1
|
Li Z, Hong Q, Li K. Nomogram predicting survival in patients with lymph node-negative hepatocellular carcinoma based on the SEER database and external validation. Eur J Gastroenterol Hepatol 2024:00042737-990000000-00349. [PMID: 38652516 DOI: 10.1097/meg.0000000000002756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
BACKGROUND The relationship between lymph node (LN) status and survival outcome in hepatocellular carcinoma (HCC) is a highly controversial topic. The aim of this study was to investigate the prognostic factors in patients without LN metastasis (LNM) and to construct a nomogram to predict cancer-specific survival (CSS) in this group of patients. METHODS We screened 6840 eligible HCC patients in the Surveillance, Epidemiology and End Results(SEER)database between 2010 and 2019 and randomized them into a training cohort and an internal validation cohort, and recruited 160 patients from Zhongnan Hospital of Wuhan University as an external validation cohort. Independent prognostic factors obtained from univariate and multivariate analysis were used to construct a nomogram prediction model. The concordance index (C-index), area under curve (AUC), calibration plots and decision curve analysis (DCA) were used to assess the predictive power and clinical application of the model. RESULTS Univariate and multivariate analysis revealed age, gender, bone metastasis, lung metastasis, AFP, T stage, surgery and chemotherapy as independent prognostic factors. The C-index of the constructed nomogram for the training cohort, internal validation cohort and external validation cohort are 0.746, 0.740, and 0.777, respectively. In the training cohort, the AUC at 1-, 3-, and 5-year were 0.81, 0.800, and 0.800, respectively. Calibration curves showed great agreement between the actual observations and predictions for the three cohorts. The DCA results suggest that the nomogram model has more clinical application potential. CONCLUSION We constructed a nomogram to predict CSS in HCC patients without LNM. The model has been internally and externally validated to have excellent predictive performance and can help clinicians determine prognosis and make treatment decisions.
Collapse
Affiliation(s)
- Ziqiang Li
- Department of Hepatobiliary and Pancreatic Surgery, Hubei Provincial Clinical Medicine Research Center for Minimally Invasive Diagnosis and Treatment of Hepatobiliary and Pancreatic Diseases, Zhongnan Hospital of Wuhan University, Wuhan, China
| | | | | |
Collapse
|
2
|
Yu L, Huang Z, Xiao Z, Tang X, Zeng Z, Tang X, Ouyang W. Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review). Oncol Rep 2024; 51:60. [PMID: 38456540 PMCID: PMC10940877 DOI: 10.3892/or.2024.8719] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 01/22/2024] [Indexed: 03/09/2024] Open
Abstract
Cancer metastasis is the primary cause of cancer deaths. Metastasis involves the spread of cancer cells from the primary tumors to other body parts, commonly through lymphatic and vascular pathways. Key aspects include the high mutation rate and the capability of metastatic cells to form invasive tumors even without a large initial tumor mass. Particular emphasis is given to early metastasis, occurring in initial cancer stages and often leading to misdiagnosis, which adversely affects survival and prognosis. The present review highlighted the need for improved understanding and detection methods for early metastasis, which has not been effectively identified clinically. The present review demonstrated the clinicopathological and molecular characteristics of early‑onset metastatic types of cancer, noting factors such as age, race, tumor size and location as well as the histological and pathological grade as significant predictors. In conclusion, the present review underscored the importance of early detection and management of metastatic types of cancer and called for improved predictive models, including advanced techniques such as nomograms and machine learning, so as to enhance patient outcomes, acknowledging the challenges and limitations of the current research as well as the necessity for further studies.
Collapse
Affiliation(s)
- Liqing Yu
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, P.R. China
- The Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Zhenjun Huang
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, P.R. China
| | - Ziqi Xiao
- The Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Xiaofu Tang
- The Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Ziqiang Zeng
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
- School of Public Health, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Xiaoli Tang
- School of Basic Medicine, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Wenhao Ouyang
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, P.R. China
| |
Collapse
|
3
|
Seow-En I, Koh YX, Zhao Y, Ang BH, Tan IEH, Chok AY, Tan EJKW, Au MKH. Predictive modeling algorithms for liver metastasis in colorectal cancer: A systematic review of the current literature. Ann Hepatobiliary Pancreat Surg 2024; 28:14-24. [PMID: 38129965 PMCID: PMC10896689 DOI: 10.14701/ahbps.23-078] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/16/2023] [Indexed: 12/23/2023] Open
Abstract
This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.
