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.

Doctor looking at a CT scan in mint Lesion™

Software-Assisted CT Assessment Outperforms Manual Methods in Oncology Study

A recent study conducted at UKE Hamburg compared manual and software-assisted assessments of computed tomography (CT) scans according to iRECIST (immune Response Evaluation Criteria in Solid Tumors) in oncological patients undergoing immune-based treatment. Utilizing mint Lesion™ for software-assisted assessment, the study found that software-assisted assessments resulted in shorter reading times, lower error rates, and higher inter-reader agreement compared to manual assessments. The researchers thus concluded that software-assisted iRECIST assessments are preferable over manual approaches for optimal oncological response evaluation.

Read more about the study here.

Immunotherapy Structured Reporting Clinical Trials Clinical Routine Clinical Research Standardized Reading Procedures Therapy Response Assessment iRECIST

Related Resources

Related Resources

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…

Puzzle pieces connecting to an interoperable system

Healthcare on FHIR: Igniting the Potential of Interoperability

Interoperability plays a crucial role in healthcare: it enables seamless communication of patient information across different systems, leads to…

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…