|
1
|
Xu GY, Gong YH, Wang Y, Han XL, Hao C, Xu L. Splenic artery aneurysm with double-rupture phenomenon and circulatory collapse following anesthesia induction: A case report. World J Clin Oncol 2025; 16:100957. [DOI: 10.5306/wjco.v16.i4.100957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2024] [Revised: 01/13/2025] [Accepted: 03/08/2025] [Indexed: 03/26/2025] Open
Abstract
BACKGROUND Splenic artery aneurysm (SAA) rupture is a rare, life-threatening condition characterized by acute intra-abdominal hemorrhage and hemodynamic instability. Ruptured SAAs may exhibit a biphasic and relatively slow clinical progression, commonly referred to as the “double-rupture phenomenon”. The reported incidence of the double-rupture phenomenon ranges 12%-21% in patients with ruptured SAAs, potentially due to variations in intra-abdominal pressure. Following anesthesia induction, muscle relaxation can decrease intra-abdominal pressure, potentially triggering the double-rupture phenomenon and leading to circulatory collapse.
CASE SUMMARY A 61-year-old female presented to the Department of Emergency with upper abdominal pain, abdominal distension, dizziness, and vomiting. Her vital signs were initially stable. Physical examination revealed abdominal tenderness and positive-shifting dullness. Abdominal contrast-enhanced computed tomography revealed cirrhosis, severe portal hypertension, and splenomegaly. Acute rupture was suggested by a hematoma on the upper left side outside the SAA. Surgeons deemed intravascular intervention challenging and open splenectomy inevitable. Circulatory collapse occurred after anesthesia induction, likely due to a double rupture of the SAA. This double-rupture phenomenon may have resulted from an initial rupture of the SAA into the omental bursa, forming a hematoma that exerted a tamponade effect. A second rupture into the peritoneal cavity may have been triggered by decreased intra-abdominal pressure following anesthesia induction. The patient’s life was saved through early, coordinated, multidisciplinary team collaboration. Following cardiopulmonary resuscitation and emergency splenectomy, she recovered without significant postoperative bleeding or hypoxic encephalopathy.
CONCLUSION Anesthesia-induced pressure reduction may trigger a second SAA rupture, causing collapse. Early diagnosis and multidisciplinary teamwork improve outcomes. This is a rare and life-threatening case of SAA rupture, which is of great significance to the medical community for understanding and handling such emergencies.
Collapse
Affiliation(s)
- Guang-Yan Xu
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Ya-Hong Gong
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Yi Wang
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Xian-Lin Han
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Chang Hao
- Department of Anesthesiology and Perioperative Medicine, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen 518067, Guangdong Province, China
| | - Li Xu
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China
| |
Collapse
|
|
2
|
Liang LX, Liang X, Zeng Y, Wang F, Yu XK. Establishment and validation of a nomogram for predicting esophagogastric variceal bleeding in patients with liver cirrhosis. World J Gastroenterol 2025; 31:102714. [DOI: 10.3748/wjg.v31.i9.102714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2024] [Revised: 01/06/2025] [Accepted: 01/15/2025] [Indexed: 02/18/2025] Open
Abstract
BACKGROUND Patients with decompensated liver cirrhosis suffering from esophagogastric variceal bleeding (EGVB) face high mortality.
AIM To investigate the risk factors for EGVB in patients with liver cirrhosis and establish a diagnostic nomogram.
METHODS Patients with liver cirrhosis who met the inclusion criteria were randomly divided into training and validation cohorts in a 6:4 ratio in this retrospective research. Univariate analysis, least absolute shrinkage and selection operator regression, and multivariate analysis were employed to establish the nomogram model. Calibration curve, the area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA) were applied to assess the discrimination, accuracy, and clinical practicability of the nomogram, respectively.
RESULTS A total of 1115 patients were enrolled in this study. The nomogram was established based on white blood cells (P < 0.001), hemoglobin (P < 0.001), fibrinogen (P < 0.001), total bilirubin (P = 0.007), activated partial thromboplastin time (P = 0.002), total bile acid (P = 0.012), and ascites (P = 0.006). The calibration curve indicated that the actual observation results were in good agreement with the prediction results of the model. The AUC values of the diagnostic model were 0.861 and 0.859 in the training and validation cohorts, respectively, which were higher than that of the aspartate aminotransferase-to-platelet ratio index, fibrosis index based on 4 factors, and aspartate aminotransferase-to-alanine aminotransferase ratio. Additionally, DCA indicated that the net benefit value of the model was higher than that of the other models.
CONCLUSION This research constructed and validated a nomogram with perfect performance for predicting EGVB events in patients with liver cirrhosis, which could help clinicians with timely diagnosis, individualized treatment, and follow-up.