Collapse
Affiliation(s)
- Isaac Seow-En
- Department of Colorectal Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore
| | - Ye Xin Koh
- Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore
- Liver Transplant Service, SingHealth Duke-National University of Singapore Transplant Centre, Singapore
| | - Yun Zhao
- Department of Colorectal Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore
- Group Finance Analytics, Singapore Health Services, Singapore
| | - Boon Hwee Ang
- Group Finance Analytics, Singapore Health Services, Singapore
| | | | - Aik Yong Chok
- Department of Colorectal Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore
| | - Emile John Kwong Wei Tan
- Department of Colorectal Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore
| | - Marianne Kit Har Au
- Group Finance Analytics, Singapore Health Services, Singapore
- Finance, SingHealth Community Hospitals, Singapore
| |
Collapse
|
4
|
Ruan J, He Y, Li Q, Jiang Z, Liu S, Ai J, Mao K, Dong X, Zhang D, Yang G, Gao D, Li Z. A nomogram for predicting liver metastasis in patients with gastric gastrointestinal stromal tumor. J Gastrointest Surg 2024:S1091-255X(24)00333-0. [PMID: 38462423 DOI: 10.1016/j.gassur.2024.02.025] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/07/2024] [Accepted: 02/17/2024] [Indexed: 03/12/2024]
Abstract
BACKGROUND Liver metastasis (LIM) is an important factor in the diagnosis, treatment, follow-up, and prognosis of patients with gastric gastrointestinal stromal tumor (GIST). There is no simple tool to assess the risk of LIM in patients with gastric GIST. Our aim was to develop and validate a nomogram to identify patients with gastric GIST at high risk of LIM. METHODS Patient data diagnosed as having gastric GIST between 2010 and 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training cohort and internal validation cohort in a 7:3 ratio. For external validation, retrospective data collection was performed on patients diagnosed as having gastric GIST at Yunnan Cancer Center (YNCC) between January 2015 and May 2023. Univariate and multivariate logistic regression analyses were used to identify independent risk factors associated with LIM in patients with gastric GIST. An individualized LIM nomogram specific for gastric GIST was formulated based on the multivariate logistic model; its discriminative performance, calibration, and clinical utility were evaluated. RESULTS In the SEER database, a cohort of 2341 patients with gastric GIST was analyzed, of which 173 cases (7.39%) were found to have LIM; 239 patients with gastric GIST from the YNCC database were included, of which 25 (10.46%) had LIM. Multivariate analysis showed tumor size, tumor site, and sex were independent risk factors for LIM (P < .05). The nomogram based on the basic clinical characteristics of tumor size, tumor site, sex, and age demonstrated significant discrimination, with an area under the curve of 0.753 (95% CI, 0.692-0.814) and 0.836 (95% CI, 0.743-0.930) in the internal and external validation cohort, respectively. The Hosmer-Lemeshow test showed that the nomogram was well calibrated, whereas the decision curve analysis and the clinical impact plot demonstrated its clinical utility. CONCLUSIONS Tumor size, tumor subsite, and sex were significantly correlated with the risk of LIM in gastric GIST. The nomogram for patients with GIST can effectively predict the individualized risk of LIM and contribute to the planning and decision making related to metastasis management in clinical practice.
Collapse
Affiliation(s)
- Jinqiu Ruan
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Yinfu He
- Department of Radiology, the Third People's Hospital of Honghe Hani and Yi Autonomous Prefecture, Gejiu, China
| | - Qingwan Li
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Zhaojuan Jiang
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Shaoyou Liu
- Department of Oncology Surgery, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jing Ai
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Keyu Mao
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Xingxiang Dong
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Dafu Zhang
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Guangjun Yang
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Depei Gao
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China.
| | - Zhenhui Li
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China.
