Advances in research of radiomics and metabolomics in acute pancreatitis
Acute pancreatitis (AP) is an acute abdominal disease that is prone to organ dysfunction, with high mortality. Timely prediction of the occurrence and development trend of the disease is the prerequisite for early treatment and intervention. Radiomics can extract quantitative features from medical i...
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Editorial Office of Journal of Diagnostics Concepts & Practice
2024-08-01
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| Series: | Zhenduanxue lilun yu shijian |
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| Online Access: | https://www.qk.sjtu.edu.cn/jdcp/fileup/1671-2870/PDF/1732171328599-410330296.pdf |
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| _version_ | 1846161389082640384 |
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| author | ZHONG Jingyu, DING Defang, XING Yue, HU Yangfan, ZHANG Huan, YAO Weiwu |
| author_facet | ZHONG Jingyu, DING Defang, XING Yue, HU Yangfan, ZHANG Huan, YAO Weiwu |
| author_sort | ZHONG Jingyu, DING Defang, XING Yue, HU Yangfan, ZHANG Huan, YAO Weiwu |
| collection | DOAJ |
| description | Acute pancreatitis (AP) is an acute abdominal disease that is prone to organ dysfunction, with high mortality. Timely prediction of the occurrence and development trend of the disease is the prerequisite for early treatment and intervention. Radiomics can extract quantitative features from medical images with high throughput and realize deep data mining. It can be used for the diagnosis of acute pancreatitis and prediction of severity, progression and recurrence of the disease. For the diagnosis of AP, CT radiomics can distinguish recurrent AP patients from functional abdominal pain, chronic pancreatitis, and recurrent AP patients, with an area under curve (AUC) of 0.88. For predicting AP recurrence, CT radiomics can accurately predict AP recurrence within 48 months, with an AUC of 0.93. For predicting the severity of AP, MRI radiomics can predict whether AP patients will progress to moderate to severe AP in the future, with an AUC of 0.85, which is better than clinical scoring systems. For predicting complications and progression of AP, MRI radiomics can effectively predict the occurrence of peripancreatic necrosis, with an AUC of 0.92. Metabolomics has confirmed that metabolic spectrum changes dynamically during the occurrence and development of AP. It has been reported that active metabolites can be used as early warning indicators for the diagnosis, etiology identification and severity assessment of AP. In addition, urinary metabolomics allows accurate diagnosis of AP, with an AUC of 0.91. For identifying the etiology of AP, the blood metabolomics models can identify patients with biliary AP, hyperlipidemic AP, and alcoholic AP, with AUCs of 0.89, 0.91, and 0.86, respectively. For predicting the severity of AP, the blood metabolomics models can accurately predict whether AP patients will progress to moderate to severe AP in the future, with an AUC of 0.99. The combination of the radiomics and metabolomics can complement each other's advantages and integrate multi-group data, which can jointly characterize the process and internal connections of disease occurrence and development from different levels, for achieving early warning and early intervention more effectively. |
| format | Article |
| id | doaj-art-ff9abaebbea74a1eb9660e5705a00651 |
| institution | Kabale University |
| issn | 1671-2870 |
| language | zho |
| publishDate | 2024-08-01 |
| publisher | Editorial Office of Journal of Diagnostics Concepts & Practice |
| record_format | Article |
| series | Zhenduanxue lilun yu shijian |
| spelling | doaj-art-ff9abaebbea74a1eb9660e5705a006512024-11-21T09:06:48ZzhoEditorial Office of Journal of Diagnostics Concepts & PracticeZhenduanxue lilun yu shijian1671-28702024-08-01230444545110.16150/j.1671-2870.2024.04.014Advances in research of radiomics and metabolomics in acute pancreatitisZHONG Jingyu, DING Defang, XING Yue, HU Yangfan, ZHANG Huan, YAO Weiwu01. Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200336, China;2. Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaAcute pancreatitis (AP) is an acute abdominal disease that is prone to organ dysfunction, with high mortality. Timely prediction of the occurrence and development trend of the disease is the prerequisite for early treatment and intervention. Radiomics can extract quantitative features from medical images with high throughput and realize deep data mining. It can be used for the diagnosis of acute pancreatitis and prediction of severity, progression and recurrence of the disease. For the diagnosis of AP, CT radiomics can distinguish recurrent AP patients from functional abdominal pain, chronic pancreatitis, and recurrent AP patients, with an area under curve (AUC) of 0.88. For predicting AP recurrence, CT radiomics can accurately predict AP recurrence within 48 months, with an AUC of 0.93. For predicting the severity of AP, MRI radiomics can predict whether AP patients will progress to moderate to severe AP in the future, with an AUC of 0.85, which is better than clinical scoring systems. For predicting complications and progression of AP, MRI radiomics can effectively predict the occurrence of peripancreatic necrosis, with an AUC of 0.92. Metabolomics has confirmed that metabolic spectrum changes dynamically during the occurrence and development of AP. It has been reported that active metabolites can be used as early warning indicators for the diagnosis, etiology identification and severity assessment of AP. In addition, urinary metabolomics allows accurate diagnosis of AP, with an AUC of 0.91. For identifying the etiology of AP, the blood metabolomics models can identify patients with biliary AP, hyperlipidemic AP, and alcoholic AP, with AUCs of 0.89, 0.91, and 0.86, respectively. For predicting the severity of AP, the blood metabolomics models can accurately predict whether AP patients will progress to moderate to severe AP in the future, with an AUC of 0.99. The combination of the radiomics and metabolomics can complement each other's advantages and integrate multi-group data, which can jointly characterize the process and internal connections of disease occurrence and development from different levels, for achieving early warning and early intervention more effectively.https://www.qk.sjtu.edu.cn/jdcp/fileup/1671-2870/PDF/1732171328599-410330296.pdf|radiomics|metabolomics|acute pancreatitis |
| spellingShingle | ZHONG Jingyu, DING Defang, XING Yue, HU Yangfan, ZHANG Huan, YAO Weiwu Advances in research of radiomics and metabolomics in acute pancreatitis Zhenduanxue lilun yu shijian |radiomics|metabolomics|acute pancreatitis |
| title | Advances in research of radiomics and metabolomics in acute pancreatitis |
| title_full | Advances in research of radiomics and metabolomics in acute pancreatitis |
| title_fullStr | Advances in research of radiomics and metabolomics in acute pancreatitis |
| title_full_unstemmed | Advances in research of radiomics and metabolomics in acute pancreatitis |
| title_short | Advances in research of radiomics and metabolomics in acute pancreatitis |
| title_sort | advances in research of radiomics and metabolomics in acute pancreatitis |
| topic | |radiomics|metabolomics|acute pancreatitis |
| url | https://www.qk.sjtu.edu.cn/jdcp/fileup/1671-2870/PDF/1732171328599-410330296.pdf |
| work_keys_str_mv | AT zhongjingyudingdefangxingyuehuyangfanzhanghuanyaoweiwu advancesinresearchofradiomicsandmetabolomicsinacutepancreatitis |