Collapse
Affiliation(s)
- Lun-Xi Liang
- Department of Gastroenterology, The Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, China
- Department of Gastroenterology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha 410008, Hunan Province, China
- Hunan Key Laboratory of Nonresolving Inflammation and Cancer, The Third Xiangya Hospital, Central South University, Changsha 410006, Hunan Province, China
| | - Xiao Liang
- School of Clinical Medicine, Changsha Medical University, Changsha 410200, Hunan Province, China
| | - Ya Zeng
- Department of Gastroenterology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha 410008, Hunan Province, China
| | - Fen Wang
- Department of Gastroenterology, The Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, China
- Hunan Key Laboratory of Nonresolving Inflammation and Cancer, The Third Xiangya Hospital, Central South University, Changsha 410006, Hunan Province, China
| | - Xue-Ke Yu
- Department of Gastroenterology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha 410008, Hunan Province, China
| |
Collapse
|
|
3
|
Zhao H, Zhang X, Huang B, Shi X, Xiao L, Li Z. Application of machine learning methods for predicting esophageal variceal bleeding in patients with cirrhosis. Eur Radiol 2025; 35:1440-1450. [PMID: 39708084 DOI: 10.1007/s00330-024-11311-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Revised: 10/20/2024] [Accepted: 11/24/2024] [Indexed: 12/23/2024]
Abstract
OBJECTIVE To develop and compare machine learning models based on CT morphology features, serum biomarkers, and basic physical conditions to predict esophageal variceal bleeding. MATERIALS AND METHODS Two hundred twenty-four cirrhotic patients with esophageal variceal bleeding and non-bleeding were included in the retrospective study. Clinical and serum biomarkers were used in our study. In addition, the open-access segmentation model was used to generate segmentation masks of the liver and spleen. Four machine learning models based on selected features are used for building prediction models, and the diagnostic performances of models were measured using the receiver operator characteristic analysis. RESULTS Two hundred twenty-four cirrhosis patients with esophageal varices, including 112 patients with bleeding (mean age 52.8 ± 11.5 years, range 18-80 years) and 112 patients with non-bleeding (mean age 57.3 ± 10.5 years, range 34-85 years). The two groups showed significant differences in standardized spleen volume, fibrinogen, alanine aminotransferase, aspartate aminotransferase, D-dimer, platelet, and age. The ratio of the training set to the test set was 8:2 in our research, and the 5-fold cross-validation was used in the research. The AUCs of linear regression, random forest, support vector machine, and adaptive boosting were, respectively, 0.742, 0.854, 0.719, and 0.821 in the training set. For the test set, the AUCs of models were, respectively, 0.763, 0.818, 0.648, and 0.804. CONCLUSIONS Our study used CT morphological measurements, serum biomarkers, and age to build machine learning models, and the random forest and adaptive boosting had potential added value in predictive model construction. KEY POINTS Question Esophageal variceal bleeding is an intractable complication of liver cirrhosis. Early prediction and prevention of esophageal variceal bleeding is important for patients with liver cirrhosis. Findings It was feasible and clinically meaningful to construct machine learning models based on CT morphology features, serum biomarkers, and physical conditions to predict variceal bleeding. Clinical relevance Our study may provide a promising tool with which clinicians can conduct therapeutic decisions on fewer invasive procedures for the prediction of esophageal variceal bleeding.
Collapse
Affiliation(s)
- Haichen Zhao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiaoya Zhang
- College of Computer Science and Technology of Qingdao University, Qingdao, China
| | - Baoxiang Huang
- College of Computer Science and Technology of Qingdao University, Qingdao, China
| | - Xiaojuan Shi
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Longyang Xiao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhiming Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
| |
Collapse
|
|
4
|
Qiu YH, Chen FF, Zhang YH, Yang Z, Zhu GX, Chen BC, Miao SL. A predictive clinical-radiomics nomogram for early diagnosis of mesenteric arterial embolism based on non-contrast CT and biomarkers. Abdom Radiol (NY) 2025:10.1007/s00261-024-04745-3. [PMID: 39815026 DOI: 10.1007/s00261-024-04745-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2024] [Revised: 11/29/2024] [Accepted: 12/03/2024] [Indexed: 01/18/2025]
Abstract
PURPOSE Mesenteric artery embolism (MAE) is a relatively uncommon abdominal surgical emergency, but it can lead to catastrophic clinical outcomes if the diagnosis is delayed. This study aims to build a prediction model of clinical-radiomics nomogram for early diagnosis of MAE based on non-contrast computed tomography (CT) and biomarkers. METHOD In this retrospective study, a total of 364 patients confirmed as MAE (n = 131) or non-MAE (n = 233) who were randomly divided into a training cohort (70%) and a validation cohort (30%). In the training cohort, the minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithms were used to select optimal radiomics features from non-contrast CT images for calculating Radscore which was utilized to establish the radiomics model. Logistic regression analysis was performed to screen clinical factors, and then generate the clinical model. A predictive nomogram model was built using Radscore and the selected clinical risk factors, which was evaluated through the receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA). RESULTS Thirteen radiomics features were chosen to calculate Radscore. Age, white blood cell (WBC) count, creatine kinase (CK) and D-dimer were determined as the independent clinical factors. The clinical-radiomics nomogram model showed the best performance in training cohort. The nomogram model was with higher area under curve (AUC) value of 0.93, compared to radiomics model with AUC value of 0.90 or clinical model with AUC value of 0.78 in the validation cohort. The calibration curve showed that nomogram model achieved a good fit in both cohorts (P = 0.59 and 0.92, respectively). The DCA indicated that nomogram model was significantly favorable for clinical usefulness of MAE diagnosis. CONCLUSIONS The nomogram provides an effective tool for the early diagnosis of MAE, which may play a crucial role in shortening the time for therapeutic decision-making, thereby reducing the risk of intestinal necrosis and death.
