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Wang P, Tian Y, Du Y, Zhong Y. Intraoperative assessment of anastomotic blood supply using indocyanine green fluorescence imaging following esophagojejunostomy or esophagogastrostomy for gastric cancer. Front Oncol 2024; 14:1341900. [PMID: 38304873 PMCID: PMC10833224 DOI: 10.3389/fonc.2024.1341900] [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: 11/21/2023] [Accepted: 01/02/2024] [Indexed: 02/03/2024] Open
Abstract
Objective This retrospective study aimed to evaluate the feasibility and safety of intraoperative assessment of anastomotic blood supply in patients undergoing esophagojejunostomy or esophagogastrostomy for gastric cancer using Indocyanine Green Fluorescence Imaging (IGFI). Materials and methods From January 2019 to October 2021, we conducted a retrospective analysis of patients who had undergone laparoscopic gastrectomy for the treatment of gastric cancer. The patients were consecutively enrolled and categorized into two study groups: the Indocyanine Green Fluorescence Imaging (IGFI) group consisting of 86 patients, and the control group comprising 92 patients. In the IGFI group, intravenous administration of Indocyanine Green (ICG) was performed, and we utilized a fluorescence camera system to assess anastomotic blood supply both before and after the anastomosis. Results The demographic characteristics of patients in both groups were found to be comparable. In the IGFI group, the mean time to observe perfusion fluorescence was 26.3 ± 12.0 seconds post-ICG injection, and six patients needed to select a more proximal resection point due to insufficient fluorescence at their initial site of choice. Notably, the IGFI group exhibited a lower incidence of postoperative anastomotic leakage, with no significant disparities observed in terms of pathological outcomes, postoperative recovery, or other postoperative complication rates when compared to the control group (p > 0.05). Conclusion This study underscores the potential of IGFI as a dependable and pragmatic tool for the assessment of anastomotic blood supply following esophagojejunostomy or esophagogastrostomy for gastric cancer. The use of IGFI may potentially reduce the occurrence of postoperative anastomotic leakage.
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Affiliation(s)
| | | | - Yongxing Du
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuxin Zhong
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Feng Y, Gong J, Hu T, Liu Z, Sun Y, Tong T. Radiomics for predicting survival in patients with locally advanced rectal cancer: a systematic review and meta-analysis. Quant Imaging Med Surg 2023; 13:8395-8412. [PMID: 38106286 PMCID: PMC10722083 DOI: 10.21037/qims-23-692] [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: 05/19/2023] [Accepted: 09/27/2023] [Indexed: 12/19/2023]
Abstract
Background Radiomics has recently received considerable research attention for providing potential prognostic biomarkers for locally advanced rectal cancer (LARC). We aimed to comprehensively evaluate the methodological quality and prognostic prediction value of radiomic studies for predicting survival outcomes in patients with LARC. Methods The Cochrane, Embase, Medline, and Web of Science databases were searched. The radiomics quality score (RQS), Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist, the Image Biomarkers Standardization Initiative (IBSI) guideline, and the Prediction Model Risk of Bias Assessment Tool were used to assess the quality of the selected studies. A further meta-analysis of hazard ratio (HR) regarding disease-free survival (DFS) and overall survival (OS) was performed. Results Among the 358 studies reported, 15 studies were selected for our review. The mean RQS score was 7.73±4.61 (21.5% of the ideal score of 36). The overall TRIPOD adherence rate was 64.4% (251/390). Most of the included studies (60%) were assessed as having a high risk of bias (ROB) overall. The pooled estimates of the HRs were 3.14 [95% confidence interval (CI): 2.12-4.64, P<0.01] for DFS and 3.36 (95% CI: 1.74-6.49, P<0.01) for OS. Conclusions Radiomics has potential to noninvasively predict outcome in patients with LARC. However, the overall methodological quality of radiomics studies was low, and the adherence to the TRIPOD statement was moderate. Future radiomics research should put a greater focus on enhancing the methodological quality and considering the influence of higher-order features on reproducibility in radiomics.
