Acute pancreatitis(AP)is a potentially life-threatening inflammatory disease of the pancreas,with clinical management determined by the severity of the disease.Diagnosis,severity prediction,and prognosis assessment of...Acute pancreatitis(AP)is a potentially life-threatening inflammatory disease of the pancreas,with clinical management determined by the severity of the disease.Diagnosis,severity prediction,and prognosis assessment of AP typically involve the use of imaging technologies,such as computed tomography,magnetic resonance imaging,and ultrasound,and scoring systems,including Ranson,Acute Physiology and Chronic Health Evaluation II,and Bedside Index for Severity in AP scores.Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity,while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications.Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild,moderate,or severe categories,guiding treatment decisions,such as intensive care unit admission,early enteral feeding,and antibiotic use.Despite the central role of imaging technologies and scoring systems in AP management,these methods have limitations in terms of accuracy,reproducibility,practicality and economics.Recent advancements of artificial intelligence(AI)provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data.AI algorithms can analyze large amounts of clinical and imaging data,identify scoring system patterns,and predict the clinical course of disease.AI-based models have shown promising results in predicting the severity and mortality of AP,but further validation and standardization are required before widespread clinical application.In addition,understanding the correlation between these three technologies will aid in developing new methods that can accurately,sensitively,and specifically be used in the diagnosis,severity prediction,and prognosis assessment of AP through complementary advantages.展开更多
AIM: To establish a model for prognosis assessment of extranodal follicular dendritic cell (FDC) sarcoma.METHODS: Nine lesions were examined by routine and molecular approaches.Clinicopathological factors from the new...AIM: To establish a model for prognosis assessment of extranodal follicular dendritic cell (FDC) sarcoma.METHODS: Nine lesions were examined by routine and molecular approaches.Clinicopathological factors from the new cases and 97 reported cases were analyzed for their prognostic values.RESULTS: The current lesions were found in f ive male and four female patients,located mainly in the head and neck area and averaging 7.2 cm in size.Six patients had recurrence or metastasis and three remained free of disease.The 106 patients (male/female ratio,1.1:1) were aged from 9 to 82 years (median,44 years).The tumor sizes ranged from 1.5 to 21 cm (mean,7.4 cm).Abdominal/pelvic region was affected most frequently (43%).Surgical resection was performed in 100 patients,followed by radiation and/or chemotherapy in 35 of them.Follow-up data were available in 91 cases,covering a period of 3-324 mo (mean,27 mo;median,19 mo).Of the informative cases,38 (42%) had recurrence or metastasis,and 12 (13%) died of the disease.These tumors were classif ied histologically into lowand high-grade lesions.A size ≥ 5 cm (P = 0.003),highgrade histology (P = 0.046) and a mitotic count ≥ 5/10 HPF (P = 0.013) were associated with tumor recurrence.The lesions were def ined as low-,intermediateand high-risk tumors,and their recurrence rates were 16%,46% and 73%,and their mortality rates 0%,4% and 45%,respectively.CONCLUSION: Extranodal FDC tumors behave like soft tissue sarcomas.Their clinical outcomes are variable and can be evaluated according to their sizes and grades.展开更多
基金Fujian Provincial Health Technology Project,No.2020GGA079Natural Science Foundation of Fujian Province,No.2021J011380National Natural Science Foundation of China,No.62276146.
文摘Acute pancreatitis(AP)is a potentially life-threatening inflammatory disease of the pancreas,with clinical management determined by the severity of the disease.Diagnosis,severity prediction,and prognosis assessment of AP typically involve the use of imaging technologies,such as computed tomography,magnetic resonance imaging,and ultrasound,and scoring systems,including Ranson,Acute Physiology and Chronic Health Evaluation II,and Bedside Index for Severity in AP scores.Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity,while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications.Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild,moderate,or severe categories,guiding treatment decisions,such as intensive care unit admission,early enteral feeding,and antibiotic use.Despite the central role of imaging technologies and scoring systems in AP management,these methods have limitations in terms of accuracy,reproducibility,practicality and economics.Recent advancements of artificial intelligence(AI)provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data.AI algorithms can analyze large amounts of clinical and imaging data,identify scoring system patterns,and predict the clinical course of disease.AI-based models have shown promising results in predicting the severity and mortality of AP,but further validation and standardization are required before widespread clinical application.In addition,understanding the correlation between these three technologies will aid in developing new methods that can accurately,sensitively,and specifically be used in the diagnosis,severity prediction,and prognosis assessment of AP through complementary advantages.
基金Supported by Grants from National Natural Science Foundation of China,No.30171052,30572125 and 30772508
文摘AIM: To establish a model for prognosis assessment of extranodal follicular dendritic cell (FDC) sarcoma.METHODS: Nine lesions were examined by routine and molecular approaches.Clinicopathological factors from the new cases and 97 reported cases were analyzed for their prognostic values.RESULTS: The current lesions were found in f ive male and four female patients,located mainly in the head and neck area and averaging 7.2 cm in size.Six patients had recurrence or metastasis and three remained free of disease.The 106 patients (male/female ratio,1.1:1) were aged from 9 to 82 years (median,44 years).The tumor sizes ranged from 1.5 to 21 cm (mean,7.4 cm).Abdominal/pelvic region was affected most frequently (43%).Surgical resection was performed in 100 patients,followed by radiation and/or chemotherapy in 35 of them.Follow-up data were available in 91 cases,covering a period of 3-324 mo (mean,27 mo;median,19 mo).Of the informative cases,38 (42%) had recurrence or metastasis,and 12 (13%) died of the disease.These tumors were classif ied histologically into lowand high-grade lesions.A size ≥ 5 cm (P = 0.003),highgrade histology (P = 0.046) and a mitotic count ≥ 5/10 HPF (P = 0.013) were associated with tumor recurrence.The lesions were def ined as low-,intermediateand high-risk tumors,and their recurrence rates were 16%,46% and 73%,and their mortality rates 0%,4% and 45%,respectively.CONCLUSION: Extranodal FDC tumors behave like soft tissue sarcomas.Their clinical outcomes are variable and can be evaluated according to their sizes and grades.
