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.

Someone reading a scientific publication on CT Radiomics, sarcopenia, gastric or esophageal cancer

CT-Radiomics unveils insights into sarcopenia's impact on esophageal and gastric cancer prognosis

Analyzing 83 patients with contrast-enhanced CT scans, University Hospital Ulm researchers tracked the prevalence of sarcopenia at different time points. They used mint Lesion™ for muscle segmentation and extraction of 85 radiomic features. These features, categorized into shape, first-order, and higher-order types, provided a detailed assessment of skeletal muscles. Machine learning models, including Random Forest, accurately predicted sarcopenia at the initial diagnosis.

While sarcopenia's link to disease progression lacked statistical significance, the study highlights CT radiomics and machine learning's potential in oncological imaging for refined diagnostics and prognostics.

Related Resources

Related Resources

Medical personnel looking at a technical device to discuss diagnostic guidelines

2,237 Patients, 11 Hospitals, four HCC Criteria: A Comparison Study

A recent study, conducted across 11 South Korean hospitals, compared the diagnostic performance of four hepatocellular carcinoma (HCC) diagnostic…

mint Lesion screenshot with HCC diagnosis according to APASL, AASLD, LI-RADS, KLCA-NCC, and EASL guidelines

Comparison of Diagnostic Guidelines for Hepatocellular Carcinoma

Recent advancements in MRI techniques and tumor biology have led to updated hepatocellular carcinoma (HCC) diagnostic guidelines from various liver…

Radiologist using for medical image analysis

Advancing Real-World Federated Learning in Radiology

Federated Learning (FL) enables collaborative model training without data centralization – a crucial aspect for radiological image analysis where…