Visual Interpretation of Radiomics Features in Filtered Computed Tomography Images during the Portal Phase of Acute Pancreatitis

作者全名：He, Wenjing; Tang, Mingyue; Jiang, Xian; Xiao, Bo; Wei, Jiayi; Zhao, Yanmei; Zhu, Yuanzhong; Chen, Xiaowen

作者地址：[He, Wenjing; Jiang, Xian; Wei, Jiayi; Zhao, Yanmei; Zhu, Yuanzhong; Chen, Xiaowen] North Sichuan Med Coll, Sch Med Imaging, Nanchong 637000, Sichuan, Peoples R China; [Tang, Mingyue] North Sichuan Med Coll, Sch Basic Med & Forens Med, Nanchong 637000, Sichuan, Peoples R China; [Xiao, Bo] Chongqing Med Univ, Bishan Hosp, Dept Radiol, Chongqing 402760, Peoples R China

通信作者：Chen, XW (通讯作者)，North Sichuan Med Coll, Sch Med Imaging, Nanchong 637000, Sichuan, Peoples R China.

来源：DISCOVERY MEDICINE

ESI学科分类：IMMUNOLOGY

WOS号：WOS:001209121600010

JCR分区：Q3

影响因子：2

年份：2024

卷号：36

期号：183

开始页：730

结束页：738

文献类型：Article

关键词：acute pancreatitis; Laplacian of Gaussian filters; logarithmic filters; gradient filters

摘要：Background: Current research on radiomics for diagnosing and prognosing acute pancreatitis predominantly revolves around model development and testing. However, there is a notable absence of ongoing interpretation and analysis regarding the physical significance of these models and features. Additionally, there is a lack of extensive exploration of visual information within the images. This limitation hinders the broad applicability of radiomics findings. This study aims to address this gap by specifically analyzing filtered Computed Tomography (CT) image features of acute pancreatitis to identify meaningful visual markers in the pancreas and peripancreatic area. Methods: Numerous filtered CT images were obtained through pyradiomics. The window width and window level were finetuned to emphasize the pancreas and peripancreatic regions. Subsequently, the LightGBM algorithm was employed to conduct an embedded feature screening, followed by statistical analysis to identify features with statistical significance (p-value < 0.01). Within the purview of the study, for each filtering method, features of high importance to the preceding prediction model were incorporated into the analysis. The image visual markers were then systematically sought in reverse, and their medical interpretation was undertaken to a certain extent. Results: In Laplacian of Gaussian filtered images within the pancreatic region, severe acute pancreatitis (SAP) exhibited fewer small areas with repetitive greyscale patterns. Conversely, in the peripancreatic region, SAP displayed greater irregularity in both area size and the distribution of greyscale levels. In logarithmic images, SAP demonstrated reduced low greyscale connectivity in the pancreatic region, while showcasing a higher average variation in greyscale between two adjacent pixels in the peripancreatic region. Moreover, in gradient images, SAP presented with decreased repetition of two adjacent pixel greyscales within the pancreatic region, juxtaposed with an increased inhomogeneity in the size of the same greyscale region within the delta range in the peripancreatic region. Conclusions: Various filtered images convey distinct physical significance and properties. The selection of the appropriate filtered image, contingent upon the characteristics of the Region of Interest (ROI), enables a more comprehensive capture of the heterogeneity of the disease.

基金机构：Project of Sichuan Natural Science Foundation [2023NS-FSC0646]; Bu-reau of Science & Technology Nanchong City [20SXQT0315]

基金资助正文：Funding This work was partly supported by the Project of Sichuan Natural Science Foundation (No. 2023NS-FSC0646) , and was also partly supported by the Bu-reau of Science & Technology Nanchong City (No. 20SXQT0315) .