| |
Collapse
|
5
|
Saez de Gordoa K, Rodrigo-Calvo MT, Archilla I, Lopez-Prades S, Diaz A, Tarragona J, Machado I, Ruiz Martín J, Zaffalon D, Daca-Alvarez M, Pellisé M, Camps J, Cuatrecasas M. Lymph Node Molecular Analysis with OSNA Enables the Identification of pT1 CRC Patients at Risk of Recurrence: A Multicentre Study. Cancers (Basel) 2023; 15:5481. [PMID: 38001742 PMCID: PMC10670609 DOI: 10.3390/cancers15225481] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/11/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
Early-stage colorectal carcinoma (CRC)-pT1-is a therapeutic challenge and presents some histological features related to lymph node metastasis (LNM). A significant proportion of pT1 CRCs are treated surgically, resulting in a non-negligible surgical-associated mortality rate of 1.5-2%. Among these cases, approximately 6-16% exhibit LNM, but the impact on survival is unclear. Therefore, there is an unmet need to establish an objective and reliable lymph node (LN) staging method to optimise the therapeutic management of pT1 CRC patients and to avoid overtreating or undertreating them. In this multicentre study, 89 patients with pT1 CRC were included. All histological features associated with LNM were evaluated. LNs were assessed using two methods, One-Step Nucleic Acid Amplification (OSNA) and the conventional FFPE plus haematoxylin and eosin (H&E) staining. OSNA is an RT-PCR-based method for amplifying CK19 mRNA. Our aim was to assess the performance of OSNA and H&E in evaluating LNs to identify patients at risk of recurrence and to optimise their clinical management. We observed an 80.9% concordance in LN assessment using the two methods. In 9% of cases, LNs were found to be positive using H&E, and in 24.7% of cases, LNs were found to be positive using OSNA. The OSNA results are provided as the total tumour load (TTL), defined as the total tumour burden present in all the LNs of a surgical specimen. In CRC, a TTL ≥ 6000 CK19 m-RNA copies/µL is associated with poor prognosis. Three patients had TTL > 6000 copies/μL, which was associated with higher tumour budding. The discrepancies observed between the OSNA and H&E results were mostly attributed to tumour allocation bias. We concluded that LN assessment with OSNA enables the identification of pT1 CRC patients at some risk of recurrence and helps to optimise their clinical management.
Collapse
Affiliation(s)
- Karmele Saez de Gordoa
- Pathology Department, Centre of Biomedical Diagnosis (CDB), Hospital Clinic, 08036 Barcelona, Spain; (K.S.d.G.); (M.T.R.-C.); (I.A.); (S.L.-P.); (A.D.)
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain; (M.P.); (J.C.)
| | - Maria Teresa Rodrigo-Calvo
- Pathology Department, Centre of Biomedical Diagnosis (CDB), Hospital Clinic, 08036 Barcelona, Spain; (K.S.d.G.); (M.T.R.-C.); (I.A.); (S.L.-P.); (A.D.)
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain; (M.P.); (J.C.)
| | - Ivan Archilla
- Pathology Department, Centre of Biomedical Diagnosis (CDB), Hospital Clinic, 08036 Barcelona, Spain; (K.S.d.G.); (M.T.R.-C.); (I.A.); (S.L.-P.); (A.D.)
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain; (M.P.); (J.C.)
| | - Sandra Lopez-Prades
- Pathology Department, Centre of Biomedical Diagnosis (CDB), Hospital Clinic, 08036 Barcelona, Spain; (K.S.d.G.); (M.T.R.-C.); (I.A.); (S.L.-P.); (A.D.)
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain; (M.P.); (J.C.)
| | - Alba Diaz
- Pathology Department, Centre of Biomedical Diagnosis (CDB), Hospital Clinic, 08036 Barcelona, Spain; (K.S.d.G.); (M.T.R.-C.); (I.A.); (S.L.-P.); (A.D.)
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain; (M.P.); (J.C.)
- Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas (CIBEREHD), 28029 Madrid, Spain
- Department of Clinical Foundations, University of Barcelona (UB), 08036 Barcelona, Spain
| | - Jordi Tarragona
- Pathology Department, Hospital Arnau de Vilanova, 25198 Lleida, Spain;
| | - Isidro Machado
- Pathology Department, Instituto Valenciano de Oncología, Hospital Quirón-Salud Valencia, University of Valencia, 46010 Valencia, Spain;
- Centro de Investigación Biomédica en Red en Cancer (CIBERONC), 28029 Madrid, Spain
| | - Juan Ruiz Martín
- Pathology Department, Virgen de la Salud Hospital, 45071 Toledo, Spain;
| | - Diana Zaffalon
- Gastroenterology Department, Consorci Sanitari de Terrassa, 08227 Terrassa, Spain;
| | - Maria Daca-Alvarez
- Gastroenterology Department, Hospital Clinic, University of Barcelona, 08036 Barcelona, Spain;
| | - Maria Pellisé
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain; (M.P.); (J.C.)
- Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas (CIBEREHD), 28029 Madrid, Spain
- Gastroenterology Department, Hospital Clinic, University of Barcelona, 08036 Barcelona, Spain;
| | - Jordi Camps
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain; (M.P.); (J.C.)
- Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas (CIBEREHD), 28029 Madrid, Spain
- Cell Biology and Medical Genetics Unit, Department of Cell Biology, Physiology and Immunology, Faculty of Medicine, Autonomous University of Barcelona (UAB), 08193 Bellaterra, Spain
| | - Miriam Cuatrecasas
- Pathology Department, Centre of Biomedical Diagnosis (CDB), Hospital Clinic, 08036 Barcelona, Spain; (K.S.d.G.); (M.T.R.-C.); (I.A.); (S.L.-P.); (A.D.)
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain; (M.P.); (J.C.)
- Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas (CIBEREHD), 28029 Madrid, Spain
- Department of Clinical Foundations, University of Barcelona (UB), 08036 Barcelona, Spain
| |
Collapse
|
6
|
Wu P, Jiang Y, Xing H, Song W, Cui X, Wu XL, Xu G. Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study. Phys Med Biol 2023; 68:175023. [PMID: 37524093 DOI: 10.1088/1361-6560/acec2d] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/31/2023] [Indexed: 08/02/2023]
Abstract
Background. Breast cancer is the most prevalent cancer diagnosed in women worldwide. Accurately and efficiently stratifying the risk is an essential step in achieving precision medicine prior to treatment. This study aimed to construct and validate a nomogram based on radiomics and deep learning for preoperative prediction of the malignancy of breast cancer (MBC).Methods. The clinical and ultrasound imaging data, including brightness mode (B-mode) and color Doppler flow imaging, of 611 breast cancer patients from multiple hospitals in China were retrospectively analyzed. Patients were divided into one primary cohort (PC), one validation cohort (VC) and two test cohorts (TC1 and TC2). A multimodality deep learning radiomics nomogram (DLRN) was constructed for predicting the MBC. The performance of the proposed DLRN was comprehensively assessed and compared with three unimodal models via the calibration curve, the area under the curve (AUC) of receiver operating characteristics and the decision curve analysis.Results. The DLRN discriminated well between the MBC in all cohorts [overall AUC (95% confidence interval): 0.983 (0.973-0.993), 0.972 (0.952-0.993), 0.897 (0.823-0.971), and 0.993 (0.977-1.000) on the PC, VC, test cohorts1 (TC1) and test cohorts2 TC2 respectively]. In addition, the DLRN performed significantly better than three unimodal models and had good clinical utility.Conclusion. The DLRN demonstrates good discriminatory ability in the preoperative prediction of MBC, can better reveal the potential associations between clinical characteristics, ultrasound imaging features and disease pathology, and can facilitate the development of computer-aided diagnosis systems for breast cancer patients. Our code is available publicly in the repository athttps://github.com/wupeiyan/MDLRN.
