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Wang J, Huang M, Shen J, Li B, Wu Y, Xie W, Xiao H, Tan L. Development and external validation of a prognosis model to predict outcomes after curative resection of early-stage intrahepatic cholangiocarcinoma. Front Surg 2023; 10:1102871. [PMID: 36969753 PMCID: PMC10030709 DOI: 10.3389/fsurg.2023.1102871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 02/20/2023] [Indexed: 03/10/2023] Open
Abstract
BackgroundEarly-stage intrahepatic cholangiocarcinoma (ESICC) with curative resection and lymph node-negative still has the risk of poor prognosis, and there lacks prognosis-assessing tools for these patients. The objective of this study was to develop a prognosis model to predict outcomes and identify risk stratification for ESICC after resection.MethodsTotally 263 patients with ESICC after hepatectomy from January 2012 to January 2022 were analyzed. Clinicopathological factors were selected using multivariable Cox regression analysis and a prognosis model was developed. The performance of the model was evaluated by concordance index (C-index), calibration plots, decision curves analysis (DCA), and net reclassification index (NRI). Kaplan-Meier curves were analyzed for risk stratification of overall survival (OS) and recurrence-free survival (RFS) based on the prognosis model.ResultsThe clinicopathological features that were independently associated with OS of ESICC included carbohydrate antigen19-9, carcinoembryonic antigen, tumor size, tumor differentiation, and T stage. The prognosis model based on these prognostic factors demonstrated excellent discriminatory performance in both derivation cohort (C-index, 0.71) and external validation cohort (C-index, 0.78), which outperformed the TNM staging system (C-index, 0.59) and individual prognostic factors (all C-index < 0.7). Calibration plots, DCA and NRI also showed superior predictive performance. According to the risk for survival, the model stratified patients into low risk (median OS, 66.6 months; median RFS, 24.3 months) and high risk (median OS, 24.0 months; median RFS, 6.4 months) (P < 0.001).ConclusionsOur prognosis model can robustly predict the outcomes of ESICC after curative resection and provide precise evaluation on prognosis risk, facilitating clinicians to develop individualized postoperative treatment options.
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Affiliation(s)
- Jianping Wang
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Manling Huang
- Department of Oncology, Cancer Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jingxian Shen
- Department of Medical Imaging, StateKey Laboratory of Oncology in Southern China, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Bin Li
- Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yanqing Wu
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Wenxuan Xie
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Han Xiao
- Department of Medical Ultrasonics, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Correspondence: Li Tan Han Xiao
| | - Li Tan
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Correspondence: Li Tan Han Xiao
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Xie ZH, Shi X, Liu MQ, Wang J, Yu Y, Zhang JX, Chu KJ, Li W, Ge RL, Cheng QB, Jiang XQ. Development and validation of a nomogram to predict overall survival in patients with incidental gallbladder cancer: A retrospective cohort study. Front Oncol 2023; 12:1007374. [PMID: 36761430 PMCID: PMC9902907 DOI: 10.3389/fonc.2022.1007374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 12/28/2022] [Indexed: 01/25/2023] Open
Abstract
Objective The aim of this study was to develop and validate a nomogram to predict the overall survival of incidental gallbladder cancer. Methods A total of 383 eligible patients with incidental gallbladder cancer diagnosed in Shanghai Eastern Hepatobiliary Surgery Hospital from 2011 to 2021 were retrospectively included. They were randomly divided into a training cohort (70%) and a validation cohort (30%). Univariate and multivariate analyses and the Akaike information criterion were used to identify variables independently associated with overall survival. A Cox proportional hazards model was used to construct the nomogram. The C-index, area under time-dependent receiver operating characteristic curves and calibration curves were used to evaluate the discrimination and calibration of the nomogram. Results T stage, N metastasis, peritoneal metastasis, reresection and histology were independent prognostic factors for overall survival. Based on these predictors, a nomogram was successfully established. The C-index of the nomogram in the training cohort and validation cohort was 0.76 and 0.814, respectively. The AUCs of the nomogram in the training cohort were 0.8, 0.819 and 0.815 for predicting OS at 1, 3 and 5 years, respectively, while the AUCs of the nomogram in the validation cohort were 0.846, 0.845 and 0.902 for predicting OS at 1, 3 and 5 years, respectively. Compared with the 8th AJCC staging system, the AUCs of the nomogram in the present study showed a better discriminative ability. Calibration curves for the training and validation cohorts showed excellent agreement between the predicted and observed outcomes at 1, 3 and 5 years. Conclusions The nomogram in this study showed excellent discrimination and calibration in predicting overall survival in patients with incidental gallbladder cancer. It is useful for physicians to obtain accurate long-term survival information and to help them make optimal treatment and follow-up decisions.
