Portal de Periódicos da CAPES

A semiautomatic CT-based ensemble segmentation of lung tumors: Comparison with oncologists’ delineations and with the surgical specimen

2012; Elsevier BV; Volume: 105; Issue: 2 Linguagem: Inglês

10.1016/j.radonc.2012.09.023

1879-0887

Emmanuel Rios Velazquez, Hugo J.W.L. Aerts, Irene Yu‐Hua Gu, Dmitry B. Goldgof, Dirk De Ruysscher, André Dekker, R Korn, Robert J. Gillies, Philippe Lambin,

Radiomics and Machine Learning in Medical Imaging

PurposeTo assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC).Materials and methodsFor 20 NSCLC patients (stages Ib–IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data are available on www.cancerdata.org.ResultsHigh overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5 ± 9.0, mean ± SD) and union (94.2 ± 6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4 ± 83.2 cm3, mean ± SD) and manual delineations (81.9 ± 94.1 cm3; p = 0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r = 0.96).ConclusionsSemiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered as the "gold standard". This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors.