1
|
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) 2024:10.1007/s00261-024-04509-z. [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] [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
|
2
|
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
|
3
|
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
|
4
|
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] [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
|
5
|
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: 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: 09/14/2023] [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
|
6
|
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
|
7
|
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
|
8
|
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
|
9
|
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: 0] [Impact Index Per Article: 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
|