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Wan M, Zhang W, Huang H, Fang X, Chen Y, Tian Y, Yao Y, Weng H, Chen Z, Yu L, Tian Y, Huang H, Li X, Hong H, Lin T. Development and validation of a novel prognostic nomogram for advanced diffuse large B cell lymphoma. Clin Exp Med 2024; 24:64. [PMID: 38554186 PMCID: PMC10981611 DOI: 10.1007/s10238-024-01326-y] [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: 11/08/2023] [Accepted: 03/07/2024] [Indexed: 04/01/2024]
Abstract
Advanced diffuse large B cell lymphoma (DLBCL) is a common malignant tumor with aggressive clinical features and poor prognosis. At present, there is lack of effective prognostic tool for patients with advanced (stage III/IV) DLBCL. The aim of this study is to identify prognostic indicators that affect survival and response and establish the first survival prediction nomogram for advanced DLBCL. A total of 402 patients with advanced DLBCL were enrolled in this study. COX multivariate analysis was used to obtain independent prognostic factors. The independent prognostic factors were included in the nomogram, and the nomogram to predict the performance of the model was established by R rms package, C-index (consistency index), AUC curve and calibration curve. The training and validation cohorts included 281 and 121 patients. In the training cohort, multivariate analysis showed that Ki-67 (70% (high expression) vs ≤ 70% (low expression), p < 0.001), LDH (lactate dehydrogenase) (elevated vs normal, p = 0.05), FER (ferritin) (elevated vs normal, p < 0.001), and β2-microglobulin (elevated vs normal, p < 0.001) were independent predictors and the nomogram was constructed. The nomogram showed that there was a significant difference in OS among the low-risk, intermediate-risk and high-risk groups, with 5-year survival rates of 81.6%, 44% and 6%, respectively. The C-index of the nomogram in the training group was 0.76. The internal validation of the training group showed good consistency. In the internal validation cohort of the training group, the AUC was 0.828, and similar results were obtained in the validation group, with a C-index of 0.74 and an AUC of 0.803. The proposed nomogram provided a valuable individualized risk assessment of OS in advanced DLBCL patients.
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Affiliation(s)
- Mengdi Wan
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China
| | - Wei Zhang
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China
| | - He Huang
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China
| | - Xiaojie Fang
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China
| | - Yungchang Chen
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China
| | - Ying Tian
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China
| | - Yuyi Yao
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China
| | - Huawei Weng
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China
| | - Zegeng Chen
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China
| | - Le Yu
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China
| | - Yuke Tian
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China
| | - Huageng Huang
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China
| | - Xudong Li
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China
| | - Huangming Hong
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China.
| | - Tongyu Lin
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China.
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China.
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Maimaiti A, Zhou Y, Wang D, Zhou Z, Pei H, Li Y. Comprehensive survival nomograms for locally advanced gastric cancer: a large population-based real-world study. Transl Cancer Res 2023; 12:2989-3006. [PMID: 38130296 PMCID: PMC10731340 DOI: 10.21037/tcr-22-1255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 05/06/2023] [Indexed: 12/23/2023]
Abstract
Background This study aimed to construct and verify nomograms predicting overall survival (OS) and cancer-specific survival (CSS) for locally advanced gastric cancer (LAGC) based on a therapeutic selection, demographic factors, and pathological features. Methods The data used for the analysis were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were constructed based on the Cox regression model. Results The entire cohort comprised 21,757 patients with histologically confirmed LAGC, and was randomly distributed into training and verification groups at a ratio of 2:1 for building the prognostic predictive model. According to the multivariate analysis, 13 variables [i.e., age, marital status, race, tumor location, pathological grade, histological type, T and N stage, surgery, radiotherapy, chemotherapy, tumor size, and regional nodes examined (RNE)] were confirmed as independent predictors for both OS and CSS. All of the significant variables were used to create the nomograms for OS and CSS. Time-dependent receiver operating characteristic (ROC) curves, a decision curve analysis (DCA), the C-index, and calibration curves were applied to identify the discriminating superiority of the nomograms. Conclusions The nomograms for OS and CSS in LAGC were built and validated based on the therapeutic selection and pathological and demographic variables using a national database. This study aims at helping clinicians make better clinical decisions and encouraging patients receive treatment actively.