Collapse
Affiliation(s)
- Yi-Hui Qiu
- Department of Vascular Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Fan-Feng Chen
- Department of Vascular Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yin-He Zhang
- Molecular Pharmacology Research Center, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, China
| | - Zhe Yang
- The Second Affiliated Hospital & The Second Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Guan-Xia Zhu
- Department of Radiology, Longgang People's Hospital, Wenzhou, China
| | - Bi-Cheng Chen
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shou-Liang Miao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| |
Collapse
|
|
5
|
Bai Y, Wang Z, Shi C, Chen L, Mei X, Kong D. Diagnosis and Treatment Options for Cirrhosis With Unexplained Upper Gastrointestinal Bleeding: An Observational Study Based on Endoscopic Ultrasonography. Surg Laparosc Endosc Percutan Tech 2025:00129689-990000000-00300. [PMID: 39812070 DOI: 10.1097/sle.0000000000001355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Accepted: 12/23/2024] [Indexed: 01/16/2025]
Abstract
OBJECTIVE To investigate the role of endoscopic ultrasonography (EUS) in the diagnosis and treatment of upper gastrointestinal bleeding of unknown origin in liver cirrhosis, focusing on patients with recurrent treatment of esophageal and gastric varices who failed to identify the bleeding site under direct endoscopy. BACKGROUND Esophagogastric variceal bleeding is one of the severe complications of decompensated liver cirrhosis, and serial endoscopic therapy can improve the long-term quality of life of patients. Most acute bleeding can be detected under direct endoscopy with thrombus or active bleeding, but there are still some patients with recurrent bleeding after repeated treatments, and it is difficult to find the bleeding site, especially in gastric variceal bleeding. Therefore, it is of great significance to identify the bleeding site. PATIENTS AND METHODS A total of 88 patients with liver cirrhosis bleeding diagnosed and treated under EUS were collected in this study, including 26 patients who failed to identify the bleeding site under direct endoscopy. EUS was used to scan the bleeding site, and EUS-guided treatment was performed. The characteristics of the bleeding site under direct endoscopy and EUS and the follow-up results after surgery were analyzed. RESULTS Among the 26 patients, 16 patients (16/26, 61.5%) showed red color signs of gastric fundus mucosa under direct endoscopy, 5 patients (5/26, 19.2%) showed punctate erosion, and the remaining 5 patients (5/26, 19.2%) showed no abnormal mucosal manifestations. All patients could find anechoic blood vessels under EUS, and blood flow signals could be detected within. Among them, 23 patients (23/26, 88.5%) chose EUS-guided treatment, and no rebleeding occurred after surgery. Another 3 patients (3/26, 11.5%) were given endoscopic variceal ligation due to small esophageal and gastric varices and inability to perform intravascular injection. The median follow-up time was 86 days. Adverse reactions included 5 cases of retrosternal pain (5/23, 21.7%), and 1 case of fever (1/23, 4.3%). CONCLUSION EUS can detect submucosal varices in the gastric mucosa, and these bleeding sites may present as red color signs or punctate erosion under direct endoscopy.
Collapse
Affiliation(s)
- Yuchuan Bai
- Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Key Laboratory of Digestive Diseases of Anhui Province, Hefei, Anhui, China
| | | | | | | | | | | |
Collapse
|
|
6
|
Wu X, Gu Y, Wang W, Wei J, Sun X, Hu W, He W. Intervention effect of ventilator‑assisted emergency endoscopy in the treatment of cirrhosis‑associated esophagogastric variceal bleeding. Wideochir Inne Tech Maloinwazyjne 2024; 19:436-441. [PMID: 40123730 PMCID: PMC11927536 DOI: 10.20452/wiitm.2024.17908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2024] [Revised: 10/31/2024] [Indexed: 03/25/2025] Open
Abstract
INTRODUCTION Emergency endoscopy has proven remarkably effective in the treatment of gastrotestinal bleeding. However, its efficacy has not been extensively evaluated specifically in patients with cirrhosis‑associated esophagogastric variceal bleeding (EGVB). The patients may experience stress and anxiety before being subjected to the procedure. AIM This study aimed to investigate the effect of ventilator‑assisted emergency endoscopy in the treatment of cirrhosis‑associated EGVB. MATERIALS AND METHODS A total of 63 patients with cirrhosis‑associated EGVB were enrolled in the study and divided into 2 groups using the random number table method. The control group (n = 31) received conventional emergency endoscopic hemostasis, while the observational group (n = 32) underwent ventilator‑assisted emergency endoscopic hemostasis. The hemostatic success rate, post‑treatment rebleeding rate, postoperative complication rate, length of stay in the intensive care unit (ICU), cost of hospitalization, and the patients' feeling of comfort (eg, fever and anxiety) were assessed in both groups. RESULTS There were no significant differences in the hemostatic success rate, rebleeding rate, mortality, length of stay in the ICU, or cost of hospitalization between the groups. The symptoms and feelings of anxiety and pain in the observational group were significantly less intense than in the control group. However, there was no significant difference in the frequency of postoperative fever between the groups. CONCLUSIONS In the emergency endoscopic treatment of patients with cirrhosis‑associated EGVB, using a ventilator ensures a smooth airway, keeps patients sedated throughout the procedure, and enhances their overall comfort. Ventilator‑assisted emergency endoscopy helps alleviate postoperative pain and reduces anxiety.