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Affiliation(s)
- Yaru Feng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Tingdan Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zonglin Liu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yiqun Sun
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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Knewitz D, Almerey T, Gabriel E. A narrative review of prognostic indices in the evaluation of gastrointestinal cancers. J Gastrointest Oncol 2023; 14:1849-1855. [PMID: 37720450 PMCID: PMC10502552 DOI: 10.21037/jgo-23-159] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/26/2023] [Indexed: 09/19/2023] Open
Abstract
Background and Objective Accurate cancer prognostication allows for conscious decision-making. There is a need for precise indices, along with predictive biomarkers, which aid cancer prognostication. We sought to conduct an overview of the current state of prognostic indices and biomarkers in the evaluation of gastrointestinal (GI) cancers, specifically esophageal, colon and rectal. Methods We conducted a comprehensive review of articles in the PubMed database between September 2001 and February 2022. Only articles written in English were included. We reviewed retrospective analyses and prospective observational studies. Key Content and Findings Nomograms are well-described tools that provide estimates of specific cancer-related events, such as overall survival (OS). They are also useful in unroofing specific patient-related variables, which may be associated with cancer survival. Certain prognostic indices have been tested against each other with the goal of discerning superiority. Finally, specific biomarkers have emerged as promising prognostic indicators. Conclusions Nomograms play a significant role in the prognostication of GI cancer. The identification of specific biomarkers in cancer prognostication is evolving. As we embark on the era of precision medicine, further investigation of reliable prognostic indices and biomarkers is needed.
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Affiliation(s)
| | | | - Emmanuel Gabriel
- Mayo Clinic, Jacksonville, FL, USA
- Department of Surgical Oncology, Mayo Clinic, Jacksonville, FL, USA
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Xie Y, Han J, Yu W, Hou Z, Wan Z. A Nomogram Model for Mortality Risk Prediction in Pulmonary Tuberculosis Patients Subjected to Directly Observed Treatment Shortcourse (DOTS). Can Respir J 2022; 2022:1449751. [PMID: 36567966 DOI: 10.1155/2022/1449751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 11/23/2022] [Accepted: 12/06/2022] [Indexed: 12/23/2022] Open
Abstract
We analyzed the risk factors of mortality for patients with pulmonary tuberculosis under the Directly Observed Treatment Shortcourse (DOTS) and established a predictive nomogram for the risk of mortality. The retrospective cohort analysis was conducted on the treatment outcomes of 11207 tuberculosis patients in the tuberculosis management information system in Tianjin from 2014 to 2019. Based on the multivariable unconditional logistic regression, we analyzed the risk factors of mortality in patients with pulmonary TB and established the death risk prediction nomogram. We further applied cross-validation and the receiver operating characteristic (ROC) curve to explore the efficiency of the nomogram. There were 10,697 patients in the survival group and 510 in the mortality group who had successfully initiated DOTS, and the mortality rate was 4.55%. Multivariable logistic regression analysis showed that age, male, relapse cases, first sputum positivity, patient delay, and HIV-positive were independent risk factors for pulmonary TB death. The calibration curve shows that the average absolute error between the predicted mortality risk and the actual death risk is 0.003. The ROC curve shows that the area under the curve where the line-up model predicts the risk of death is 0.816 (95% CI: 0.799∼0.832). The nomogram model based on independent risk factors of mortality in TB patients shows good discrimination and accuracy, with potentially high clinical value in screening patients with a high risk of death, which could be useful for setting the interventional strategies in patients with tuberculosis who had successfully initiated DOTS.