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Affiliation(s)
- Aizezi Maimaiti
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China
- The National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, China
| | - Yuan Zhou
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China
- The National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, China
| | - Dan Wang
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China
- The National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, China
| | - Zhongyi Zhou
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China
- The National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, China
| | - Haiping Pei
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China
- The National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, China
| | - Yuqiang Li
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China
- The National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, China
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Ren X, Huang T, Tang X, Ma Q, Zheng Y, Hu Z, Wang Y, Zhou Y. Development and validation of nomogram models to predict radiotherapy or chemotherapy benefit in stage III/IV gastric adenocarcinoma with surgery. Front Oncol 2023; 13:1223857. [PMID: 37655111 PMCID: PMC10466399 DOI: 10.3389/fonc.2023.1223857] [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: 05/16/2023] [Accepted: 07/25/2023] [Indexed: 09/02/2023] Open
Abstract
Objectives The advanced gastric adenocarcinoma (GAC) patients (stage III/IV) with surgery may have inconsistent prognoses due to different demographic and clinicopathological factors. In this retrospective study, we developed clinical prediction models for estimating the overall survival (OS) and cancer-specific survival (CSS) in advanced GAC patients with surgery. Methods A retrospective analysis was conducted using the Surveillance, Epidemiology, and End Results (SEER) database. The total population from 2004 to 2015 was divided into four levels according to age, of which 179 were younger than 45 years old, 695 were 45-59 years old, 1064 were 60-74 years old, and 708 were older than 75 years old. There were 1,712 men and 934 women. Univariate and multivariate Cox regression analyses were performed to identify prognostic factors for OS and CSS. Nomograms were constructed to predict the 1-, 3-, and 5-year OS and CSS. The models' calibration and discrimination efficiency were validated. Discrimination and accuracy were evaluated using the consistency index, area under the receiver operating characteristic curve, and calibration plots; and clinical usefulness was assessed using decision curve analysis. Cross-validation was also conducted to evaluate the accuracy and stability of the models. Prognostic factors identified by Cox regression were analyzed using Kaplan-Meier survival analysis. Results A total of 2,646 patients were included in our OS study. Age, primary site, differentiation grade, AJCC 6th_TNM stage, chemotherapy, radiotherapy, and number of regional nodes examined were identified as prognostic factors for OS in advanced GAC patients with surgery (P < 0.05). A total of 2,369 patients were included in our CSS study. Age, primary site, differentiation grade, AJCC 6th_TNM stage, chemotherapy, radiotherapy, and number of regional nodes examined were identified as risk factors for CSS in these patients (P < 0.05). These factors were used to construct the nomogram to predict the 1-, 3-, and 5-year OS and CSS of advanced GAC patients with surgery. The consistency index and area under the receiver operating characteristic curve demonstrated that the models effectively differentiated between events and nonevents. The calibration plots for 1-, 3-, and 5-year OS and CSS probability showed good consistence between the predicted and the actual events. The decision curve analysis indicated that the nomogram had higher clinical predictive value and more significant net gain than AJCC 6th_TNM stage in predicting OS and CSS of advanced GAC patients with surgery. Cross-validation also revealed good accuracy and stability of the models. Conclusion The developed predictive models provided available prognostic estimates for advanced GAC patients with surgery. Our findings suggested that both OS and CSS can benefit from chemotherapy or radiotherapy in these patients.
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Affiliation(s)
- Xiangqing Ren
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
- Department of Gastroenterology, the First Hospital of Lanzhou University, Lanzhou, China
- Key Laboratory for Gastrointestinal Diseases of Gansu Province, The First Hospital of Lanzhou University, Lanzhou, China
| | - Tian Huang
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
- Department of Gastroenterology, the First Hospital of Lanzhou University, Lanzhou, China
- Key Laboratory for Gastrointestinal Diseases of Gansu Province, The First Hospital of Lanzhou University, Lanzhou, China
| | - Xiaolong Tang
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Qian Ma
- Geriatrics Department, Xianyang First People’s Hospital, Xianyang, China
| | - Ya Zheng
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
- Department of Gastroenterology, the First Hospital of Lanzhou University, Lanzhou, China
- Key Laboratory for Gastrointestinal Diseases of Gansu Province, The First Hospital of Lanzhou University, Lanzhou, China
| | - Zenan Hu
- Department of Gastroenterology, the First Hospital of Lanzhou University, Lanzhou, China
- Key Laboratory for Gastrointestinal Diseases of Gansu Province, The First Hospital of Lanzhou University, Lanzhou, China
| | - Yuping Wang
- Department of Gastroenterology, the First Hospital of Lanzhou University, Lanzhou, China
- Key Laboratory for Gastrointestinal Diseases of Gansu Province, The First Hospital of Lanzhou University, Lanzhou, China
| | - Yongning Zhou
- Department of Gastroenterology, the First Hospital of Lanzhou University, Lanzhou, China
- Key Laboratory for Gastrointestinal Diseases of Gansu Province, The First Hospital of Lanzhou University, Lanzhou, China
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Biondi A, Agnes A, Laurino A, Moretta P, Lorenzon L, D'Ugo D, Persiani R. The definition of "R1" lymph node dissection status in patients undergoing curative-aim gastrectomy for gastric carcinoma: A proof of concept study. Surg Oncol 2023; 48:101908. [PMID: 36906935 DOI: 10.1016/j.suronc.2023.101908] [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: 08/09/2022] [Revised: 01/11/2023] [Accepted: 01/28/2023] [Indexed: 02/04/2023]
Abstract
INTRODUCTION The aim of this study was to define and investigate the prognostic impact of "R1-Lymph-node dissection" during gastrectomy. METHODS This was a retrospective study conducted with 499 patients undergoing curative-aim gastrectomy. We defined R1-Lymph dissection as an involvement of lymph node stations anatomically connected with lymph node stations outside the declared level of dissection (D1 to D2+). The primary outcomes were disease-free and disease-specific survival (DFS and DSS). RESULTS At multivariable analysis, the type of gastrectomy, pT and pN were associated with DFS, and the type of gastrectomy, R1-Margin status, R1-Lymph status, pT, pN and adjuvant therapy were associated with DSS. Moreover, pT and R1-Lymph status were the only factors associated with overall loco-regional recurrence. CONCLUSIONS In this study, we introduced the concept of R1-Lymph-node dissection, which was significantly associated with DSS and appeared to be a stronger prognostic factor for loco-regional recurrence than the R1 status on the resection margin.
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Affiliation(s)
- Alberto Biondi
- Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy
| | - Annamaria Agnes
- Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy.
