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Bao W, Liao M, Yang J, Huang J, Zeng K, Lu Q. A nomogram based on ultrasonographic features and clinical indicators for differentiating mass-forming intrahepatic cholangiocarcinoma and liver metastatic colorectal adenocarcinoma. Front Oncol 2023; 13:1245686. [PMID: 38023257 PMCID: PMC10644673 DOI: 10.3389/fonc.2023.1245686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
Objective This study aimed to develop and validate a nomogram based on ultrasonographic features and clinical indicators to differentiate mass-forming intrahepatic cholangiocarcinoma (MF-ICC) from hepatic metastatic colorectal adenocarcinoma. Materials and methods A total of 343 patients with pathologically confirmed MF-ICC or metastatic colorectal adenocarcinoma were enrolled between October 2018 and July 2022. Patients were randomly assigned to training and validation sets at a ratio of 7:3. Preoperative ultrasound features and clinical indicators were retrieved. Univariate logistic regression analysis was employed to select relevant features. Multivariate logistic regression analysis was used to establish a predictive model, which was presented as a nomogram in training sets. The model's performance was assessed in terms of discrimination, calibration, and clinical usefulness. Results The study included 169 patients with MF-ICC and 174 with liver metastatic colorectal adenocarcinoma, assigned to training (n=238) and validation (n=105) cohorts. The nomogram incorporated ultrasound features (tumor size, lesion number, echogenicity, tumor necrosis, and rim arterial phase hyperenhancement) and clinical information (serum levels of CEA, CA19-9, CA125). The nomogram demonstrated promising performance in differentiating these two entities in both training and validation sets, with an AUC value of 0.937 (95%CI: 0.907,0.969) and 0.916 (95%CI: 0.863,0.968), respectively. The Hosmer-Lemeshow test and calibration curves confirmed good consistency between predictions and observations. Additionally, decision curve analysis confirmed the nomogram's high clinical practicability. Conclusion The nomogram based on ultrasound features and clinical indicators demonstrated good discrimination performance in differentiating MF-ICC from metastatic colorectal adenocarcinoma, which may enhance clinical decision-making process in managing these challenging diagnostic scenarios.
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Affiliation(s)
| | | | | | | | | | - Qiang Lu
- Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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2
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Zhang T, Zhang S, Jin C, Lin Z, Deng T, Xie X, Deng L, Li X, Ma J, Ding X, Liu Y, Shan Y, Yu Z, Wang Y, Chen G, Li J. A Predictive Model Based on the Gut Microbiota Improves the Diagnostic Effect in Patients With Cholangiocarcinoma. Front Cell Infect Microbiol 2021; 11:751795. [PMID: 34888258 PMCID: PMC8650695 DOI: 10.3389/fcimb.2021.751795] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 11/01/2021] [Indexed: 12/12/2022] Open
Abstract
Cholangiocarcinoma (CCA) is a malignant hepatic tumor with a poor prognosis, which needs early diagnosis urgently. The gut microbiota has been shown to play a crucial role in the progression of liver cancer. Here, we explored a gut microbiota model covering genera Burkholderia-Caballeronia-Paraburkholderia, Faecalibacterium, and Ruminococcus_1 (B-F-R) for CCA early diagnosis. A case-control study was conducted to enroll 53 CCA patients, 47 cholelithiasis patients, and 40 healthy controls. The feces samples and clinical information of participants were collected in the same period. The gut microbiota and its diversity of individuals were accessed with 16S rDNA sequencing, and the gut microbiota profile was evaluated according to microbiota diversity. Finally, four enriched genera in the CCA group (genera Bacteroides, Muribaculaceae_unclassified, Muribaculum, and Alistipes) and eight enriched genera in the cholelithiasis group (genera Bifidobacterium, Streptococcus, Agathobacter, Ruminococcus_gnavus_group, Faecalibacterium, Subdoligranulum, Collinsella, Escherichia-Shigella) constitute an overall different microbial community composition (P = 0.001). The B-F-R genera model with better diagnostic value than carbohydrate antigen 19-9 (CA19-9) was identified by random forest and Statistical Analysis of Metagenomic Profiles (STAMP) to distinguish CCA patients from healthy controls [area under the curve (AUC) = 0.973, 95% CI = 0.932–1.0]. Moreover, the correlative analysis found that genera Burkholderia-Caballeronia-Paraburkholderia were positively correlated with body mass index (BMI). The significantly different microbiomes between cholelithiasis and CCA were found via principal coordinates analysis (PCoA) and linear discriminant analysis effect size (LEfSe), and Venn diagram and LEfSe were utilized to identify four genera by comparing microbial compositions among patients with malignant obstructive jaundice (MOJ-Y) or not (MOJ-N). In brief, our findings suggest that gut microbiota vary from benign and malignant hepatobiliary diseases to healthy people and provide evidence supporting gut microbiota to be a non-invasive biomarker for the early diagnosis of CCA.
