Abstract
ObjectiveTo evaluate the degree of similarity between manual and semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging (MRI).Materials and MethodsThis was a retrospective study of 15 MRI examinations of patients with histopathologically confirmed soft-tissue sarcomas acquired before therapeutic intervention. Manual and semiautomatic segmentations were performed by three radiologists, working independently, using the software 3D Slicer. The Dice similarity coefficient (DSC) and the Hausdorff distance were calculated in order to evaluate the similarity between manual and semiautomatic segmentation. To compare the two modalities in terms of the tumor volumes obtained, we also calculated descriptive statistics and intraclass correlation coefficients (ICCs).ResultsIn the comparison between manual and semiautomatic segmentation, the DSC values ranged from 0.871 to 0.973. The comparison of the volumes segmented by the two modalities resulted in ICCs between 0.9927 and 0.9990. The DSC values ranged from 0.849 to 0.979 for intraobserver variability and from 0.741 to 0.972 for interobserver variability. There was no significant difference between the semiautomatic and manual modalities in terms of the segmentation times (p > 0.05).ConclusionThere appears to be a high degree of similarity between manual and semiautomatic segmentation, with no significant difference between the two modalities in terms of the time required for segmentation.
Highlights
Soft-tissue sarcomas are a heterogeneous group of malignant tumors with a broad spectrum of histological presentations and prognoses[1]
There appears to be a high degree of similarity between manual and semiautomatic segmentation, with no significant difference between the two modalities in terms of the time required for segmentation
All patient information contained in the Digital Imaging and Communication in Medicine (DICOM) files related to the Magnetic resonance imaging (MRI) scans was anonymized using the K-PACS viewer (IMAGE Information Systems, Rostock, Germany)
Summary
Soft-tissue sarcomas are a heterogeneous group of malignant tumors with a broad spectrum of histological presentations and prognoses[1]. They affect connective tissues throughout the body, the most common location is in the extremities, in 59% of cases, followed by the trunk, in 19%, the retroperitoneum, in 15%, and the head/neck region, in 9%(2,3). The segmentation process, which consists of marking the region of interest from which the quantitative information will be extracted for analysis, is essential to the application of radiomics. Automatic segmentation consists in completely automated identification of the contours of the lesion by the software, without the use of data provided through a manual process. Automatic segmentation appears to be more effective, especially considering its time-saving potential, it requires complex computational resources and, in general, it is not easy to obtain satisfactory results[7]
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