Collapse
Affiliation(s)
- Peiyan Wu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Yan Jiang
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Hanshuo Xing
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Wenbo Song
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Xinwu Cui
- Department of Medical Ultrasound, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Xing Long Wu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Guoping Xu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| |
Collapse
|
7
|
Wu H, Ding P, Wu J, Sun C, Guo H, Chen S, Lowe S, Yang P, Tian Y, Liu Y, Zhao Q. A New Online Dynamic Nomogram: Construction and Validation of a Predictive Model for Distant Metastasis Risk and Prognosis in Patients with Gastrointestinal Stromal Tumors. J Gastrointest Surg 2023; 27:1429-1444. [PMID: 37231240 DOI: 10.1007/s11605-023-05706-9] [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] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/28/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND Gastrointestinal stromal tumor (GIST) is the most common sarcoma of the digestive tract, among which patients with distant metastases tend to have a poor prognosis. This study aimed to develop a model for predicting distant metastasis in GIST patients and to develop two models for monitoring overall survival (OS) and cancer-specific survival (CSS) in GIST patients with metastasis. This would allow us to develop an optimal, individualized treatment strategy. METHODS We reviewed demographic and clinicopathological characteristics data from 2010 to 2017 of patients diagnosed with GIST in the Surveillance, Epidemiology, and End Results (SEER) database. The data of the external validation group was reviewed from the Forth Hospital of Hebei Medical University. Univariate and multivariate logistic regression analyses were used to confirm the independent risk factors for distant metastasis in the GIST patients, and univariate and multivariate Cox regression analyses were performed to identify the independent prognostic factors for OS and CSS in the GIST patients with distant metastasis. Subsequently, three web-based novel nomograms were developed, which were evaluated by the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS Of the 3639 patients who met the inclusion criteria, 418 (11.4%) had distant metastases. The risk factors for distant metastasis in GIST patients included sex, primary site, grade, N stage, tumor size, and mitotic count. For OS, the independent prognosis factors for GIST patients with metastasis included age, race, marital, primary site, chemotherapy, mitotic count, and metastasis at the lung, and for CSS, age, race, marital, primary site, and metastasis at the lung were the independent prognosis factors. Three web-based nomograms were constructed based on these independent factors, respectively. The ROC curves, calibration curves, and DCA were performed in the training, testing, and validation sets which confirmed the high accuracy and strong clinical practice power for the nomograms. CONCLUSION Population-based nomograms can help clinicians predict the occurrence and prognosis of distant metastases in patients with GIST, which may be helpful for clinicians to formulate clinical management and appropriate treatment strategies.
Collapse
Affiliation(s)
- Haotian Wu
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China
| | - Ping'an Ding
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China
| | - Jiaxiang Wu
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China
| | - Chenyu Sun
- AMITA Health Saint Joseph Hospital Chicago, 2900 N. Lake Shore Drive, Chicago, IL, 60657, USA
| | - Honghai Guo
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China
| | - Shuya Chen
- Newham University Hospital, Glen Road, Plaistow, E13 8SL, London, UK
| | - Scott Lowe
- College of Osteopathic Medicine, Kansas City University, 1750 Independence Ave, Kansas City, MO, 64106, USA
| | - Peigang Yang
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China
| | - Yuan Tian
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China
| | - Yang Liu
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China
| | - Qun Zhao
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China.
| |
Collapse
|
8
|
Liu Y, Sun Z, Guo Y, Liu C, Tian S, Dong W. Construction and validation of a nomogram of risk factors and cancer-specific survival prognosis for combined lymphatic metastases in patients with early-onset colorectal cancer. Int J Colorectal Dis 2023; 38:128. [PMID: 37183238 DOI: 10.1007/s00384-023-04432-7] [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] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/09/2023] [Indexed: 05/16/2023]
Abstract
PURPOSE This study aimed to investigate the risk and prognostic factors of lymph node metastasis (LNM) in early-onset colorectal cancer (EO-CRC) and to develop nomograms for quantitatively predicting LNM and the cancer-specific survival (CSS). METHODS A total of 22,405 EO-CRC patients were included in this study using the SEER database from 2010 to 2017. Logistic and Cox regression were used to identify risk and the potential prognostic factors, respectively, for EO-CRC with LNM. Subsequently, nomograms regarding the risk of LNM in EO-CRC patients and its corresponding CSS were constructed based on these factors. The discriminative ability, calibration and clinical usefulness of the nomograms were assessed by the area under the receiver operating characteristic (ROC) curves (AUC), calibration curves, and decision curve analysis (DCA). RESULTS T-stage and pathological grade were the most represented factors in the predicted LNM nomogram, while histological type and combined distant metastases were the most represented in the nomogram for CSS in EO-CRC patients with LNM (all P < 0.05). The nomogram constructed based on the prognostic factors screened by Cox regression had good performance with C-index of 0.807 and 0.802 for the training and validation cohorts, respectively. The calibration curve showed that the nomograms' predictions were in line with actual observations. Additionally, the ROC curves indicated good discrimination, and the DCA curves implied significant clinical utility of the nomograms. CONCLUSION The nomograms we constructed have significant performance in predicting the incidence and prognosis of LNM in EO-CRC patients, which may help clinicians to make better treatment decision making.