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Affiliation(s)
- Tan Zhang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Sina Zhang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Chen Jin
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Zixia Lin
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Tuo Deng
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaozai Xie
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Liming Deng
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xueyan Li
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jun Ma
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Xiwei Ding
- Department of Gastroenterology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yaming Liu
- Department of Gastroenterology, Zhongshan Hospital Xiamen University, Xiamen, China
| | - Yunfeng Shan
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhengping Yu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yi Wang
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Gang Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jialiang Li
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Shen X, Zhao H, Jin X, Chen J, Yu Z, Ramen K, Zheng X, Wu X, Shan Y, Bai J, Zhang Q, Zeng Q. Development and validation of a machine learning-based nomogram for prediction of intrahepatic cholangiocarcinoma in patients with intrahepatic lithiasis. Hepatobiliary Surg Nutr 2021; 10:749-765. [PMID: 35004943 DOI: 10.21037/hbsn-20-332] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 03/18/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Accurate diagnosis of intrahepatic cholangiocarcinoma (ICC) caused by intrahepatic lithiasis (IHL) is crucial for timely and effective surgical intervention. The aim of the present study was to develop a nomogram to identify ICC associated with IHL (IHL-ICC). METHODS The study included 2,269 patients with IHL, who received pathological diagnosis after hepatectomy or diagnostic biopsy. Machine learning algorithms including Lasso regression and random forest were used to identify important features out of the available features. Univariate and multivariate logistic regression analyses were used to reconfirm the features and develop the nomogram. The nomogram was externally validated in two independent cohorts. RESULTS The seven potential predictors were revealed for IHL-ICC, including age, abdominal pain, vomiting, comprehensive radiological diagnosis, alkaline phosphatase (ALK), carcinoembryonic antigen (CEA), and cancer antigen (CA) 19-9. The optimal cutoff value was 2.05 µg/L for serum CEA and 133.65 U/mL for serum CA 19-9. The accuracy of the nomogram in predicting ICC was 82.6%. The area under the curve (AUC) of nomogram in training cohort was 0.867. The AUC for the validation set was 0.881 from The Second Affiliated Hospital of Wenzhou Medical University, and 0.938 from The First Affiliated Hospital of Fujian Medical University. CONCLUSIONS The nomogram holds promise as a novel and accurate tool to predict IHL-ICC, which can identify lesions in IHL in time for hepatectomy or avoid unnecessary surgical resection.