Collapse
Affiliation(s)
- Xiu‑Lian Wu
- Department of Intensive Care Medicine, Beijing You’an Hospital, Capital Medical University, China
| | - Yanmei Gu
- Department of Intensive Care Medicine, Beijing You’an Hospital, Capital Medical University, China
| | - Wenhui Wang
- Department of Intensive Care Medicine, Beijing You’an Hospital, Capital Medical University, China
| | - Jiayuan Wei
- Department of Intensive Care Medicine, Beijing You’an Hospital, Capital Medical University, China
| | - Xiaoqing Sun
- Department of Intensive Care Medicine, Beijing You’an Hospital, Capital Medical University, China
| | - Wenyue Hu
- Department of Intensive Care Medicine, Beijing You’an Hospital, Capital Medical University, China
| | - Wenhui He
- Department of Intensive Care Medicine, Beijing You’an Hospital, Capital Medical University, China
| |
Collapse
|
|
7
|
Yan C, Li M, Liu C, Zhang Z, Zhang J, Gao M, Han J, Zhang M, Zhao L. Development of a non-invasive diagnostic model for high-risk esophageal varices based on radiomics of spleen CT. Abdom Radiol (NY) 2024; 49:4373-4382. [PMID: 39096392 DOI: 10.1007/s00261-024-04509-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 07/23/2024] [Indexed: 08/05/2024]
Abstract
PURPOSE To evaluate the diagnostic performance of radiomics models derived from multi-phase spleen CT for high-risk esophageal varices (HREV) in cirrhotic patients. METHODS We retrospectively selected cirrhotic patients with esophageal varices from two hospitals from September 2019 to September 2023. Patients underwent non-contrast and contrast-enhanced CT scans and were categorized into HREV and non-HREV groups based on endoscopic evaluations. Radiomics features were extracted from spleen CT images in non-contrast, arterial, and portal venous phases, with feature selection via lasso regression and Pearson's correlation. Ten machine learning models were developed to diagnose HREV, evaluated by area under the curve (AUC). The AUC values of the three groups of models were statistically compared by the Kruskal-Wallis H test and Bonferroni-corrected Mann-Whitney U test. A p-value less than 0.05 was considered statistically significant. RESULTS Among 233 patients, 11, 6, and 11 features were selected from non-contrast, arterial, and portal venous phases, respectively. Significant differences in AUC values were observed across phases (p < 0.05), and the arterial phase models showed the highest AUC values. The best model in arterial phase was the logical regression model, whose AUC value was 0.85, sensitivity was 83.3%, specificity was 80% and F1 score was 0.81. CONCLUSION Radiomics models based on spleen CT, especially the arterial phase models, demonstrate high diagnostic accuracy for HREV, offering the potential for early detection and intervention.
Collapse
Affiliation(s)
- Cheng Yan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Min Li
- Department of Radiology, Beijing Traditional Chinese Medicine Hospital, Capital Medical University, Beijing, 100010, China
| | - Changchun Liu
- Department of Radiology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100039, China
| | - Zhe Zhang
- Department of Radiology, Beijing Changping Hospital of Chinese Medicine, Beijing, 102200, China
| | - Jingwen Zhang
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Mingzi Gao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Jing Han
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Mingxin Zhang
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Liqin Zhao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
| |
Collapse
|
|
8
|
Singh S, Chandan S, Vinayek R, Aswath G, Facciorusso A, Maida M. Comprehensive approach to esophageal variceal bleeding: From prevention to treatment. World J Gastroenterol 2024; 30:4602-4608. [PMID: 39575399 PMCID: PMC11572636 DOI: 10.3748/wjg.v30.i43.4602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2024] [Revised: 10/02/2024] [Accepted: 10/18/2024] [Indexed: 10/31/2024] Open
Abstract
Esophageal variceal bleeding is a severe complication often associated with portal hypertension, commonly due to liver cirrhosis. Prevention and treatment of this condition are critical for patient outcomes. Preventive strategies focus on reducing portal hypertension to prevent varices from developing or enlarging. Primary prophylaxis involves the use of non-selective beta-blockers, such as propranolol or nadolol, which lower portal pressure by decreasing cardiac output and thereby reducing blood flow to the varices. Endoscopic variceal ligation (EVL) may also be employed as primary prophylaxis to prevent initial bleeding episodes. Once bleeding occurs, immediate treatment is essential. Initial management includes hemodynamic stabilization followed by pharmacological therapy with vasoactive drugs such as octreotide or terlipressin to control bleeding. Endoscopic intervention is the cornerstone of treatment, with techniques such as EVL or sclerotherapy applied to directly manage the bleeding varices. In cases where bleeding is refractory to endoscopic treatment, transjugular intrahepatic portosystemic shunt may be considered to effectively reduce portal pressure. Long-term management after an acute bleeding episode involves secondary prophylaxis using beta-blockers and repeated EVL sessions to prevent rebleeding, complemented by monitoring and managing liver function to address the underlying disease. In light of new scientific evidence, including the findings of the study by Peng et al, this editorial aims to review available strategies for the prevention and treatment of esophageal varices.
Collapse
Affiliation(s)
- Sahib Singh
- Department of Internal Medicine, Sinai Hospital, Baltimore, MD 21215, United States
| | - Saurabh Chandan
- Center for Interventional Endoscopy, Advent Health, Orlando, FL 32803, United States
| | - Rakesh Vinayek
- Department of Gastroenterology, Sinai Hospital of Baltimore, Baltimore, MD 21215, United States
| | - Ganesh Aswath
- Division of Gastroenterology and Hepatology, State University of New York Upstate Medical University, Syracuse, NY 13210, United States
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia 71122, Italy
| | - Marcello Maida
- Department of Medicine and Surgery, University of Enna ‘Kore’, Enna 94100, Sicilia, Italy
| |
Collapse
|
|
9
|
Peng YJ, Liu X, Liu Y, Tang X, Zhao QP, Du Y. Computed tomography-based multi-organ radiomics nomogram model for predicting the risk of esophagogastric variceal bleeding in cirrhosis. World J Gastroenterol 2024; 30:4044-4056. [PMID: 39351251 PMCID: PMC11439117 DOI: 10.3748/wjg.v30.i36.4044] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 08/28/2024] [Accepted: 09/03/2024] [Indexed: 09/20/2024] Open
Abstract
BACKGROUND Radiomics has been used in the diagnosis of cirrhosis and prediction of its associated complications. However, most current studies predict the risk of esophageal variceal bleeding (EVB) based on image features at a single level, which results in incomplete data. Few studies have explored the use of global multi-organ radiomics for non-invasive prediction of EVB secondary to cirrhosis. AIM To develop a model based on clinical and multi-organ radiomic features to predict the risk of first-instance secondary EVB in patients with cirrhosis. METHODS In this study, 208 patients with cirrhosis were retrospectively evaluated and randomly split into training (n = 145) and validation (n = 63) cohorts. Three areas were chosen as regions of interest for extraction of multi-organ radiomic features: The whole liver, whole spleen, and lower esophagus-gastric fundus region. In the training cohort, radiomic score (Rad-score) was created by screening radiomic features using the inter-observer and intra-observer correlation coefficients and the least absolute shrinkage and selection operator method. Independent clinical risk factors were selected using multivariate logistic regression analyses. The radiomic features and clinical risk variables were combined to create a new radiomics-clinical model (RC model). The established models were validated using the validation cohort. RESULTS The RC model yielded the best predictive performance and accurately predicted the EVB risk of patients with cirrhosis. Ascites, portal vein thrombosis, and plasma prothrombin time were identified as independent clinical risk factors. The area under the receiver operating characteristic curve (AUC) values for the RC model, Rad-score (liver + spleen + esophagus), Rad-score (liver), Rad-score (spleen), Rad-score (esophagus), and clinical model in the training cohort were 0.951, 0.930, 0.801, 0.831, 0.864, and 0.727, respectively. The corresponding AUC values in the validation cohort were 0.930, 0.886, 0.763, 0.792, 0.857, and 0.692. CONCLUSION In patients with cirrhosis, combined multi-organ radiomics and clinical model can be used to non-invasively predict the probability of the first secondary EVB.