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Lu JH, Tong GX, Hu XY, Guo RF, Wang S. Construction and Evaluation of a Nomogram to Predict Gallstone Disease Based on Body Composition. Int J Gen Med 2022; 15:5947-5956. [PMID: 35811775 PMCID: PMC9258801 DOI: 10.2147/ijgm.s367642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 06/22/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- Jian-hui Lu
- Department of Clinical Nutrition Center, Inner Mongolia People’s Hospital, Hohhot, Inner Mongolia, People’s Republic of China
| | - Gen-xi Tong
- Department of Hepatobiliary, Pancreatic and Spleen Surgery, Inner Mongolia People’s Hospital, Hohhot, Inner Mongolia, People’s Republic of China
| | - Xiang-yun Hu
- Department of Hepatobiliary, Pancreatic and Spleen Surgery, Inner Mongolia People’s Hospital, Hohhot, Inner Mongolia, People’s Republic of China
| | - Rui-fang Guo
- Department of Clinical Nutrition Center, Inner Mongolia People’s Hospital, Hohhot, Inner Mongolia, People’s Republic of China
- Correspondence: Rui-fang Guo, Department of Clinical Nutrition Center, Inner Mongolia People’s Hospital, Hohhot, Inner Mongolia, People’s Republic of China, Email
| | - Shi Wang
- Department of Hepatobiliary, Pancreatic and Spleen Surgery, Inner Mongolia People’s Hospital, Hohhot, Inner Mongolia, People’s Republic of China
- Shi Wang, Department of Hepatobiliary, Pancreatic and Spleen Surgery; Inner Mongolia People’s Hospital, Hohhot, Inner Mongolia, People’s Republic of China, Email
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Zheng H, Li Z, Zheng S, Li J, Yang J, Zhao E. A New Nomogram for Predicting the Postoperative Overall Survival in Patients with Middle-Aged and Elderly Rectal Cancer: A Single Center Retrospective Study in Chinese Population. Int J Gen Med 2022; 15:5197-5209. [PMID: 35651674 PMCID: PMC9150496 DOI: 10.2147/ijgm.s365947] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/19/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose Patients with middle-aged and elderly rectal cancer (MERC) usually have poor prognosis after surgery. This study aimed to develop a nomogram to achieve individualized prediction of overall survival (OS) in patients with MERC and to guide follow-up and subsequent diagnosis and treatment plans. Patients and Methods A total of 349 patients were randomly assigned to the training and validation cohorts in a 7:3 ratio. Multivariate Cox regression analysis was performed using the results of univariate Cox regression analysis to confirm independent prognostic factors of OS. Thereafter, the nomogram was built using the “rms” package. Subsequently, discriminative ability and calibration of the nomogram were evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Integrated discrimination improvement (IDI), net reclassification improvement (NRI), and the area under the ROC curves (AUC) were compared between the nomogram and the tumor-node-metastasis (TNM) staging system (8th edition). Finally, we established a predictive model to assess the survival benefit of patients with MERC by calculating nomogram scores for each patient. Results Six variables were identified as independent prognostic factors and included in the nomogram: smoking history, family history, hematochezia, tumor size, N stage, and M stage. Based on these factors, we successfully constructed a nomogram and evaluated its discriminative and predictive abilities using ROC curves, calibration curves, and DCA. ROC curves, IDI, and NRI showed that the nomogram had outstanding clinical utility compared with the TNM staging system (8th edition) for OS prediction. The predictive model successfully distinguished between high-, medium-, and low-risk MERC patients. Conclusion Our nomogram provided a more satisfactory survival prediction ability than the TNM staging system (8th edition) for MERC patients. In addition, the nomogram was able to accurately categorize patients into different risk groups after surgery.
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Affiliation(s)
- Honghong Zheng
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Chengde Medical University, Chengde, 067000, People’s Republic of China
| | - Zhehong Li
- Department of Orthopedic, The Affiliated Hospital of Chengde Medical University, Chengde, 067000, People’s Republic of China
| | - Shuai Zheng
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Chengde Medical University, Chengde, 067000, People’s Republic of China
| | - Jianjun Li
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Chengde Medical University, Chengde, 067000, People’s Republic of China
| | - Ji Yang
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Chengde Medical University, Chengde, 067000, People’s Republic of China
| | - Enhong Zhao
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Chengde Medical University, Chengde, 067000, People’s Republic of China
- Correspondence: Enhong Zhao, The Affiliated Hospital of Chengde Medical University, No. 36 Nanyingzi St., Chengde, 067000, People’s Republic of China, Email
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Gui S, Lan M, Wang C, Nie S, Fan B. Application Value of Radiomic Nomogram in the Differential Diagnosis of Prostate Cancer and Hyperplasia. Front Oncol 2022; 12:859625. [PMID: 35494065 PMCID: PMC9047828 DOI: 10.3389/fonc.2022.859625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/17/2022] [Indexed: 12/12/2022] Open