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Affiliation(s)
- Xian Shen
- Department of General Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Huanhu Zhao
- School of Pharmacy, Minzu University of China, Beijing, China
| | - Xing Jin
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fujian, China
| | - Junyu Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhengping Yu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | | | - Xiangwu Zheng
- Radiological Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiuling Wu
- Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yunfeng Shan
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jianling Bai
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Qiyu Zhang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiqiang Zeng
- Department of General Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
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Zhang ZJ, Huang YP, Li XX, Liu ZT, Liu K, Deng XF, Xiong L, Zou H, Wen Y. A Novel Ferroptosis-Related 4-Gene Prognostic Signature for Cholangiocarcinoma and Photodynamic Therapy. Front Oncol 2021; 11:747445. [PMID: 34712611 PMCID: PMC8545875 DOI: 10.3389/fonc.2021.747445] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 09/13/2021] [Indexed: 12/17/2022] Open
Abstract
Cholangiocarcinoma is the second most common malignant tumor in the hepatobiliary system. Compared with data on hepatocellular carcinoma, fewer public data and prognostic-related studies on cholangiocarcinoma are available, and effective prognostic prediction methods for cholangiocarcinoma are lacking. In recent years, ferroptosis has become an important subject of tumor research. Some studies have indicated that ferroptosis plays an important role in hepatobiliary cancers. However, the prediction of the prognostic effect of ferroptosis in patients with cholangiocarcinoma has not been reported. In addition, many reports have described the ability of photodynamic therapy (PDT), a potential therapy for cholangiocarcinoma, to regulate ferroptosis by generating reactive oxygen species (ROS). By constructing ferroptosis scores, the prognoses of patients with cholangiocarcinoma can be effectively predicted, and potential gene targets can be discovered to further enhance the efficacy of PDT. In this study, gene expression profiles and clinical information (TCGA, E-MTAB-6389, and GSE107943) of patients with cholangiocarcinoma were collected and divided into training sets and validation sets. Then, a model of the ferroptosis gene signature was constructed using least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis. Furthermore, through the analysis of RNA-seq data after PDT treatment of cholangiocarcinoma, PDT-sensitive genes were obtained and verified by immunohistochemistry staining and Western blot. The results of this study provide new insight for predicting the prognosis of cholangiocarcinoma and screening target genes that enhance the efficacy of PDT.
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Affiliation(s)
- Zi-Jian Zhang
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yun-Peng Huang
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Xiao-Xue Li
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Zhong-Tao Liu
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Kai Liu
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Xiao-Feng Deng
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Li Xiong
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Heng Zou
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yu Wen
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
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A radiomic-based model of different contrast-enhanced CT phase for differentiate intrahepatic cholangiocarcinoma from inflammatory mass with hepatolithiasis. Abdom Radiol (NY) 2021; 46:3835-3844. [PMID: 33728532 DOI: 10.1007/s00261-021-03027-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 02/17/2021] [Accepted: 02/25/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND Intrahepatic cholangiocarcinoma (ICC) is hard to distinguish from inflammatory mass (IM) complicated with hepatolithiasis in clinical practice preoperatively. This study looked to develop and confirm the radiomics models to make a distinction between ICC with hepatolithiasis from IM and to compare the results of different contrast-enhanced computed tomography (CT) phase. METHODS The models were developed in a training cohort of 110 patients from January 2005 to June 2020. Radiomics features were extracted from both arterial phase and portal venous phase contrast-enhanced computed tomography (CT) scans. The radiomics scores based on radiomics features, were built by logistic regression after using the least absolute shrinkage and selection operator (LASSO) method. The rad-scores of two contrast -enhanced CT phases and clinical features were incorporated into a novel model. The performance of the models were determined by theirs discrimination, calibration, and clinical usefulness. The models were externally validated in 35 consecutive patients. RESULTS The radiomics signature comprised two features in arterial phase (training cohort, AUC = 0.809, sensitivity 0.700, specificity 0.848, and accuracy 0.774;validation cohort, AUC = 0.790, sensitivity 0.714, specificity 0.800, and accuracy 0.757) and three related features in portal venous phase (training cohort, AUC = 0.801, sensitivity 0.800, specificity 0.717, and accuracy 0.759; validation cohort, AUC = 0.830, sensitivity 0.700, specificity 0.750, and accuracy 0.775) showed significant association with ICC in both cohorts (P < 0.05).We also developed a model only based on clinical variables (training cohort, AUC = 0.778, sensitivity 0.567, specificity 0.891, and accuracy 0.729; validation cohort, AUC = 0.788, sensitivity 0.571, specificity 0.950, and accuracy 0.761). The radiomics-based model contained rad-score of two phases and two clinical factors (CEA and CA19-9) showed the best performance (training cohort, AUC = 0.864, sensitivity 0.867, specificity 0.804, and accuracy 0.836; validation cohort, AUC = 0.843, sensitivity 0.643, specificity 0.980, and accuracy 0.821). CONCLUSIONS Our radiomics-based models provided a diagnostic tool for differentiate intrahepatic cholangiocarcinoma (ICC) from inflammatory mass (IM) with hepatolithiasis both in arterial phase and portal venous phase. To go a step further, the diagnostic accuracy will improved by a clinico-radiologic model.