| | - Antonio Laurino
- Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Pasquale Moretta
- Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Laura Lorenzon
- Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Domenico D'Ugo
- Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy
| | - Roberto Persiani
- Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy
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Hou C, Yin F, Liu Y. Developing and validating nomograms for predicting the survival in patients with clinical local-advanced gastric cancer. Front Oncol 2022; 12:1039498. [PMID: 36387146 PMCID: PMC9644132 DOI: 10.3389/fonc.2022.1039498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 10/14/2022] [Indexed: 12/24/2022] Open
Abstract
Background Many patients with gastric cancer are at a locally advanced stage during initial diagnosis. TNM staging is inaccurate in predicting survival. This study aims to develop two more accurate survival prediction models for patients with locally advanced gastric cancer (LAGC) and guide clinical decision-making. Methods We recruited 2794 patients diagnosed with LAGC (2010–2015) from the Surveillance, Epidemiology, and End Results (SEER) database and performed external validation using data from 115 patients with LAGC at Yantai Affiliated Hospital of Binzhou Medical University. Univariate and multifactorial survival analyses were screened for meaningful independent prognostic factors and were used to build survival prediction models. Concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were evaluated for nomograms. Finally, the differences and relationships of survival and prognosis between the three different risk groups were described using the Kaplan–Meier method. Results Cox proportional risk regression model analysis identified independent prognostic factors for patients with LAGC, and variables associated with overall survival (OS) included age, race, marital status, T-stage, N-stage, grade, histologic type, surgery, and chemotherapy. Variables associated with cancer-specific survival (CSS) included age, race, T-stage, N-stage, grade, histological type, surgery, and chemotherapy. In the training cohort, C-index of nomogram for predicting OS was 0.722 (95% confidence interval [95% CI]: 0.708–0.736] and CSS was 0.728 (95% CI: 0.713–0.743). In the external validation cohort, C-index of nomogram for predicted OS was 0.728 (95% CI:0.672–0.784) and CSS was 0.727 (95% CI:0.668–0.786). The calibration curves showed good concordance between the predicted and actual results. C-index, ROC, and DCA results indicated that our nomograms could more accurately predict OS and CSS than TNM staging and had a higher clinical benefit. Finally, to facilitate clinical use, we set up two web servers based on nomograms. Conclusion The nomograms established in this study have better risk assessment ability than the clinical staging system, which can help clinicians predict the individual survival of LAGC patients more accurately and thus develop appropriate treatment strategies.
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Affiliation(s)
- Chong Hou
- Department of Gastroenterology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
| | - Fangxu Yin
- Department of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China
| | - Yipin Liu
- Department of Gastroenterology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
- *Correspondence: Yipin Liu,
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Sun H, Yan L, Chen H, Zheng T, Zhang Y, Wang H. Development of a nomogram to predict prognosis in ovarian cancer: a SEER-based study. Transl Cancer Res 2020; 9:5829-5842. [PMID: 35117197 PMCID: PMC8799304 DOI: 10.21037/tcr-20-1238] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 09/10/2020] [Indexed: 12/27/2022]
Abstract
Background Ovarian cancer remains the most lethal gynecologic malignancy. In this study, we aimed to identify the specific risk factors affecting overall survival (OS) and develop a nomogram for prognostic prediction of ovarian cancer patients based on data from the Surveillance, Epidemiology, and End Results (SEER) database. Methods Information from the SEER database on ovarian cancer between 2004 and 2016 was screened and retrieved. Cases were randomly divided into the training cohort hand the validation cohort at a 7:3 ratio. The prognostic effects of individual variables on survival were evaluated via Kaplan-Meier method and Cox proportional hazards regression model using data from the training cohort. A nomogram was formulated to predict the 3- and 5-year OS rates of patients with ovarian cancer, and then validated both in the training cohort and the validation cohort. Results A total of 28,375 patients were selected from 75,921 samples (19,862 in training cohort and 8,513 in validation cohort). Cox regression analysis identified race, age laterality, histology, stage, grade, surgery, chemotherapy, radiotherapy, and marital status as independent risk factors for ovarian cancer prognosis. A nomogram was developed based on the results of multivariate analysis and validated using an internal bootstrap resampling approach, which demonstrated a sufficient level of discrimination according to the C-index (0.752, 95% CI: 0.746–0.758 in the training cohort, 0.755, 95% CI: 0.746–0.764). Conclusions We developed a nomogram valuable for accurate prediction of 3- and 5-year OS rates of ovarian cancer patients based on individual characteristics.