Abstract
Objective Prostate cancer and hyperplasia require different treatment strategies and have completely different outcomes; thus, preoperative identification of prostate cancer and hyperplasia is very important. The purpose of this study was to evaluate the application value of magnetic resonance imaging (MRI)-derived radiomic nomogram based on T2-weighted images (T2WI) in differentiating prostate cancer and hyperplasia. Materials and Methods One hundred forty-six patients (66 cases of prostate cancer and 80 cases of prostate hyperplasia) who were confirmed by surgical pathology between September 2019 and September 2019 were selected. We manually delineated T2WI of all patients using ITK-SNAP software and radiomic analysis using Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. Subsequently, the effective features were selected using the LASSO algorithm, and the radiomic feature model was constructed. Next, combined with independent clinical risk factors, a multivariate Logistic regression model was used to establish a radiomic nomogram. The receiver operator characteristic (ROC) curve was used to evaluate the prediction performance of the radiomic nomogram. Finally, the clinical application value of the nomogram was evaluated by decision curve analysis. Results The PSA and the selected imaging features were significantly correlated with the differential diagnosis of prostate cancer and hyperplasia. The radiomic model had good discrimination efficiency for prostate cancer and hyperplasia. The training set (AUC = 0.85; 95% CI: 0.77–0.92) and testing set (AUC = 0.84; 95% CI: 0.72–0.96) were effective. The radiomic nomogram, combined with the radiomic characteristics of MRI and independent clinical risk factors, showed better differentiation efficiency in the training set (AUC = 0.91; 95% CI: 0.85–0.97) and testing set (AUC = 0.90; 95% CI: 0.81–0.99). The decision curve showed the clinical application value of the radiomic nomogram. Conclusion The radiomic nomogram of T2-MRI combined with clinical risk factors can easily identify prostate cancer and hyperplasia. It also provides suggestions for further clinical events.
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Affiliation(s)
- Shaogao Gui
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Min Lan
- Department of Orthopedics, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Chaoxiong Wang
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Si Nie
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
- *Correspondence: Si Nie, ; Bing Fan,
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
- *Correspondence: Si Nie, ; Bing Fan,
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Chen S, Tang Y, Li N, Jiang J, Jiang L, Chen B, Fang H, Qi S, Hao J, Lu N, Wang S, Song Y, Liu Y, Li Y, Jin J. Development and Validation of an MRI-Based Nomogram Model for Predicting Disease-Free Survival in Locally Advanced Rectal Cancer Treated With Neoadjuvant Radiotherapy. Front Oncol 2021; 11:784156. [PMID: 34869040 PMCID: PMC8634258 DOI: 10.3389/fonc.2021.784156] [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: 09/27/2021] [Accepted: 10/27/2021] [Indexed: 11/17/2022] Open
Abstract
Objectives To develop a prognostic prediction MRI-based nomogram model for locally advanced rectal cancer (LARC) treated with neoadjuvant therapy. Methods This was a retrospective analysis of 233 LARC (MRI-T stage 3-4 (mrT) and/or MRI-N stage 1-2 (mrN), M0) patients who had undergone neoadjuvant radiotherapy and total mesorectal excision (TME) surgery with baseline MRI and operative pathology assessments at our institution from March 2015 to March 2018. The patients were sequentially allocated to training and validation cohorts at a ratio of 4:3 based on the image examination date. A nomogram model was developed based on the univariate logistic regression analysis and multivariable Cox regression analysis results of the training cohort for disease-free survival (DFS). To evaluate the clinical usefulness of the nomogram, Harrell’s concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) were conducted in both cohorts. Results The median follow-up times were 43.2 months (13.3–61.3 months) and 32.0 months (12.3–39.5 months) in the training and validation cohorts. Multivariate Cox regression analysis identified MRI-detected extramural vascular invasion (mrEMVI), pathological T stage (ypT) and perineural invasion (PNI) as independent predictors. Lymphovascular invasion (LVI) (which almost reached statistical significance in multivariate regression analysis) and three other independent predictors were included in the nomogram model. The nomogram showed the best predictive ability for DFS (C-index: 0.769 (training cohort) and 0.776 (validation cohort)). It had a good 3-year DFS predictive capacity [area under the curve, AUC=0.843 (training cohort) and 0.771 (validation cohort)]. DCA revealed that the use of the nomogram model was associated with benefits for the prediction of 3-year DFS in both cohorts. Conclusion We developed and validated a novel nomogram model based on MRI factors and pathological factors for predicting DFS in LARC treated with neoadjuvant therapy. This model has good predictive value for prognosis, which could improve the risk stratification and individual treatment of LARC patients.