Collapse
Affiliation(s)
- Yu-Jie Peng
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
- Department of Radiology, The People’s Hospital of Chongqing Liang Jiang New Area, Chongqing 401121, China
| | - Xin Liu
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
- Department of Radiology, The People’s Hospital of Chongqing Liang Jiang New Area, Chongqing 401121, China
| | - Ying Liu
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xue Tang
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Qi-Peng Zhao
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Yong Du
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| |
Collapse
|
|
10
|
Li Z, He Q, Yang X, Zhu T, Li X, Lei Y, Tang W, Peng S. A clinical-radiomics nomogram for the prediction of the risk of upper gastrointestinal bleeding in patients with decompensated cirrhosis. Front Med (Lausanne) 2024; 11:1308435. [PMID: 39144667 PMCID: PMC11322063 DOI: 10.3389/fmed.2024.1308435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 07/22/2024] [Indexed: 08/16/2024] Open
Abstract
Objective To develop a model that integrates radiomics features and clinical factors to predict upper gastrointestinal bleeding (UGIB) in patients with decompensated cirrhosis. Methods 104 decompensated cirrhosis patients with UGIB and 104 decompensated cirrhosis patients without UGIB were randomized according to a 7:3 ratio into a training cohort (n = 145) and a validation cohort (n = 63). Radiomics features of the abdominal skeletal muscle area (SMA) were extracted from the cross-sectional image at the largest level of the third lumbar vertebrae (L3) on the abdominal unenhanced multi-detector computer tomography (MDCT) images. Clinical-radiomics nomogram were constructed by combining a radiomics signature (Rad score) with clinical independent risk factors associated with UGIB. Nomogram performance was evaluated in calibration, discrimination, and clinical utility. Results The radiomics signature was built using 11 features. Plasma prothrombin time (PT), sarcopenia, and Rad score were independent predictors of the risk of UGIB in patients with decompensated cirrhosis. The clinical-radiomics nomogram performed well in both the training cohort (AUC, 0.902; 95% CI, 0.850-0.954) and the validation cohort (AUC, 0.858; 95% CI, 0.762-0.953) compared with the clinical factor model and the radiomics model and displayed excellent calibration in the training cohort. Decision curve analysis (DCA) demonstrated that the predictive efficacy of the clinical-radiomics nomogram model was superior to that of the clinical and radiomics model. Conclusion Clinical-radiomics nomogram that combines clinical factors and radiomics features has demonstrated favorable predictive effects in predicting the occurrence of UGIB in patients with decompensated cirrhosis. This helps in early diagnosis and treatment of the disease, warranting further exploration and research.
Collapse
Affiliation(s)
- Zhichun Li
- Chongqing Health Center for Women and Children, Women and Children’s Hospital of Chongqing Medical University, Chongqing, China
- Sichuan Key Laboratory of Medical Imaging, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Qian He
- Sichuan Key Laboratory of Medical Imaging, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiao Yang
- Sichuan Key Laboratory of Medical Imaging, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Tingting Zhu
- Sichuan Key Laboratory of Medical Imaging, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xinghui Li
- Sichuan Key Laboratory of Medical Imaging, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yan Lei
- Department of Clinical Laboratory, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Wei Tang
- Chongqing Health Center for Women and Children, Women and Children’s Hospital of Chongqing Medical University, Chongqing, China
- Sichuan Key Laboratory of Medical Imaging, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Song Peng
- Chongqing Health Center for Women and Children, Women and Children’s Hospital of Chongqing Medical University, Chongqing, China
| |
Collapse
|
|
11
|
Xu J, Tan L, Jiang N, Li F, Wang J, Wang B, Li S. Assessment of nomogram model for the prediction of esophageal variceal hemorrhage in hepatitis B-induced hepatic cirrhosis. Eur J Gastroenterol Hepatol 2024; 36:758-765. [PMID: 38683192 PMCID: PMC11045406 DOI: 10.1097/meg.0000000000002750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 02/12/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND Esophageal variceal (EV) hemorrhage is a life-threatening consequence of portal hypertension in hepatitis B virus (HBV) -induced cirrhotic patients. Screening upper endoscopy and endoscopic variceal ligation to find EVs for treatment have complications, contraindications, and high costs. We sought to identify the nomogram models (NMs) as alternative predictions for the risk of EV hemorrhage. METHODS In this case-control study, we retrospectively analyzed 241 HBV-induced liver cirrhotic patients treated for EVs at the Second People's Hospital of Fuyang City, China from January 2021 to April 2023. We applied univariate analysis and multivariate logistic regression to assess the accuracy of various NMs in EV hemorrhage. The area under the curve (AUC) and calibration curves of the receiver's operating characteristics were used to evaluate the predictive accuracy of the nomogram. Decision curve analysis (DCA) was used to determine the clinically relevant of nomograms. RESULTS In the prediction group, multivariate logistic regression analysis identified platelet distribution and spleen length as independent risk factors for EVs. We applied NMs as the independent risk factors to predict EVs risk. The NMs fit well with the calibration curve and have good discrimination ability. The AUC and DCA demonstrated that NMs with a good net benefit. The above results were validated in the validation cohort. CONCLUSION Our non-invasive NMs based on the platelet distribution width and spleen length may be used to predict EV hemorrhage in HBV-induced cirrhotic patients. NMs can help clinicians to increase diagnostic performance leading to improved treatment measures.