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Affiliation(s)
- Huizhen Sun
- Department of Gynecology and Obstetrics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Li Yan
- Department of Radiation Oncology, Eye and ENT Hospital, Fudan University, Shanghai, China
| | - Hainan Chen
- Department of Gynecology and Obstetrics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Zheng
- Department of Gynecology and Obstetrics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yi Zhang
- Department of Assisted Reproduction, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Husheng Wang
- Department of Gynecology and Obstetrics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Wang Y, Zhang J, Guo S, Meng XY, Zheng ZC, Zhao Y. Indications of neoadjuvant chemotherapy for locally advanced Gastric Cancer patients based on pre-treatment clinicalpathological and laboratory parameters. J Cancer 2020; 11:6000-6008. [PMID: 32922540 PMCID: PMC7477425 DOI: 10.7150/jca.46430] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 08/01/2020] [Indexed: 12/27/2022] Open
Abstract
Objective: There are controversial indications for neoadjuvant chemotherapy (NAT) in the treatment of locally advanced gastric cancer (LAGC). Here, we aimed to identify indications for NAT based on pre-treatment clinicopathological and laboratory parameters. Methods: This study included a retrospective cohort of 1083 LAGC patients who had underwent radical D2 gastrectomy in the Cancer Hospital of China Medical University between 2012 and 2016. After propensity score matching, 756 patients were recruited and were separated into NAT (n=378) or primary surgery (PS) (n=378) groups. Cox regression identified pre-treatment risk factors for overall survival (OS). A nomogram was established to predict OS and calculate scores for risk factors. Recursive partitioning analysis (RPA) determined cut off values, where the entire patient cohort was divided into low and high risk groups. Results: Seven risk factors that were significantly related to OS were incorporated in the nomogram. These risk factors included age, tumor size, tumor site, carbohydrate antigen 199 (CA199), carcino-embryonic antigen (CEA), clinical T stage (cT) and clinical N stage (cN). The model contained a C-index of 0.637. The calibration curve revealed anticipated values that were reflective of actual values. The decision curve revealed an achievement of optimal clinical impact when threshold possibility was 0-54%. Next, the cohort was split into low (≤ 252 points) or high (> 252 points) risk groups based on the 5-year OS projected by RPA. The PS group showed a worse OS compared to the NAT group for high-risk patients (P =0.004). There was no significant difference when comparing OS between the PS and NAT groups for low-risk patients (P =0.407). Conclusions: A feasible, quantifiable and practical prognostic tool was generated to screen for potential survival benefits for patients receiving NAT. Surgeons can use this model to identify optimal treatment regimens for individualized treatment strategies during the diagnosis of LAGC patients. For these patients, NAT is suggested for high-risk patients.
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Affiliation(s)
- Yue Wang
- Department of Gastric Cancer, Liaoning Cancer Hospital & Institute (Cancer Hospital of China Medical University), No. 44 Xiaoheyan Road, Dadong District, Shenyang City, Liaoning Province, China
| | - Jun Zhang
- Department of Gastric Cancer, Liaoning Cancer Hospital & Institute (Cancer Hospital of China Medical University), No. 44 Xiaoheyan Road, Dadong District, Shenyang City, Liaoning Province, China
| | - Shuai Guo
- China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning Province, China
| | - Xiang-Yu Meng
- Department of Gastric Cancer, Liaoning Cancer Hospital & Institute (Cancer Hospital of China Medical University), No. 44 Xiaoheyan Road, Dadong District, Shenyang City, Liaoning Province, China
| | - Zhi-Chao Zheng
- Department of Gastric Cancer, Liaoning Cancer Hospital & Institute (Cancer Hospital of China Medical University), No. 44 Xiaoheyan Road, Dadong District, Shenyang City, Liaoning Province, China
| | - Yan Zhao
- Department of Gastric Cancer, Liaoning Cancer Hospital & Institute (Cancer Hospital of China Medical University), No. 44 Xiaoheyan Road, Dadong District, Shenyang City, Liaoning Province, China
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Huang Z, Chen Y, Zhang W, Liu H, Wang Z, Zhang Y. Modified Gastric Cancer AJCC Staging with a Classification Based on the Ratio of Regional Lymph Node Involvement: A Population-Based Cohort Study. Ann Surg Oncol 2020; 27:1480-1487. [DOI: 10.1245/s10434-019-08098-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Indexed: 08/30/2023]
