Stay Informed: Transforming Radiology with Structured Reporting and Data-Driven Approaches

Dive into our activities, projects, and product updates. Catch on the latest industry news and learn who we are as a company and as a team.

Novel oncologic therapy response criteria (iRECIST and more)

Advances in oncology therapies, such as novel immune-related agents, require adapted and refined criteria and guidelines for the assessment and prediction of treatment response.

At RSNA 2016, Mint Medical showcases a comprehensive set of new criteria including mRECIST mesothelioma, the Lugano (Cheson 2014 lymphoma) Classification, the Prostate Cancer Working Group (PCWG2) criteria, and iRECIST.

As an example, immune-related therapies can show tumor growth from treatment effect rather than true disease progression (“pseudoprogression”). mint Lesion™ 3.3 now supports the iRECIST criteria as currently drafted by the RECIST working group. iRECIST is the first immune-related criteria distinguishing between an unconfirmed and a confirmed Progressive Disease (PD).

Therapy Response Assessment iRECIST

Related Resources

Related Resources

Virtual examination of human lungs on a modern interface screen

Early Detection of Treatment Response in Lung Cancer Using Delta-Radiomics Features

The study conducted by Heidelberg University Hospital investigates the use of diffusion-weighted MRI (DWI) to predict early treatment outcomes in…

The image serves as a graphical abstract, displaying visuals from the study, ranging from MRI scans to the analysis of delta-radiomics texture features

Heidelberg University Hospital: Delta-Radiomics Features from ADC Maps as Early Predictors of Treatment Success in Lung Cancer Therapy

In this prospective study conducted by Heidelberg University Hospital, researchers investigated whether changes in radiomic features from…

Doctors looking at MRI scans to evaluate a glioblastoma.

Optimizing Glioblastoma Imaging: Enhancing MRI Efficiency and Quality with Deep Learning

This study investigates the use of deep learning (DL) to optimize MRI protocols for glioblastoma patients, aiming to reduce scan time and improve…