomography/computed tomography (PET/CT) imaging to non-invasively detect carbonic anhydrase 9 (CAIX)-expressing clear cell renal cell carcinoma (ccRCC) in patients with renal masses as determined by conventional imaging.

We aim to experiment and implement various deep learning architectures in order to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, we are interested in detecting renal tumors from CT urography scans in this project. We would like to classify renal tumor to cancer, non cancer, renal cyst I, renal cyst II, renal cyst III and renal cyst VI, with high sensitivity and low false positive rate using various types of convolutional neural networks (CNN). This task can be considered as the first step in building CAD systems for renal cancer diagnosis. Moreover, by automating this task, we can significantly reduce the time for the radiologists to create large-scale labeled datasets of CT-urography scans.

This study was designed as a pilot study to try and find a Chemokine that may be specific for renal cell carcinoma

Collect blood samples and associated clinical data prior to, during, and post radiation treatment.

Recruitment of participants to the study takes place at the urological department at Zealand University Hospital, Roskilde, Feb. 2018-April 2018. 10 people with diagnosis of renal cancer should be recruited in phases 1. Subsequently, biological materials from 100 patients with kidney cancer will be used from the national Biobank. In phase 3, 10 patients will be recruited for the final gene test.

Prospective translational study investigating predictors of outcome in metastatic renal cell carcinoma patients treated with Nivolumab (I-Rene trial)