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Vaidya P, Bera K, Gupta A, Wang X, Corredor G, Fu P, Beig N, Prasanna P, Patil PD, Velu PD, Rajiah P, Gilkeson R, Feldman MD, Choi H, Velcheti V, Madabhushi A. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction. LANCET DIGITAL HEALTH 2020; 2:e116-e128. [PMID: 33334576 DOI: 10.1016/s2589-7500(20)30002-9] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 01/02/2020] [Accepted: 01/08/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Use of adjuvant chemotherapy in patients with early-stage lung cancer is controversial because no definite biomarker exists to identify patients who would receive added benefit from it. We aimed to develop and validate a quantitative radiomic risk score (QuRiS) and associated nomogram (QuRNom) for early-stage non-small cell lung cancer (NSCLC) that is prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy following surgery. METHODS We did a retrospective multicohort study of individuals with early-stage NSCLC (stage I and II) who either received surgery alone or surgery plus adjuvant chemotherapy. We selected patients for whom we had available pre-treatment diagnostic CT scans and corresponding survival information. We used radiomic texture features derived from within and outside the primary lung nodule on chest CT scans of patients from the Cleveland Clinic Foundation (Cleveland, OH, USA; cohort D1) to develop QuRiS. A least absolute shrinkage and selection operator-Cox regularisation model was used for data dimension reduction, feature selection, and QuRiS construction. QuRiS was independently validated on a cohort of patients from the University of Pennsylvania (Philadephia, PA, USA; cohort D2) and a cohort of patients whose CT scans were derived from The Cancer Imaging Archive (cohort D3). QuRNom was constructed by integrating QuRiS with tumour and node descriptors (according to the tumour, node, metastasis staging system) and lymphovascular invasion. The primary endpoint of the study was the assessment of the performance of QuRiS and QuRNom in predicting disease-free survival. The added benefit of adjuvant chemotherapy estimated using QuRiS and QuRNom was validated by comparing patients who received adjuvant chemotherapy versus patients who underwent surgery alone in cohorts D1-D3. FINDINGS We included: 329 patients in cohort D1 (73 [22%] had surgery plus adjuvant chemotherapy and 256 (78%) had surgery alone); 114 patients in cohort D2 (33 [29%] had surgery plus adjuvant chemotherapy and 81 (71%) had surgery alone); and 82 patients in cohort D3 (24 [29%] had surgery plus adjuvant chemotherapy and 58 (71%) had surgery alone). QuRiS comprised three intratumoral and 10 peritumoral CT-radiomic features and was found to be significantly associated with disease-free survival (ie, prognostic validation of QuRiS; hazard ratio for predicted high-risk vs predicted low-risk groups 1·56, 95% CI 1·08-2·23, p=0·016 for cohort D1; 2·66, 1·24-5·68, p=0·011 for cohort D2; and 2·67, 1·39-5·11, p=0·0029 for cohort D3). To validate the predictive performance of QuRiS, patients were partitioned into three risk groups (high, intermediate, and low risk) on the basis of their corresponding QuRiS. Patients in the high-risk group were observed to have significantly longer survival with adjuvant chemotherapy than patients who underwent surgery alone (0·27, 0·08-0·95, p=0·042, for cohort D1; 0·08, 0·01-0·42, p=0·0029, for cohorts D2 and D3 combined). As concerns QuRNom, the nomogram-estimated survival benefit was predictive of the actual efficacy of adjuvant chemotherapy (0·25, 0·12-0·55, p<0·0001, for cohort D1; 0·13, <0·01-0·99, p=0·0019 for cohort D3). INTERPRETATION QuRiS and QuRNom were validated as being prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy, especially in clinically defined low-risk groups. Since QuRiS is based on routine chest CT imaging, with additional multisite independent validation it could potentially be employed for decision management in non-invasive treatment of resectable lung cancer. FUNDING National Cancer Institute of the US National Institutes of Health, National Center for Research Resources, US Department of Veterans Affairs Biomedical Laboratory Research and Development Service, Department of Defence, National Institute of Diabetes and Digestive and Kidney Diseases, Wallace H Coulter Foundation, Case Western Reserve University, and Dana Foundation.