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Affiliation(s)
- Zhi-Hua Xie
- Department I of Biliary Tract Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China
| | - Xuebing Shi
- Department I of Biliary Tract Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China
| | - Ming-Qi Liu
- Department I of Biliary Tract Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China
| | - Jinghan Wang
- Department of Hepatopancreatobiliary Surgery, East Hospital, Tongji University, Shanghai, China
| | - Yong Yu
- Department I of Biliary Tract Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China
| | - Ji-Xiang Zhang
- Department I of Biliary Tract Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China
| | - Kai-Jian Chu
- Department I of Biliary Tract Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China
| | - Wei Li
- Department I of Biliary Tract Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China
| | - Rui-Liang Ge
- Department I of Biliary Tract Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China
| | - Qing-Bao Cheng
- Department I of Biliary Tract Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China,*Correspondenc: Xiao-Qing Jiang, ; Qing-Bao Cheng,
| | - Xiao-Qing Jiang
- Department I of Biliary Tract Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China,*Correspondenc: Xiao-Qing Jiang, ; Qing-Bao Cheng,
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Xiang F, Liang X, Yang L, Liu X, Yan S. Contrast-enhanced CT radiomics for prediction of recurrence-free survival in gallbladder carcinoma after surgical resection. Eur Radiol 2022. [PMID: 35612664 DOI: 10.1007/s00330-022-08858-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/11/2022] [Accepted: 04/30/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES Gallbladder carcinoma (GBC) is the most common and aggressive biliary tract malignancy with high postoperative recurrence rates. This single-center study aimed to develop and validate a radiomics signature to estimate GBC recurrence-free survival (RFS). METHODS This study retrospectively included 204 consecutive patients with pathologically diagnosed GBC and were randomly divided into development (n = 142) and validation (n = 62) cohorts (7:3). The radiomics features of tumor were extracted from preoperative contrast-enhanced CT imaging for each patient. In the development cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression was employed to develop a radiomics signature for RFS prediction. The patients were stratified into high-score or low-score groups according to their median value of radiomics score. A nomogram was established using multivariable Cox regression by incorporating significant pathological predictors and radiomics signatures. RESULTS The radiomics signature based on 12 features could discriminate high-risk patients with poor RFS. Multivariate Cox analysis revealed that pT3/4 stage (hazard ratio, [HR] = 2.691), pN2 stage (HR = 3.60), poor differentiation grade (HR = 2.651), and high radiomics score (HR = 1.482) were independent risk variables associated with worse RFS and were incorporated to construct a nomogram. The nomogram displayed good prediction performance in estimating RFS with AUC values of 0.895, 0.935, and 0.907 at 1, 3, and 5 years, respectively. CONCLUSIONS The radiomics signature and combined nomogram may assist in predicting RFS in GBC patients. KEY POINTS • A radiomics signature extracted from preoperative contrast-enhanced CT can be a useful tool to preoperatively predict RFS of GBC. • T3/T4 stage, N2, poor tumor differentiation, and high radiomics score were positively associated with postoperative recurrence.
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Cao P, Hong H, Yu Z, Chen G, Qi S. A Novel Clinically Prognostic Stratification Based on Prognostic Nutritional Index Status and Histological Grade in Patients With Gallbladder Cancer After Radical Surgery. Front Nutr 2022; 9:850971. [PMID: 35600830 PMCID: PMC9116425 DOI: 10.3389/fnut.2022.850971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose Gallbladder carcinoma (GBC) is the most common malignancy of the biliary tract, with a 5-year survival rate of 5%. The prognostic models to predict the prognosis of patients with GBC remain controversial. Therefore, to construct a prognosis prediction of GBC, a retrospective cohort study was carried out to investigate the prognostic nutritional index and histological grade in the long-term outcome of patients with GBC after radical surgery (RS). Methods A retrospective study of a total of 198 patients with GBC who underwent surgical treatment were enrolled. The hematological indicators, imageological data, and perioperative clinical data were acquired for statistical analysis and poor prognosis model construction. Results Prognostic nutrition index (PNI) < 45.88, maximum tumor diameter (MTD) > 2.24 cm, and jaundice (JD) were all associated with a poor prognosis in multivariate logistic regression analysis. The prognosis prediction model was based on the three risk factors, which indicated a superior predictive ability in the primary cohort [area under the curve (AUC) = 0.951] and validation cohort (AUC = 0.888). In multivariate Cox regression analysis, poorly differentiation (PD) was associated with poor 3-year survival. In addition, Kaplan-Meier (KM) survival analysis suggested that GBC patients with high-risk scores and PD had a better prognosis after RS (p < 0.05), but there was no significant difference in prognosis for patients with non-poorly differentiation (NPD) or low-risk scores after RS (p > 0.05). Conclusion Our prediction model for GBC patients with prognosis evaluation is accurate and effective. For patients with PD and high-risk scores, RS is highly recommended; a simple cholecystectomy can also be considered for acceptance for patients with NPD or low-risk score. The significant findings provide a new therapeutic strategy for the clinical treatment of GBC.
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Affiliation(s)
- Peng Cao
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fujian Medical University Cancer Center, Fuzhou, China
| | - Haijie Hong
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fujian Medical University Cancer Center, Fuzhou, China
| | - Zijian Yu
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Guodong Chen
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Shuo Qi
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
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