Register Now: Image-Based Risk: The Next Frontier in Breast Cancer Screening
|Articles|May 1, 2006

Computers tackle melanoma tumor volume measurement

As the incidence of malignant melanoma increases, researchers are seeking ways to accurately and reproducibly measure tumor volume and therapy response. Computer-aided volumetry may be an answer.

As the incidence of malignant melanoma increases, researchers are seeking ways to accurately and reproducibly measure tumor volume and therapy response. Computer-aided volumetry may be an answer.

Dr. Michael Fabel-Schulte of the department of radiology at the German Cancer Research Center in Heidelberg and colleagues tested 3D semiautomated segmentation and volumetry of lymph nodes in 25 patients with malignant metastatic melanoma. CT scanning covered the neck, chest, abdomen, and pelvis.

Fabel-Schulte presented the results at the 2006 European Congress of Radiology.

Two independent readers evaluated 120 suspicious lymph nodes by using volume, time, segmentation quality, and number of corrections. Additionally, 20 lymph nodes were segmented manually.

Segmentation quality was rated acceptable to excellent in 81% by reader one and 79% by reader two. Correlation of the volume was highly significant between both readers.

Readers manually corrected 15% of the lymph nodes. The average time for automated segmentation per lymph nodes was 70 to 100 seconds, compared with 180 to 200 seconds per lymph nodes for the primary manual segmentation. Encouraged by the results, researchers suggest further study in a larger patient population.

Related to this article

Emerging Research Insights and New Advances in Neuroradiology
Catch up on key neuroradiology news and research from the past month, including recently published studies involving white matter hyperintensity findings on brain MRI, MRI-enhanced amyloid PET and the newly FDA-approved PET agent floretyrosine F18 for glioma imaging.
Current Perspectives on Integrating AI into Radiology
In a recent interview with Diagnostic Imaging, Joseph Cavallo, MD, MBA, offered insights on post-deployment monitoring with AI, the potential for improved workflow efficiency and the ongoing challenge of balancing essential clinical skills and AI literacy in the training of radiology residents and fellows.