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Affiliation(s)
- Pranjal Vaidya
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Amit Gupta
- University Hospitals, Cleveland, OH, USA
| | - Xiangxue Wang
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Germán Corredor
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Pingfu Fu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Niha Beig
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Prateek Prasanna
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | | | | | | | | | - Michael D Feldman
- University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Vamsidhar Velcheti
- NYU Langone- Laura and Isaac Perlmutter Cancer Center, New York, NY, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio, USA.
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Guan E, Tian F, Liu Z. A novel risk score model for stomach adenocarcinoma based on the expression levels of 10 genes. Oncol Lett 2020; 19:1351-1367. [PMID: 31966067 PMCID: PMC6956285 DOI: 10.3892/ol.2019.11190] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 10/11/2019] [Indexed: 12/11/2022] Open
Abstract
Stomach adenocarcinoma (STAD) accounts for 95% of cases of malignant gastric cancer, which is the third leading cause of cancer-associated mortality worldwide. The pathogenesis and effective diagnosis of STAD have become popular topics for research in the previous decade. In the present study, high-throughput RNA sequencing expression profiles and clinical data from patients with STAD were obtained from The Cancer Genome Atlas database and were used as a training dataset to screen differentially expressed genes (DEGs). Prognostic DEGs were identified using univariate Cox regression analysis and were further screened by the least absolute shrinkage and selection operator regularization regression algorithm. The resulting genes were used to construct a risk score model, the validation and effectiveness evaluation of which were performed on an independent dataset downloaded from the Gene Expression Omnibus database. Stratified and functional pathway (gene set enrichment) analyses were performed on groups with different estimated prognosis. A total of 92 genes significantly associated with STAD prognosis were obtained by univariate Cox regression analysis, and 10 prognosis-associated DEGs; hemoglobin b, chromosome 4 open reading frame 48, Dickkopf WNT signaling pathway inhibitor 1, coagulation factor V, serpin family E member 1, transmembrane protein 200A, NADPH oxidase organizer 1, C-X-C motif chemokine ligand 3, mannosidase endo-α-like and tripartite motif-containing 31; were selected for the development of the risk score model. The reliability of this prognostic method was verified using a validation set, and the results of multivariate Cox analysis indicated that the risk score may serve as an independent prognostic factor. In functional DEG analysis, eight Kyoto Encyclopedia of Genes and Genomes pathways were identified to be significantly associated with STAD risk factors. Thus, the 10-gene risk score model established in the present study was regarded as credible. This risk assessment tool may help identify patients with a high risk of STAD, and the proposed prognostic mRNAs may be useful in elucidating STAD pathogenesis.