Collapse
Affiliation(s)
- Jing Xu
- Department of Hepatology, The Second People’s Hospital of Fuyang City, Fuyang, Anhui Province, P.R. of China
| | - Lin Tan
- Department of Hepatology, The Second People’s Hospital of Fuyang City, Fuyang, Anhui Province, P.R. of China
| | - Ning Jiang
- Department of Hepatology, The Second People’s Hospital of Fuyang City, Fuyang, Anhui Province, P.R. of China
| | - Fengcheng Li
- Department of Hepatology, The Second People’s Hospital of Fuyang City, Fuyang, Anhui Province, P.R. of China
| | - Jinling Wang
- Department of Hepatology, The Second People’s Hospital of Fuyang City, Fuyang, Anhui Province, P.R. of China
| | - Beibei Wang
- Department of Hepatology, The Second People’s Hospital of Fuyang City, Fuyang, Anhui Province, P.R. of China
| | - Shasha Li
- Department of Hepatology, The Second People’s Hospital of Fuyang City, Fuyang, Anhui Province, P.R. of China
| |
Collapse
|
|
12
|
Peng J, Zeng X, Huang S, Zhang H, Xia H, Zou K, Zhang W, Shi X, Shi L, Zhong X, Lü M, Peng Y, Tang X. Trends of hospitalisation among new admission inpatients with oesophagogastric variceal bleeding in cirrhosis from 2014 to 2019 in the Affiliated Hospital of Southwest Medical University: a single-centre time-series analysis. BMJ Open 2024; 14:e074608. [PMID: 38423766 PMCID: PMC10910539 DOI: 10.1136/bmjopen-2023-074608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
OBJECTIVES This study aimed to assess the internal law and time trend of hospitalisation for oesophagogastric variceal bleeding (EGVB) in cirrhosis and develop an effective model to predict the trend of hospitalisation time. DESIGN We used a time series covering 72 months to analyse the hospitalisation for EGVB in cirrhosis. The number of inpatients in the first 60 months was used as the training set to establish the autoregressive integrated moving average (ARIMA) model, and the number over the next 12 months was used as the test set to predict and observe their fitting effect. SETTING AND DATA Case data of patients with EGVB between January 2014 and December 2019 were collected from the Affiliated Hospital of Southwest Medical University. OUTCOME MEASURES The number of monthly hospitalised patients with EGVB in our hospital. RESULTS A total of 877 patients were included in the analysis. The proportion of EGVB in patients with cirrhosis was 73% among men and 27% among women. The peak age at hospitalisation was 40-60 years. The incidence of EGVB varied seasonally with two peaks from January to February and October to November, while the lowest number was observed between April and August. Time-series analysis showed that the number of inpatients with EGVB in our hospital increased annually. The sequence after the first-order difference was a stationary series (augmented Dickey-Fuller test p=0.02). ARIMA (0,1,0) (0,1,1)12 with a minimum Akaike Information Criterion value of 260.18 could fit the time trend of EGVB inpatients and had a good short-term prediction effect. The root mean square error and mean absolute error were 2.4347 and 1.9017, respectively. CONCLUSIONS The number of hospitalised patients with EGVB at our hospital is increasing annually, with seasonal changes. The ARIMA model has a good prediction effect on the number of hospitalised patients with EGVB in cirrhosis.
Collapse
Affiliation(s)
- Jieyu Peng
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Key Laboratory of Nuclear Medicine and Molecular Imaging of Sichuan Province, Luzhou, Sichuan, China
| | - Xinyi Zeng
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Key Laboratory of Nuclear Medicine and Molecular Imaging of Sichuan Province, Luzhou, Sichuan, China
| | - Shu Huang
- Department of Gastroenterology, Lianshui County People's Hospital, Huai'an, Jiangsu, China
| | - Han Zhang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Key Laboratory of Nuclear Medicine and Molecular Imaging of Sichuan Province, Luzhou, Sichuan, China
| | - Huifang Xia
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Key Laboratory of Nuclear Medicine and Molecular Imaging of Sichuan Province, Luzhou, Sichuan, China
| | - Kang Zou
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Key Laboratory of Nuclear Medicine and Molecular Imaging of Sichuan Province, Luzhou, Sichuan, China
| | - Wei Zhang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Key Laboratory of Nuclear Medicine and Molecular Imaging of Sichuan Province, Luzhou, Sichuan, China
| | - Xiaomin Shi
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Key Laboratory of Nuclear Medicine and Molecular Imaging of Sichuan Province, Luzhou, Sichuan, China
| | - Lei Shi
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Key Laboratory of Nuclear Medicine and Molecular Imaging of Sichuan Province, Luzhou, Sichuan, China
| | - Xiaolin Zhong
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Key Laboratory of Nuclear Medicine and Molecular Imaging of Sichuan Province, Luzhou, Sichuan, China
| | - Muhan Lü
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Key Laboratory of Nuclear Medicine and Molecular Imaging of Sichuan Province, Luzhou, Sichuan, China
| | - Yan Peng
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Key Laboratory of Nuclear Medicine and Molecular Imaging of Sichuan Province, Luzhou, Sichuan, China
| | - Xiaowei Tang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Key Laboratory of Nuclear Medicine and Molecular Imaging of Sichuan Province, Luzhou, Sichuan, China
| |
Collapse
|
|
13
|
