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Additional file 1: Figure S1. Chromatogram of top 10 metabolites in the targeted metabolomics analysis. Figure S2. The correlation between the predictive metabolites of CAS [PE (20:2/16:0), PG (18:0/20:4) and de novo lipogenesis (16:0/18:2n-6)] and metabolic index among nonobese MAFLD. Group 1: nonobese MAFLD patients without CAS; Group 2: nonobese MAFLD patients with CAS. Figure S3. The correlation between the predictive metabolites of CAS [PE (20:2/16:0), PG (18:0/20:4) and de novo lipogenesis (16:0/18:2n-6)] and pathological index among nonobese MAFLD. Group 1: nonobese MAFLD patients without CAS; Group 2: nonobese MAFLD patients with CAS. Figure S4. The correlation between the predictive metabolites of CAS [l-cystine, SM (16:1/18:1) and de novo lipogenesis (16:0/18:2n-6)] and metabolic index among obese MAFLD. Group 3: obese MAFLD patients without CAS; Group 4: obese MAFLD patients with CAS. Figure S5. The correlation between the predictive metabolites of CAS [l-cystine, SM (16:1/18:1) and de novo lipogenesis (16:0/18:2n-6)] and pathological index among obese MAFLD. Group 3: obese MAFLD patients without CAS; Group 4: obese MAFLD patients with CAS.

Supplemental material, sj-pdf-1-taj-10.1177_20406223211033119 for Steatosis grading consistency between controlled attenuation parameter and MRI-PDFF in monitoring metabolic associated fatty liver disease by Cong xiang Shao, Junzhao Ye, Zhi Dong, Fuxi Li, Yansong Lin, Bing Liao, Shiting Feng and Bihui Zhong in Therapeutic Advances in Chronic Disease

Supplemental material, sj-pdf-1-taj-10.1177_20406223211033119 for Steatosis grading consistency between controlled attenuation parameter and MRI-PDFF in monitoring metabolic associated fatty liver disease by Cong xiang Shao, Junzhao Ye, Zhi Dong, Fuxi Li, Yansong Lin, Bing Liao, Shiting Feng and Bihui Zhong in Therapeutic Advances in Chronic Disease

Supplemental material, sj-pdf-2-taj-10.1177_20406223211033119 for Steatosis grading consistency between controlled attenuation parameter and MRI-PDFF in monitoring metabolic associated fatty liver disease by Cong xiang Shao, Junzhao Ye, Zhi Dong, Fuxi Li, Yansong Lin, Bing Liao, Shiting Feng and Bihui Zhong in Therapeutic Advances in Chronic Disease

Hepatocellular carcinoma (HCC) is the second most frequent cause of malignancy-related death and is one of the diseases with the highest incidence in the world. Because the liver is the only organ in the human body that is supplied by two major vessels: the hepatic artery and the portal vein, various types of malignant tumors can spread from other organs to the liver. And due to the liver masses' heterogeneous and diffusive shape, the tumor lesions are very difficult to be recognized, thus automatic lesion detection is necessary for the doctors with huge workloads. To assist doctors, this work uses the existing large-scale annotation medical image data to delve deep into liver lesion detection from multiple directions. To solve technical difficulties, such as the image-recognition task, traditional deep learning with convolution neural networks (CNNs) has been widely applied in recent years. However, this kind of neural network, such as Faster Regions with CNN features (R-CNN), cannot leverage the spatial information because it is applied in natural images (2D) rather than medical images (3D), such as computed tomography (CT) images. To address this issue, we propose a novel algorithm that is appropriate for liver CT imaging. Furthermore, according to radiologists' experience in clinical diagnosis and the characteristics of CT images of liver cancer, a liver cancer-detection framework with CNN, including image processing, feature extraction, region proposal, image registration, and classification recognition, was proposed to facilitate the effective detection of liver lesions.