Assessment of liver metastases radiomic feature reproducibility with deep-learning-based semi-automatic segmentation software. (March 2021)

• Record Type: Journal Article
• Authors: Wang, Lan; Tan, Jingwen; Ge, Yingqian; Tao, Xinwei; Cui, Zheng; Fei, Zhenyu; Lu, Jing; Zhang, Huan; Pan, Zilai
• Is Part Of: Acta radiologica. Volume 62:Number 3(2021)
• Page Start: 291
• Page End: 301
• Publication Date: 2021-03
• Subjects: Liver metastasis -- radiomics -- deep learning -- reproducibility of results; Radiology, Medical -- Periodicals; Radiography, Medical -- Periodicals; Radiotherapy -- Periodicals; 616.0757
• DOI: 10.1177/0284185120922822
• Languages: English
• ISSNs: 0284-1851

Abstract

Background: Good feature reproducibility enhances model reliability. The manual segmentation of gastric cancer with liver metastasis (GCLM) can be time-consuming and unstable. Purpose: To assess the value of a semi-automatic segmentation tool in improving the reproducibility of the radiomic features of GCLM. Material and Methods: Patients who underwent dual-source computed tomography were retrospectively reviewed. As an intra-observer analysis, one radiologist segmented metastatic liver lesions manually and semi-automatically twice. Another radiologist re-segmented the lesions once as an inter-observer analysis. A total of 1691 features were extracted. Spearman rank correlation was used for feature reproducibility analysis. The times for manual and semi-automatic segmentation were recorded and analyzed. Results: Seventy-two patients with 168 lesions were included. Most of the GCLM radiomic features became more reliable with the tool than the manual method. For the intra-observer feature reproducibility analysis of manual and semi-automatic segmentation, the rates of features with good reliability were 45.5% and 62.3% ( P < 0.02), respectively; for the inter-observer analysis, the rates were 29.3% and 46.0% ( P < 0.05), respectively. For feature types, the semi-automatic method increased reliability in 6/7 types in the intra-observer analysis and 5/7 types in the inter-observer analysis. For image types, the reliability of the square and exponential types was significantly increased. The mean time of semi-automatic segmentation was significantly shorter than that of the manual method ( P < 0.05). Conclusion: The application of semi-automated software increased feature reliability in the intra- and inter-observer analyses. The semi-automatic process took less time than the manual process.