Abstract
The volume estimation of retroperitoneal sarcoma (RPS) is often difficult due to its huge dimensions and irregular shape; thus, it often requires manual segmentation, which is time-consuming and operator-dependent. This study aimed to evaluate two fully automated deep learning networks (ENet and ERFNet) for RPS segmentation. This retrospective study included 20 patients with RPS who received an abdominal computed tomography (CT) examination. Forty-nine CT examinations, with a total of 72 lesions, were included. Manual segmentation was performed by two radiologists in consensus, and automatic segmentation was performed using ENet and ERFNet. Significant differences between manual and automatic segmentation were tested using the analysis of variance (ANOVA). A set of performance indicators for the shape comparison (namely sensitivity), positive predictive value (PPV), dice similarity coefficient (DSC), volume overlap error (VOE), and volumetric differences (VD) were calculated. There were no significant differences found between the RPS volumes obtained using manual segmentation and ENet (p-value = 0.935), manual segmentation and ERFNet (p-value = 0.544), or ENet and ERFNet (p-value = 0.119). The sensitivity, PPV, DSC, VOE, and VD for ENet and ERFNet were 91.54% and 72.21%, 89.85% and 87.00%, 90.52% and 74.85%, 16.87% and 36.85%, and 2.11% and −14.80%, respectively. By using a dedicated GPU, ENet took around 15 s for segmentation versus 13 s for ERFNet. In the case of CPU, ENet took around 2 min versus 1 min for ERFNet. The manual approach required approximately one hour per segmentation. In conclusion, fully automatic deep learning networks are reliable methods for RPS volume assessment. ENet performs better than ERFNet for automatic segmentation, though it requires more time.
Highlights
Soft tissue sarcomas are rare, malignant mesenchymal neoplasms that account for less than 1% of all malignant tumors
This study shows that automatic segmentations with deep learning networks and using portal-venous computed tomography (CT) images are reliable methods for the automatic tumor volumetric measurements of retroperitoneal sarcoma (RPS)
This study describes two deep learning automatic segmentation methods for RPS volume assessment without the need for user interaction
Summary
Soft tissue sarcomas are rare, malignant mesenchymal neoplasms that account for less than 1% of all malignant tumors. The prognosis for patients with retroperitoneal sarcoma (RPS) is relatively poor, with a 36% to 58% overall 5-year survival rate and a natural history characterized by late recurrence [3]. RPS are frequently underdiagnosed at the early stage, and symptoms appear late, as they are associated with the displacement of adjacent organs and obstructive phenomena. Symptoms include abdominal pain, back pain, bowel obstruction, or palpable abdominal mass [1]. CT examination allows for tissue components characterization and offers multiplanar reconstructions to depict the anatomic site of the origin of a mass, as well as its relationship to adjacent organs and vasculature [5]
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