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Standardized response criteria evaluation in mint Lesion featuring the TGRM module.
From Theory to Practice: Evaluating g-values within mint Lesion

Following the FDA’s recent analysis of the growth rate constant (g) as a prognostic marker1, the challenge for many research organizations is…

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Update RSNA: Heart and Lung Imaging

Seminar in Radiological Diagnostics: "RSNA Update – Heart and Lung Imaging"

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PET scans of torso with relevant false positives highlighted by red dotted circles, true positives in green
Representative prediction pitfalls in cases with high DSC
LMU University Hospital: Artificial Intelligence for TNM Staging in NSCLC – How Reliable Are AI-Based Segmentations?

The recent study “Artificial intelligence for TNM staging in NSCLC – a critical appraisal of segmentation utility in [¹⁸F]FDG PET/CT” provides a…

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Participants at the RECIST and Beyond workshop in Cologne during hands-on training with mint Lesion for structured tumor response assessment.
The two-day “RECIST and Beyond” workshop at radCIO Cologne offered intensive hands-on training on RECIST, iRECIST, mRECIST and LI-RADS, with real-case reporting performed directly in mint Lesion.
Successful “RECIST and Beyond” Workshop in Cologne: Advancing Precision in Oncologic Imaging

How can complex tumor findings be assessed accurately, reproducibly, and in line with clinical guidelines?

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RSNA 2025

Annual Meeting of the Radiological Society of North America

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Screenshot of the mint Lesion interface showing RANO 2.0 configuration, tumor burden calculations, and structured neuro-oncology assessment tools.
mint Lesion fully supports RANO 2.0 implementation with configurable parameters, automated tumor burden calculations, and structured workflows for neuro-oncology clinical trials.
Implementing RANO 2.0 for Neuro-Oncology Clinical Trials in mint Lesion

Tumor response assessment in neuro-oncology clinical trials requires careful attention to measurement protocols and confirmation scan requirements. To…

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BioPharma Clinical Trials Nexus

24th BioPharma Clinical Trials Nexus

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Interview with Prof. Timm Denecke about the RACOON-MARDER project and AI-powered early detection of liver cancer using MRI
An in-depth interview with Prof. Timm Denecke about the RACOON-MARDER project, which aims to predict hepatocellular carcinoma risk using MRI, AI, and clinical data.
“Making the Invisible Visible”: How RACOON-MARDER Aims to Improve Early Detection of Liver Cancer Using MRI and AI

Hepatocellular carcinoma (HCC) is a potentially deadly tumor. The decisive factor is the timing of diagnosis: if HCC is detected early, there is a…

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