Collapse
Affiliation(s)
- Yupei Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, No 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
| | - Zhiyi Sun
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Yinyun Guo
- Department of Gastroenterology, Renmin Hospital of Wuhan University, No 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
| | - Chuan Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, No 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
| | - Shan Tian
- Department of Infection, Union Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Weiguo Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, No 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China.
| |
Collapse
|
9
|
Xu C, Zhang F, Cheng W, Zhu Y. Prediction models for overall and cancer-specific survival in patients with metastatic early-onset colorectal cancer: a population-based study. Int J Colorectal Dis 2023; 38:99. [PMID: 37067609 DOI: 10.1007/s00384-023-04369-x] [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] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/06/2023] [Indexed: 04/18/2023]
Abstract
PURPOSE Metastatic early-onset colorectal cancer (EO-CRC) is on the rise, yet there is a dearth of predictive models for this disease. Therefore, it is crucial to develop a nomogram to aid in the early detection and management of metastatic colorectal cancer in young patients. METHODS We retrieved data from the SEER database on patients with metastatic colorectal cancer aged 50 or younger between 2010 and 2017. The data were randomly allocated in a 7:3 ratio to training and validation cohorts, and univariate and multivariate Cox regression analyses were used to identify independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) at 1, 3, and 5 years. The nomograms were developed based on these factors, and their discriminatory and calibration capabilities were validated. Using the nomogram risk scores, patients were stratified into low-risk and high-risk groups. RESULTS The study included 2470 patients with metastatic EO-CRC. Univariate and multivariate Cox regression analysis identified 12 independent risk factors that were included in the nomogram. The training cohort had a consistency index (C-index) of 0.71, while the validation cohort had a C-index of 0.70, demonstrating good predictive accuracy. Calibration plots showed a high level of consistency between the observed and predicted values, with overlapping plots along the diagonal. The decision curve analysis (DCA) revealed that the nomogram had a high clinical application value. CONCLUSIONS The novel nomograms were created to predict the prognosis of patients with metastatic EO-CRC, which can aid clinicians in developing more effective treatment strategies and contribute to more accurate prognostic assessments.
Collapse
Affiliation(s)
- Chengxin Xu
- Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Fengfeng Zhang
- Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - WanRong Cheng
- Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yanbo Zhu
- Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China.
| |
Collapse
|
10
|
Duan W, Wang W, He C. A novel potential inflammation-nutrition biomarker for predicting lymph node metastasis in clinically node-negative colon cancer. Front Oncol 2023; 13:995637. [PMID: 37081978 PMCID: PMC10111825 DOI: 10.3389/fonc.2023.995637] [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: 07/16/2022] [Accepted: 03/20/2023] [Indexed: 04/22/2023] Open
Abstract
Background The purpose of this study is to investigate the predictive significance of (platelet × albumin)/lymphocyte ratio (PALR) for lymph node metastasis (LNM) in patients with clinically node-negative colon cancer (cN0 CC). Methods Data from 800 patients with primary CC who underwent radical surgery between March 2016 and June 2021 were reviewed. The non-linear relationship between PALR and the risk of LNM was explored using a restricted cubic spline (RCS) function while a receiver operating characteristic (ROC) curve was developed to determine the predictive value of PALR. Patients were categorized into high- and low-PALR cohorts according to the optimum cut-off values derived from Youden's index. Univariate and multivariate logistic regression analyses were used to identify the independent indicators of LNM. Sensitivity analysis was performed to repeat the main analyses with the quartile of PALR. Results A total of eligible 269 patients with primary cN0 CC were retrospectively selected. The value of the area under the ROC curve for PALR for predicting LNM was 0.607. RCS visualized the uptrend linear relationship between PALR and the risk of LNM (p-value for non-linearity > 0.05). PALR (odds ratio = 2.118, 95% confidence interval, 1.182-3.786, p = 0.011) was identified as an independent predictor of LNM in patients with cN0 CC. A nomogram incorporating PALR and other independent predictors was constructed with an internally validated concordance index of 0.637. The results of calibration plots and decision curve analysis supported a good performance ability and the sensitivity analysis further confirmed the robustness of our findings. Conclusion PALR has promising clinical applications for predicting LNM in patients with cN0 CC.