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6
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Xue B, Wu S, Zheng M, Jiang H, Chen J, Jiang Z, Tian T, Tu Y, Zhao H, Shen X, Ramen K, Wu X, Zhang Q, Zeng Q, Zheng X. Development and Validation of a Radiomic-Based Model for Prediction of Intrahepatic Cholangiocarcinoma in Patients With Intrahepatic Lithiasis Complicated by Imagologically Diagnosed Mass. Front Oncol 2021; 10:598253. [PMID: 33489897 PMCID: PMC7817533 DOI: 10.3389/fonc.2020.598253] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 11/11/2020] [Indexed: 12/14/2022] Open
Abstract
Background This study was conducted with the intent to develop and validate a radiomic model capable of predicting intrahepatic cholangiocarcinoma (ICC) in patients with intrahepatic lithiasis (IHL) complicated by imagologically diagnosed mass (IM). Methods A radiomic model was developed in a training cohort of 96 patients with IHL-IM from January 2005 to July 2019. Radiomic characteristics were obtained from arterial-phase computed tomography (CT) scans. The radiomic score (rad-score), based on radiomic features, was built by logistic regression after using the least absolute shrinkage and selection operator (LASSO) method. The rad-score and other independent predictors were incorporated into a novel comprehensive model. The performance of the Model was determined by its discrimination, calibration, and clinical usefulness. This model was externally validated in 35 consecutive patients. Results The rad-score was able to discriminate ICC from IHL in both the training group (AUC 0.829, sensitivity 0.868, specificity 0.635, and accuracy 0.723) and the validation group (AUC 0.879, sensitivity 0.824, specificity 0.778, and accuracy 0.800). Furthermore, the comprehensive model that combined rad-score and clinical features was great in predicting IHL-ICC (AUC 0.902, sensitivity 0.771, specificity 0.923, and accuracy 0.862). Conclusions The radiomic-based model holds promise as a novel and accurate tool for predicting IHL-ICC, which can identify lesions in IHL timely for hepatectomy or avoid unnecessary surgical resection.
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Affiliation(s)
- Beihui Xue
- Radiological Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Sunjie Wu
- Radiological Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Minghua Zheng
- Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Huanchang Jiang
- The First Clinical Medical College of Wenzhou Medical University, Wenzhou, China
| | - Jun Chen
- The First Clinical Medical College of Wenzhou Medical University, Wenzhou, China
| | - Zhenghao Jiang
- The First Clinical Medical College of Wenzhou Medical University, Wenzhou, China
| | - Tian Tian
- The First Clinical Medical College of Wenzhou Medical University, Wenzhou, China
| | - Yifan Tu
- The First Clinical Medical College of Wenzhou Medical University, Wenzhou, China
| | - Huanhu Zhao
- School of Pharmacy, Minzu University of China, Beijing, China
| | - Xian Shen
- The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | | | - Xiuling Wu
- Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiyu Zhang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiqiang Zeng
- The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiangwu Zheng
- Radiological Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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7
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Wang N, Yang J, Lyu J, Liu Q, He H, Liu J, Li L, Ren X, Li Z. A convenient clinical nomogram for predicting the cancer-specific survival of individual patients with small-intestine adenocarcinoma. BMC Cancer 2020; 20:505. [PMID: 32487033 PMCID: PMC7268250 DOI: 10.1186/s12885-020-06971-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 05/18/2020] [Indexed: 02/07/2023] Open
Abstract
Background The objective of this study was to develop a practical nomogram for predicting the cancer-specific survival (CSS) of patients with small-intestine adenocarcinoma. Methods Patients diagnosed with small-intestine adenocarcinoma between 2010 and 2015 were selected for inclusion in this study from the Surveillance, Epidemiology, and End Results (SEER) database. The selected patients were randomly divided into the training and validation cohorts at a ratio of 7:3. The predictors of CSS were identified by applying both forward and backward stepwise selection methods in a Cox regression model. The performance of the nomogram was measured by the concordance index (C-index), the area under receiver operating characteristic curve (AUC), calibration