ArticleEJNMMI research2015
Optimal definition of biological tumor volume using positron emission tomography in an animal model.
Article in EJNMMI research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Machine Learning of Multi-Modal Tumor Imaging Reveals Trajectories of Response to Precision Treatment.Cancers · 2023Article
- Preclinical PERCIST and 25% of SUVJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2020Article
- Concurrent imaging of vascularization and metabolism in a mouse model of paraganglioma under anti-angiogenic treatment.Theranostics · 2020Article
- Monitoring of Tumor Growth with [(18)F]-FET PET in a Mouse Model of Glioblastoma: SUV Measurements and Volumetric Approaches.Frontiers in neuroscience · 2016Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThe goal of the study is to investigate (18)F-fluorodeoxyglucose positron emission tomography ((18)F-FDG-PET)'s ability to delineate the viable portion of a tumor in an animal model using cross-sectional histology as the validation standard.
methodsSyngeneic mammary tumors were grown in female Lewis rats. Macroscopic histological images of the transverse tumor sections were paired with their corresponding FDG micro-PET slices of the same cranial-caudal location to form 51 pairs of co-registered images. A binary classification system based on four FDG-PET tumor contouring methods was applied to each pair of images: threshold based on (1) percentage of maximum tumor voxel counts (Cmax), (2) percentage of tumor peak voxel counts (Cpeak), (3) multiples of liver mean voxel counts (Cliver) derived from PERCIST, and (4) an edge-detection-based automated contouring system. The sensitivity, which represented the percentage of viable tumor areas correctly delineated by the gross tumor area (GTA) generated from a particular tumor contouring method, and the ratio (expressed in percentage) of the overestimated areas of a gross tumor area (GTAOE)/whole tumor areas on the macroscopic histology (WTAH), which represented how much a particular GTA extended into the normal structures surrounding the primary tumor target, were calculated.
resultsThe receiver operating characteristic curves of all pairs of FDG-PET images have a mean area under the curve value of 0.934 (CI of 0.911-0.954), for representing how well each contouring method accurately delineated the viable tumor area. FDG-PET single value threshold tumor contouring based on 30 and 35 % of tumor Cmax or Cpeak and 6 × Cliver + 2 × SD achieved a sensitivity greater than 90 % with a GTAOE/WTAH ratio less than 10 %. Contouring based on 50 % of Cmax or Cpeak had a much lower sensitivity of 67.2-75.6 % with a GTAOE/WTAH ratio of 1.1-1.7 %. Automated edge detection was not reliable in this system.
conclusionsSingle-value-threshold tumor contouring using (18)F-FDG-PET is able to accurately delineate the viable portion of a tumor. 30 and 35 % of Cmax, 30 and 35 % of Cpeak, and 6 × Cliver + 2 × SD are three appropriate threshold values to delineate viable tumor volume in our animal model. The commonly used threshold value of 50 % of Cmax or Cpeak failed to detect one third of the viable tumor volume in our model.
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Registered trials
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