Clinical Research Directory

M7824 Related Adverse Effects in Adults With Cancer

Background: Immunotherapy drugs use a person s own immune system to help fight cancer. These drugs work better for some people than others. The drug M7824 has helped some people with cancer. But it can cause side effects. Researchers want to learn all the side effects that M7824 can cause. Once they do, they can prevent or reduce these side effects in future cancer treatments. This will lead to better overall outcomes for people with cancer. Objective: To make a thorough list of adverse events in people with cancer being treated with systemic therapies including M7824 at the National Cancer Institute (NCI). Eligibility: Participants previously enrolled in NCI protocols #15-C-0179 and #18-C-0056 Design: All needed data have already been collected. These data are stored in existing records and databases. Researchers will review the medical records of adults with cancer who were enrolled in the above protocols. The data collected will be relevant to the specific objectives being addressed. Data will be collected only if 2 conditions are met. One, the principal investigator gave permission for use of the data gathered in the trial. Two, the participants of the trial did not opt out of future use of the data. Other protocols may be added. This will be done with an amendment.

CNN-Based AI Versus Physicians for Solitary Skin Lesion Diagnosis

The goal of this observational study is to evaluate the diagnostic accuracy of a CNN-based artificial intelligence model in patients with solitary skin lesions. The main questions it aims to answer are: * What is the diagnostic performance (sensitivity and specificity) of the CNN-based model in identifying solitary skin lesions using macroscopic clinical images? * How does the diagnostic accuracy of the CNN-based model compare with the evaluations performed by dermatologists and non-dermatologist physicians? Researchers will compare the AI model's diagnostic outputs to the independent evaluations of dermatologists and non-dermatologist physicians to see if the AI model can achieve a diagnostic performance comparable to or better than human clinicians. Participants (physicians acting as clinical readers) will: * Independently review a predefined set of anonymized macroscopic clinical images sourced from a retrospective patient archive. * Provide a primary diagnosis for each lesion based solely on the images, without access to patient history or histopathological results. * Submit their assessments to be compared against the gold standard (histopathological diagnosis) and the AI model's results.