Collapse
|
11
|
Zhou Y, Lin C, Zhu L, Zhang R, Cheng L, Chang Y. Nomograms and scoring system for forecasting overall and cancer-specific survival of patients with prostate cancer. Cancer Med 2022; 12:2600-2613. [PMID: 35993499 PMCID: PMC9939188 DOI: 10.1002/cam4.5137] [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: 04/23/2022] [Revised: 07/11/2022] [Accepted: 08/02/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Estimated life expectancy is one of the most important factors in determining treatment options for prostate cancer (PCa) patients. However, clinicians have few effective prognostic tools to individually assess survival in patients with PCa. METHODS We screened 283,252 patients diagnosed with PCa from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015, and randomly divided them into the training and validation groups. We used univariate and multivariate Cox analyses to identify independent prognostic factors and further established nomograms to predict 1-, 3-, 5-, and 10-year overall survival (OS) and cancer-specific survival (CSS) for PCa patients. The prediction performance of nomograms was tested and externally validated by Concordance index (C-index) and receiver operating characteristic (ROC) curve. Calibration curve and decision curve analysis (DCA) were used for internal validation. We further developed PCa prognostic scoring system based on the impact of available variables on survival. RESULTS The variables age, race, marital status, TNM stage, surgery method, radiotherapy, chemotherapy, PSA value, and Gleason score identified as independent prognostic factors were included in the survival nomograms. The results of training (C-index: OS = 0.776, CSS = 0.889; AUC value: OS = 0.772-0.802, CSS = 0.892-0.936) and external validation (C-index: OS = 0.759, CSS = 0.875) indicated our nomograms had good performance in predicting 1-, 3-, 5-, and 10-year OS and CSS prediction. Internal validation using the calibration curves and DCA curves demonstrated the effectiveness of the prediction models. The prognostic scoring system was more effective than the AJCC staging system in predicting the survival of PCa patients, especially for OS. CONCLUSION The prognostic nomograms and prognostic scoring system have favorable performance in predicting OS and CSS of PCa patients. These individualized survival prediction tools may contribute to clinical decisions.
Collapse
Affiliation(s)
- Yuan Zhou
- Department of Urology SurgeryThe People's Hospital of Xuancheng CityXuanchengChina,Wannan Medical CollegeWuhuChina
| | - Changming Lin
- Department of Urology SurgeryThe Fourth Affiliated Hospital of AnHui Medical UniversityHefeiChina
| | - Lian Zhu
- Department of Urology SurgeryThe People's Hospital of Xuancheng CityXuanchengChina,Wannan Medical CollegeWuhuChina
| | - Rentao Zhang
- Department of Urology SurgeryThe People's Hospital of Xuancheng CityXuanchengChina,Wannan Medical CollegeWuhuChina
| | - Lei Cheng
- Department of Pulmonary MedicineShanghai Chest Hospital, Shanghai Jiao Tong UniversityShanghaiChina
| | - Yuanyuan Chang
- Department of Pulmonary MedicineShanghai Chest Hospital, Shanghai Jiao Tong UniversityShanghaiChina
| |
Collapse
|
12
|
Wang Y, Xu Y, Zhang Y. A novel ferroptosis-related long noncoding RNA signature for relapse free survival prediction in patients with breast cancer. Medicine (Baltimore) 2022; 101:e29573. [PMID: 35945765 PMCID: PMC9351903 DOI: 10.1097/md.0000000000029573] [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] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Ferroptosis is the process of cell death dependent on iron. Growing evidence suggests that ferroptosis plays vital roles in the biological process of many cancers. However, just a small number of ferroptosis-related lncRNAs have been explored in depth. Ferroptosis-related lncRNAs in breast cancer (BC) were identified by co-expression analysis based on The Cancer Genome Atlas database (TCGA). The whole set was divided into a training set and a test set with a 1:1 ratio. Univariate Cox regression and