What are the true volumes of SEGA tumors? Reliability of planimetric and popular semi-automated image segmentation methods.
OBJECTIVE
To evaluate the reliability of the standard planimetric methodology of volumetric analysis and three different open-source semi-automated approaches of brain tumor segmentation.


METHODS
The volumes of subependymal giant cell astrocytomas (SEGA) examined by 30 MRI studies of 10 patients from a previous everolimus-related trial (EMINENTS study) were estimated using four methods: planimetric method (modified MacDonald ellipsoid method), ITK-Snap (pixel clustering, geodesic active contours, region competition methods), 3D Slicer (level-set thresholding), and NIRFast (k-means clustering, Markov random fields). The methods were compared, and a trial simulation was performed to determine how the choice of approach could influence the final decision about progression or response.


RESULTS
Intraclass correlation coefficient was high (0.95; 95% CI 0.91-0.98). The planimetric method always overestimated the size of the tumor, while virtually no mean difference was found between ITK-Snap and 3D Slicer (P = 0.99). NIRFast underestimated the volume and presented a proportional bias. During the trial simulation, a moderate level of agreement between all the methods (kappa 0.57-0.71, P < 0.002) was noted.


CONCLUSIONS
Semi-automated segmentation can ease oncological follow-up but the moderate level of agreement between segmentation methods suggests that the reference standard volumetric method for SEGA tumors should be revised and chosen carefully, as the selection of volumetry tool may influence the conclusion about tumor progression or response.
Department of Biostatistics and Translational Medicine, Hematology and Diabetology, Medical University of Lodz, ul. Sporna 36/50, 91-001, Lodz, Poland.|Department of Pediatrics, Oncology, Hematology and Diabetology, University Hospital No 4, Lodz, Poland.
Konrad
K
Stawiski
Department of Pediatrics, Oncology, Hematology and Diabetology, University Hospital No 4, Lodz, Poland.
Joanna
J
Trelińska
Department of Pediatric Radiology, University Hospital No 4, Lodz, Poland.
Dobromiła
D
Baranska
Department of Pediatrics, Oncology, Hematology and Diabetology, University Hospital No 4, Lodz, Poland.
Iwona
I
Dachowska
Department of Neurology and Epileptology and Pediatric Rehabilitation, The Children's Memorial Health Institute, Warsaw, Poland.
Katarzyna
K
Kotulska
Department of Pediatric Neurology, Medical University of Warsaw, Warsaw, Poland.
Sergiusz
S
Jóźwiak
Department of Biostatistics and Translational Medicine, Hematology and Diabetology, Medical University of Lodz, ul. Sporna 36/50, 91-001, Lodz, Poland. wojciech.fendler@umed.lodz.pl.|Department of Pediatrics, Oncology, Hematology and Diabetology, University Hospital No 4, Lodz, Poland. wojciech.fendler@umed.lodz.pl.
Wojciech
W
Fendler
Department of Pediatrics, Oncology, Hematology and Diabetology, University Hospital No 4, Lodz, Poland.
Wojciech
W
Młynarski
D001254:Astrocytoma / Q000000981:diagnostic imaging*; D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods / Q000706:statistics & numerical data; D008279:Magnetic Resonance Imaging / Q000379:methods / Q000706:statistics & numerical data; D059906:Neuroimaging / Q000379:methods* / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D047368:Tumor Burden
Brain tumor segmentation; SEGA