plots, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Results Multivariate Cox regression indicated that factors including age at diagnosis, sex, marital status, insurance status, histology grade, SEER stage, surgery status, T stage, and N stage were independent covariates associated with CSS. These factors were used to construct a predictive model, which was built and virtualized by a nomogram. The C-index of the constructed nomogram was 0.850. The AUC values indicated that the established nomogram displayed better discrimination performance than did the seventh edition of the American Joint Committee on Cancer TNM staging system in predicting CSS. The IDI and NRI also showed that the nomogram exhibited superior performance in both the training and validation cohorts. Furthermore, the calibrated nomogram predicted survival rates that closely corresponded to actual survival rates, while the DCA demonstrated the considerable clinical usefulness of the nomogram. Conclusion We have constructed a nomogram for predicting the CSS of small-intestine adenocarcinoma patients. This prognostic model may improve the ability of clinicians to predict survival in individual patients and provide them with treatment recommendations.
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Affiliation(s)
- Na Wang
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China.,School of Nursing and Health, Henan University, Kaifeng, Henan, China
| | - Jin Yang
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China.,School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Qingqing Liu
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Hairong He
- Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Jie Liu
- School of Nursing and Health, Henan University, Kaifeng, Henan, China
| | - Li Li
- School of Nursing and Health, Henan University, Kaifeng, Henan, China
| | - Xuequn Ren
- Center for Evidence-Based Medicine and Clinical Research, Huaihe Hospital of Henan University, Kaifeng, Henan, China. .,Department of General Surgery, Huaihe Hospital of Henan University, Kaifeng, Henan, China.
| | - Zhendong Li
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China.
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8
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Wu Q, Wang WJ, Huang YQ, Fang SY, Guan YJ. Nomograms for estimating survival in patients with liver-only colorectal metastases: A retrospective study. Int J Surg 2018; 60:1-8. [PMID: 30366096 DOI: 10.1016/j.ijsu.2018.10.032] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 10/10/2018] [Accepted: 10/15/2018] [Indexed: 11/20/2022]
Abstract
BACKGROUND The aim of this study was to develop and validate nomograms for individual risk prediction in patients with liver-only colorectal metastases (CRLM). METHODS Histologically confirmed CRLM diagnosed between 2010 and 2015 were analysed from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate analyses were used to obtain independent prognostic factors to build nomograms for predicting 1- and 3-year overall survival (OS) and cancer-specific survival (CSS). The predictive accuracy of the nomogram was determined by concordance index (C-index) and calibration plots. RESULTS A total of 9615 patients with CRLM were included in the study. A nomogram predicting OS was constructed according to 9 independent clinicopathological factors. A nomogram predicting CSS was constructed based on the same 9 factors. The C-indexes of the nomograms were significantly better than the TNM staging system (7th edition) in both sets for predicting both OS and CSS. The calibration plots displayed an optimal agreement between the predictive results and the actual observed outcomes. CONCLUSIONS The proposed nomograms can help clinicians calculate the probability in patients with CRLM.
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Affiliation(s)
- Qiong Wu
- Department of Intervention and Vascular Surgery, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou Cancer Medical Center, Suzhou, Jiangsu, 215001, China
| | - Wen-Jie Wang
- Department of Radiation Oncology, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou Cancer Medical Center, Suzhou, Jiangsu, 215001, China
| | - Yue-Qing Huang
- Department of General Practice, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou Cancer Medical Center, Suzhou, Jiangsu, 215001, China
| | - Shi-Ying Fang
- Department of General Surgery, West Anhui Health Vocational College, Luan, Anhui, 237000, China
| | - Yong-Jun Guan
- Department of General Surgery, Yan Da International Hospital, Langfang, Hebei, 065000, China.
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