Quibim
@quibimbiomarker
Transforming imaging data into actionable predictions
ID: 2378551596
https://www.quibim.com 08-03-2014 11:03:05
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๐จ ๐ง๐ต๐ฒ ๐๐ต๐ฎ๐น๐น๐ฒ๐ป๐ด๐ฒ: Patients with B-cell #lymphoma who relapse after 2 treatments often face relapse or progression again despite CAR-T therapy. ๐ก ๐ง๐ต๐ฒ ๐ฆ๐ผ๐น๐๐๐ถ๐ผ๐ป: Using QP-Insights, we developed advanced image-based models to predict CAR-T response, overall
๐๐ ๐: 70 years old ๐๐๐ ๐ฅ๐๐ฏ๐๐ฅ: 15 ng/ml, rising kinetic ๐๐๐: Adenoma grade III mpRMI 2 years before PI-RADS 4 in left-lateralized base, biopsy (-) "๐๐ฉ๐ฆ ๐ด๐ข๐ฎ๐ฆ ๐ข๐จ๐จ๐ณ๐ฆ๐ด๐ด๐ช๐ท๐ฆ ๐ญ๐ฆ๐ด๐ช๐ฐ๐ฏ ๐ต๐ฉ๐ข๐ต ๐ธ๐ฆ ๐ด๐ข๐ธ (๐ข๐ฏ๐ฅ ๐ฎ๐ข๐ต๐ค๐ฉ๐ฆ๐ฅ ๐ธ๐ช๐ต๐ฉ
๐ We are a company dedicated to responsible innovation, aligning our initiatives with the United Nations Sustainable Development Goals (SDGs) to build a future where effective #healthcare is accessible beyond all barriers. Dive into our latest blog to discover more about how we're
In this recent paper, co-authored by Quibim and other leading experts, we explore the strategies used by five major European projects, Primage Project, CHAIMELEON, ProCAncer-I, INCISIVE project and EuCanImage, to manage data de-identification. These are some main takeaways,
The path to bringing a new drug to market is challenging: costly trials, lengthy timelines, and the risk of failure due to poor patient selection and limited biomarker #data. ๐๐ผ๐ ๐ฐ๐ฎ๐ป ๐ค๐ฃ-๐๐ป๐๐ถ๐ด๐ต๐๐ ๐ต๐ฒ๐น๐ฝ? โก Smarter Patient Selection โก Non-Invasive Monitoring