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Affiliation(s)
- Encui Guan
- Department of Gastroenterology, The Central Hospital of Linyi, Linyi, Shandong 276400, P.R. China
| | - Feng Tian
- Department of Gastroenterology, The Central Hospital of Linyi, Linyi, Shandong 276400, P.R. China
| | - Zhaoxia Liu
- Department of Gastroenterology, The Central Hospital of Linyi, Linyi, Shandong 276400, P.R. China
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Jiang S, Qin Y, Liu P, Yang J, Yang S, He X, Zhou S, Gui L, Zhang C, Zhou L, Sun Y, Shi Y. Prognostic Nomogram and Predictive Factors in Refractory or Relapsed Diffuse Large B-Cell Lymphoma Patients Failing Front-Line R-CHOP Regimens. J Cancer 2020; 11:1516-1524. [PMID: 32047558 PMCID: PMC6995391 DOI: 10.7150/jca.36997] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2019] [Accepted: 11/30/2019] [Indexed: 12/12/2022] Open
Abstract
Background: The clinical course of relapsed or refractory (r/r) diffuse large B-cell lymphoma (DLBCL) is variable, with limited efficacy data of second-line treatment in a post-rituximab real-world context. Hence, we explored the predictors and constructed a nomogram for risk stratification in this population. Patients and methods: Among 296 r/r DLBCL patients pretreated with R-CHOP (rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone) at the Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College between 2006 and 2017, 231 were included for nomogram construction. After randomization, we constructed the prognostic nomogram in the primary cohort (n=161) based on a multivariate analysis and confirmed it in the validation cohort (n=70). Additionally, we explored predictive factors for second-line therapy using a ordinal regression analysis. Results: Four independent prognostic factors including rituximab in the second-line setting, initial Eastern Cooperative Oncology Group (ECOG) performance status (PS), response to front-line treatment, and invasion on progression/recurrence were used to construct the nomogram. The nomogram had a C index of 0.70 with AUC values of 0.73 and 0.71 for the primary and validation cohorts, respectively. Subsequently, three risk groups (low, intermediate, and high) were determined with median overall survival (OS) of 38.0, 25.0, and 7.0 months, respectively. Additionally, we conducted a multivariate ordinal regression analysis and identified prior response to front-line treatment (odds ratio=4.50, 95% CI: 1.84-11.27, p=0.001) and bulky disease at diagnosis (odds ratio=0.36, 95% CI: 0.182-0.702, p=0.003) were two independent factors for second-line treatment efficacy. Conclusions: The established predictors for treatment efficacy and the novel nomogram for survival in r/r DLBCL patients could potentially be applied for risk stratification and treatment guidance in the post-rituximab era.
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Affiliation(s)
- Shiyu Jiang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China
| | - Yan Qin
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China
| | - Peng Liu
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China
| | - Jianliang Yang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China
| | - Sheng Yang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China
| | - Xiaohui He
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China
| | - Shengyu Zhou
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China
| | - Lin Gui
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China
| | - Changgong Zhang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China
| | - Liqiang Zhou
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China
| | - Yan Sun
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China
| | - Yuankai Shi
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China
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Prognostic Models for Predicting Overall Survival in Patients with Primary Gastric Cancer: A Systematic Review. BIOMED RESEARCH INTERNATIONAL 2019; 2019:5634598. [PMID: 31641669 PMCID: PMC6766665 DOI: 10.1155/2019/5634598] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 08/23/2019] [Accepted: 09/05/2019] [Indexed: 02/06/2023]
Abstract
Background This study was designed to review the methodology and reporting of gastric cancer prognostic models and identify potential problems in model development. Methods This systematic review was conducted following the CHARMS checklist. MEDLINE and EMBASE were searched. Information on patient characteristics, methodological details, and models' performance was extracted. Descriptive statistics was used to summarize the methodological and reporting quality. Results In total, 101 model developments and 32 external validations were included. The median (range) of training sample size, number of death, and number of final predictors were 360 (29 to 15320), 193 (14 to 9560), and 5 (2 to 53), respectively. Ninety-one models were developed from routine clinical data. Statistical assumptions were reported to be checked in only nine models. Most model developments (94/101) used complete-case analysis. Discrimination and calibration were not reported in 33 and 55 models, respectively. The majority of models (81/101) have never been externally validated. None of the models have been evaluated regarding clinical impact. Conclusions Many prognostic models have been developed, but their usefulness in clinical practice remains uncertain due to methodological shortcomings, insufficient reporting, and lack of external validation and impact studies. Impact Future research should improve methodological and reporting quality and emphasize more on external validation and impact assessment.
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