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Affiliation(s)
- Silin Chen
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuan Tang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ning Li
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Jun Jiang
- Department of Imaging, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Liming Jiang
- Department of Imaging, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Chen
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hui Fang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shunan Qi
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jing Hao
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ningning Lu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shulian Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yongwen Song
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yueping Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yexiong Li
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jing Jin
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
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Zheng Z, Wang X, Liu Z, Lu X, Huang Y, Chi P. Individualized conditional survival nomograms for patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy and radical surgery. Eur J Surg Oncol 2021; 47:3175-3181. [PMID: 34120806 DOI: 10.1016/j.ejso.2021.06.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/08/2021] [Accepted: 06/03/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Conditional survival (CS) considers the time already survived after surgery when estimating the survival probability, which may provide further useful prognostic information. OBJECTIVE To evaluate CS in patients with locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT) and to create CS nomograms predicting the conditional probability of survival after proctectomy. METHODS Consecutive patients with LARC who received nCRT followed by radical resection between 2011 and 2016 were identified. CS was defined as the probability of surviving y years after already surviving for x years. The formula used for CS was CS(x|y) = S(x + y)/S(x), where S(x) represents the survival at x years. Nomograms were constructed to predict the 5-year conditional overall survival (cOS) and conditional recurrence-free survival (cRFS). RESULTS A total of 785 patients were included. The median follow-up time was 65.5 months. The probability of achieving 5-year survival after surgery for cancer increases with additional survival time. Maximum tumor diameter, distance from the anal verge, preoperative CA19-9 level, ypTNM stage and perineural invasion were independent predictors of OS, while maximum tumor diameter, distance from the anal verge, ypTNM stage and perineural invasion were independent risk factors for RFS. The nomograms predicted 5-year cOS and cRFS using these predictors and the time already survived. The online calculator can be accessed at http://www.rectalcancer.top/webcalculator. CONCLUSION The proposed nomograms predict survival in patients after surgery, taking the time already survived into account.
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Affiliation(s)
- Zhifang Zheng
- Department of Colorectal Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Xiaojie Wang
- Department of Colorectal Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Zhun Liu
- Department of Colorectal Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Xingrong Lu
- Department of Colorectal Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Ying Huang
- Department of Colorectal Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
| | - Pan Chi
- Department of Colorectal Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
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Song Z, Zhou Y, Bai X, Zhang D. A Practical Nomogram to Predict Early Death in Advanced Epithelial Ovarian Cancer. Front Oncol 2021; 11:655826. [PMID: 33816311 PMCID: PMC8017286 DOI: 10.3389/fonc.2021.655826] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 01/19/2021] [Accepted: 02/22/2021] [Indexed: 01/14/2023] Open
Abstract
Background: Ovarian cancer is a common gynecological malignancy, most of which is epithelial ovarian cancer (EOC). Advanced EOC is linked with a higher incidence of premature death. To date, no effective prognostic tools are available to evaluate the possibility of early death in patients with advanced EOC. Methods: Advanced (FIGO stage III and IV) EOC patients who were enrolled in the Surveillance, Epidemiology, and End Results database between 2004 and 2015 were regarded as subjects and studied. We aimed to construct a nomogram that can deliver early death prognosis in patients with advanced EOC by identifying crucial independent factors using univariate and multivariate logistic regression analyses to help deliver accurate prognoses. Results: In total, 13,403 patients with advanced EOC were included in this study. Three hundred ninety-seven out of a total of 9,379 FIGO stage III patients died early. There were 4,024 patients with FIGO stage IV, 414 of whom died early. Nomograms based on independent prognostic factors have the satisfactory predictive capability and clinical pragmatism. The internal validation feature of the nomogram demonstrated a high level of accuracy of the predicted death. Conclusions: By analyzing data from a large cohort, a clinically convenient nomogram was established to predict premature death in advanced EOC. This tool can aid clinicians in screening patients who are at higher risk for tailoring treatment plans.
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Affiliation(s)
- Zixuan Song
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yangzi Zhou
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xue Bai
- Department of Health Management, Shengjing Hospital of China Medical University, Shenyang, China
| | - Dandan Zhang
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
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