Lu T, Lu M, Wu D, Ding YY, Liu HN, Li TT, Song DQ. Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study. World J Gastrointest Surg 2024; 16:85-94. [PMID: 38328326 PMCID: PMC10845275 DOI: 10.4240/wjgs.v16.i1.85] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/24/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Gastric cancer is one of the most common malignant tumors in the digestive system, ranking sixth in incidence and fourth in mortality worldwide. Since 42.5% of metastatic lymph nodes in gastric cancer belong to nodule type and peripheral type, the application of imaging diagnosis is restricted. AIM To establish models for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) algorithms and to evaluate their predictive performance in clinical practice. METHODS Data of a total of 369 patients who underwent radical gastrectomy at the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy at the Department of General Surgery of Jining First People's Hospital (Jining, China) were collected and analyzed as the verification group. Seven ML models, including decision tree, random forest, support vector machine (SVM), gradient boosting machine, naive Bayes, neural network, and logistic regression, were developed to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML models were established following ten cross-validation iterations using the training dataset, and subsequently, each model was assessed using the test dataset. The models' performance was evaluated by comparing the area under the receiver operating characteristic curve of each model. RESULTS Among the seven ML models, except for SVM, the other ones exhibited higher accuracy and reliability, and the influences of various risk factors on the models are intuitive. CONCLUSION The ML models developed exhibit strong predictive capabilities for lymph node metastasis in gastric cancer, which can aid in personalized clinical diagnosis and treatment.
Collapse
Affiliation(s)
- Tong Lu
- Department of Emergency Medicine, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
| | - Miao Lu
- Wuxi Mental Health Center, Wuxi 214000, Jiangsu Province, China
| | - Dong Wu
- Department of Emergency Medicine, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
| | - Yuan-Yuan Ding
- Department of Gastroenterology, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
| | - Hao-Nan Liu
- Department of Oncology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221002, Jiangsu Province, China
| | - Tao-Tao Li
- Department of Emergency Medicine, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
| | - Da-Qing Song
- Department of Emergency Medicine, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
| |
Collapse
|
|
14
|
Lu T, Fang Y, Liu H, Chen C, Li T, Lu M, Song D. Comparison of Machine Learning and Logic Regression Algorithms for Predicting Lymph Node Metastasis in Patients with Gastric Cancer: A two-Center Study. Technol Cancer Res Treat 2024; 23:15330338231222331. [PMID: 38190617 PMCID: PMC10775719 DOI: 10.1177/15330338231222331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 11/01/2023] [Accepted: 11/20/2023] [Indexed: 01/10/2024] Open
Abstract
OBJECTIVES This two-center study aimed to establish a model for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) and logistic regression (LR) algorithms, and to evaluate its predictive performance in clinical practice. METHODS Data of a total of 369 patients who underwent radical gastrectomy in the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy in the Department of General Surgery of Jining First People's Hospital (Jining, China) were collected and analyzed as the verification group. Besides, 7 ML and logistic models were developed, including decision tree, random forest, support vector machine (SVM), gradient boosting machine (GBM), naive Bayes, neural network, and LR, in order to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML model was established following 10 cross-validation iterations within the training dataset, and subsequently, each model was assessed using the test dataset. The model's performance was evaluated by comparing the area under the receiver operating characteristic curve of each model. RESULTS Compared with the traditional logistic model, among the 7 ML algorithms, except for SVM, the other models exhibited higher accuracy and reliability, and the influences of various risk factors on the model were more intuitive. CONCLUSION For the prediction of lymph node metastasis in gastric cancer patients, the ML algorithm outperformed traditional LR, and the GBM algorithm exhibited the most robust predictive capability.
Collapse
Affiliation(s)
- Tong Lu
- Department of emergency medicine, Jining No.1 People's Hospital, Jining, China
| | - Yu Fang
- Jiangsu Normal University, Xuzhou, China
| | - Haonan Liu
- Department of Oncology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Chong Chen
- Department of Gastroenterology, Xuzhou No.1 People's Hospital, Xuzhou, China
| | - Taotao Li
- Department of emergency medicine, Jining No.1 People's Hospital, Jining, China
| | - Miao Lu
- Wuxi Mental Health Center, Wuxi, China
| | - Daqing Song
- Department of emergency medicine, Jining No.1 People's Hospital, Jining, China
| |
Collapse
|
|
15
|
Cheng JA, Lin YC, Lin Y, Wu RC, Lu HY, Yang LY, Chiang HJ, Juan YH, Lai YC, Lin G. Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT. Diagnostics (Basel) 2023; 13:3632. [PMID: 38132216 PMCID: PMC10742777 DOI: 10.3390/diagnostics13243632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/01/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND We aimed to develop and validate a preoperative CT-based radiomics signature for differentiating lymphoma versus benign splenomegaly. METHODS We retrospectively analyzed CT studies from 139 patients (age range 26-93 years, 43% female) between 2011 and 2019 with histopathological diagnosis of the spleen (19 lymphoma, 120 benign) and divided them into developing (n = 79) and testing (n = 60) datasets. The volumetric radiomic features were extracted from manual segmentation of the whole spleen on venous-phase CT imaging using PyRadiomics package. LASSO regression was applied for feature selection and development of the radiomic signature, which was interrogated with the complete blood cell count and differential count. All p values < 0.05 were considered to be significant. RESULTS Seven features were selected for constructing the radiomic signature after feature selection, including first-order statistics (10th percentile and Robust Mean Absolute Deviation), shape-based (Surface Area), and texture features (Correlation, MCC, Small Area Low Gray-level Emphasis and Low Gray-level Zone Emphasis). The radiomic signature achieved an excellent diagnostic accuracy of 97%, sensitivity of 89%, and specificity of 98%, distinguishing lymphoma versus benign splenomegaly in the testing dataset. The radiomic signature significantly correlated with the platelet and segmented neutrophil percentage. CONCLUSIONS CT-based radiomics signature can be useful in distinguishing lymphoma versus benign splenomegaly and can reflect the changes in underlying blood profiles.
