ISSN 1305-3825 | E-ISSN 1305-3612
Abdominal Imaging - Original Article
Semi-automatic 3D-volumetry of liver metastases from neuroendocrine tumors to improve combination therapy with 177Lu-DOTATOC and 90Y-DOTATOC
1 Department of Nuclear Medicine, University Hospital Heidelberg, Heidelberg, Germany  
2 Institute for Medical Imaging Computing, Fraunhofer MEVIS, Bremen  
3 Department of Radiology, University Hospital Heidelberg, Heidelberg, Germany  
4 Department of Biostatistics, German Cancer Research Center, Germany  
5 Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA  
6 Department of Nuclear Medicine, University Hospital Heidelberg, Heidelberg, Germany; Clinical Cooperation Unit Nuclear Medicine, DKFZ Heidelberg, Germany  
Diagn Interv Radiol 2016; 22: 201-206
DOI: 10.5152/dir.2015.15304
Abstract

PURPOSE
Patients with neuroendocrine tumors (NET) often present with disseminated liver metastases and can be treated with a number of different nuclides or nuclide combinations in peptide receptor radionuclide therapy (PRRT) depending on tumor load and lesion diameter. For quantification of disseminated liver lesions, semi-automatic lesion detection is helpful to determine tumor burden and tumor diameter in a time efficient manner. Here, we aimed to evaluate semi-automated measurement of total metastatic burden for therapy stratification.

 

METHODS
Nineteen patients with liver metastasized NET underwent contrast-enhanced 1.5 T MRI using gadolinium-ethoxybenzyl diethylenetriaminepentaacetic acid. Liver metastases (n=1537) were segmented using Fraunhofer MEVIS Software for three-dimensional (3D) segmentation. All lesions were stratified according to longest 3D diameter >20 mm or ≤20 mm and relative contribution to tumor load was used for therapy stratification.

 

RESULTS
Mean count of lesions ≤20 mm was 67.5 and mean count of lesions >20 mm was 13.4. However, mean contribution to total tumor volume of lesions ≤20 mm was 24%, while contribution of lesions >20 mm was 76%.

 

CONCLUSION
Semi-automatic lesion analysis provides useful information about lesion distribution in predominantly liver metastasized NET patients prior to PRRT. As conventional manual lesion measurements are laborious, our study shows this new approach is more efficient and less operator-dependent and may prove to be useful in the decision making process selecting the best combination PRRT in each patient.

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