학술논문

A semiautomatic CT-based ensemble segmentation of lung tumors: Comparison with oncologists’ delineations and with the surgical specimen
Document Type
Article
Source
Radiotherapy & Oncology. Nov2012, Vol. 105 Issue 2, p167-173. 7p.
Subject
*CANCER tomography
*LUNG cancer diagnosis
*IMAGE segmentation
*CANCER radiotherapy
*ONCOLOGISTS
*POSITRON emission tomography
*MEDICAL practice
*COMPARATIVE studies
Language
ISSN
0167-8140
Abstract
Abstract: Purpose: To 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 methods: For 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. Results: High 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±83.2cm3, mean±SD) and manual delineations (81.9±94.1cm3; 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). Conclusions: Semiautomatic 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. [Copyright &y& Elsevier]