Collapse
Affiliation(s)
- Jih-An Cheng
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
| | - Yu-Chun Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Yenpo Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Ren-Chin Wu
- Department of Pathology, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan;
| | - Hsin-Ying Lu
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Lan-Yan Yang
- Clinical Trial Center, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan;
| | - Hsin-Ju Chiang
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Yu-Hsiang Juan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
| | - Ying-Chieh Lai
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Gigin Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| |
Collapse
|
|
16
|
Guinazu C, Fernández Muñoz A, Maldonado MD, De La Cruz JA, Herrera D, Arruarana VS, Calderon Martinez E. Assessing the Predictive Factors for Bleeding in Esophageal Variceal Disease: A Systematic Review. Cureus 2023; 15:e48954. [PMID: 38106778 PMCID: PMC10725706 DOI: 10.7759/cureus.48954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2023] [Indexed: 12/19/2023] Open
Abstract
Esophageal varices, dilated submucosal distal esophageal veins, are a common source of upper gastrointestinal bleeding in patients with portal hypertension. This review aims to comprehensively assess predictive factors for both the first occurrence and subsequent risk of esophageal variceal bleeding. A systematic search was conducted in PubMed/MEDLINE (Medical Literature Analysis and Retrieval System Online) and Cochrane databases. A total of 33 studies were selected using rigorous inclusion and exclusion criteria. The risk of bias was assessed using the Newcastle-Ottawa Scale. Several predictive factors were identified for esophageal variceal bleeding, including the Child-Pugh score, Fibrosis Index, specific endoscopic findings, ultrasound parameters, portal vein diameter, presence and size of collaterals, CT scan findings, ascites, platelet counts, coagulation parameters, albumin levels, Von Willebrand Factor, bilirubin levels, diabetes mellitus, and the use of b-blocking agents in primary prophylaxis. The findings of this systematic review shed light on multiple potential predictive factors for esophageal variceal bleeding. Endoscopic findings were found to be reliable predictors. Additionally, ultrasound parameters showed associations with bleeding risk. This systematic review identifies multiple potential predictive factors for esophageal variceal bleeding in patients with portal hypertension. While certain factors exhibit strong predictive capabilities, further research is needed to refine and validate these findings, considering potential limitations and biases. This study serves as a critical resource for bridging knowledge gaps in this field.
Collapse
Affiliation(s)
- Camila Guinazu
- Internal Medicine, Universidad del Salvador, Buenos Aires, ARG
| | - Adolfo Fernández Muñoz
- Cardiovascular Medicine, Queen Elizabeth Hospital, Bridgetown, BRB
- Cardiovascular Medicine, Universidad de Ciencias Médicas - Santiago de Cuba, Santiago de Cuba, CUB
| | - Maria D Maldonado
- Medicine, Faculty of Medicine, Universidad Nacional de Córdoba, Cordoba, ARG
| | - Jeffry A De La Cruz
- Medicine, Universidad Tecnológica de Santiago (UTESA), Santiago de los Caballeros, DOM
| | - Domenica Herrera
- Medicine, Pontificia Universidad Católica del Ecuador, Quito, ECU
| | | | | |
Collapse
|
|
17
|
Fanni SC, Febi M, Francischello R, Caputo FP, Ambrosini I, Sica G, Faggioni L, Masala S, Tonerini M, Scaglione M, Cioni D, Neri E. Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment. Diagnostics (Basel) 2023; 13:2623. [PMID: 37627882 PMCID: PMC10453085 DOI: 10.3390/diagnostics13162623] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/25/2023] [Accepted: 08/05/2023] [Indexed: 08/27/2023] Open
Abstract
The spleen, often referred to as the "forgotten organ", plays numerous important roles in various diseases. Recently, there has been an increased interest in the application of radiomics in different areas of medical imaging. This systematic review aims to assess the current state of the art and evaluate the methodological quality of radiomics applications in spleen imaging. A systematic search was conducted on PubMed, Scopus, and Web of Science. All the studies were analyzed, and several characteristics, such as year of publication, research objectives, and number of patients, were collected. The methodological quality was evaluated using the radiomics quality score (RQS). Fourteen articles were ultimately included in this review. The majority of these articles were published in non-radiological journals (78%), utilized computed tomography (CT) for extracting radiomic features (71%), and involved not only the spleen but also other organs for feature extraction (71%). Overall, the included papers achieved an average RQS total score of 9.71 ± 6.37, corresponding to an RQS percentage of 27.77 ± 16.04. In conclusion, radiomics applications in spleen imaging demonstrate promising results in various clinical scenarios. However, despite all the included papers reporting positive outcomes, there is a lack of consistency in the methodological approaches employed.
Collapse
Affiliation(s)
- Salvatore Claudio Fanni
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Maria Febi
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Roberto Francischello
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Francesca Pia Caputo
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Ilaria Ambrosini
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Giacomo Sica
- Radiology Unit, Monaldi Hospital, 80131 Napoli, Italy
| | - Lorenzo Faggioni
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Salvatore Masala
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Michele Tonerini
- Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, 56124 Pisa, Italy
| | - Mariano Scaglione
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Dania Cioni
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Emanuele Neri
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| |
Collapse
|