LASSO analyses were performed to establish a signature in the 3 sets. Kaplan-Meier analysis and receiver operating characteristic (ROC) for the 3 sets validated the effectiveness and robustness of the signature. Besides, we also explore the relationship between this and clinical characteristics, immune cell infiltration and tumor microenvironment. Meanwhile, the nomogram was drawn by screening indicators of independent recurrent prediction. Finally, we evaluated the relationships between the signature and tumor microenvironment. We identified 391 ferroptosis-related lncRNAs and constructed a 5 lncRNAs-based signature in the training, test, and whole sets, stratifying patients into high-risk and low-risk groups. According to survival analysis, patients in the high-risk groups had worse relapse free survival (RFS) compared to the low risk-groups. The ROC curves indicated that the recurrent signature had a promising predictive capability for BC patients. Moreover, an independent factors-based nomogram model could offer the quantitative prediction and net benefit for the recurrence of BC patients. Finally, the microenvironment, including tumor mutational burden (TMB), immune cell functions and immune checkpoints, showed big differences between the 2 groups. The 5 ferroptosis-related lncRNAs and their signature might be novel promising biomarkers and immunotherapy targets for patients with BC.
Collapse
Affiliation(s)
- Yuzhi Wang
- Department of Laboratory Medicine, People’s Hospital of Deyang City, Deyang, Sichuan, P. R. China
| | - Yunfei Xu
- Department of Laboratory Medicine, Chengdu Women’s and Children’s Central Hospital, Chengdu, Sichuan, P. R. China
| | - Yi Zhang
- Department of Blood Transfusion, People’s Hospital of Deyang City, Deyang, Sichuan, P. R. China
- *Correspondence: Yi Zhang, People’s Hospital of Deyang City, No. 173, Section 1, Taishan North Road, Deyang City, Sichuan 618000, China (e-mail: )
| |
Collapse
|
13
|
Zhou J, Li T, Xiao Y, Lin J, Chen X, Peng S, Huang M, Shi X, Cai L, Huang P, Huang M. Development and external validation of prognostic nomograms for liver disease-free and overall survival in locally advanced rectal cancer with neoadjuvant therapy: a post cohort study based on the FOWARC trial. Ann Transl Med 2022; 10:694. [PMID: 35845530 PMCID: PMC9279784 DOI: 10.21037/atm-22-2790] [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] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 06/20/2022] [Indexed: 11/06/2022]
Abstract
Background There is still a lack of nomograms that can accurately predict liver metastasis and poor prognosis after neoadjuvant therapy for locally advanced rectal cancer (LARC). Effective nomograms may help clinicians better identify LARC patients with potential high-risk risks, so as to carry out more targeted monitoring, treatment and follow-up. Methods The nomograms were based on the FOWARC trial (NCT01211210), which included 302 LARC patients who underwent neoadjuvant treatment before surgery at the Sixth Affiliated Hospital of Sun Yat-sen University from 2011 to 2014. The predictive accuracy and discriminative ability of the nomograms were determined by the concordance index (C-index) and calibration curve. The results were validated using bootstrap resampling and a prospective study on 100 patients in 2017. Results The 3-year liver disease-free survival (LDFS) rate after neoadjuvant treatment for LARC was 91.65% (training cohort 92.22%, validation cohort 90.01%). Factors associated with LDFS were hepatitis B virus (HBV) infection, anemia, lymph node number, postoperative T stage and tumor nodule, which were all included in the nomogram for LDFS. The C-indies of the nomogram for LDFS were 0.828 and 0.845 in the training and validation cohorts. The 3-year overall survival (OS) rate was 94.14% (training cohort 94.13%, validation cohort 94.05%). Factors in the nomogram for OS were mesorectal fascia involvement (MRF), postoperative N stage, pathological differentiation, tumor nodule and neural invasion. The C-indies of the nomogram for predicting OS were 0.73 and 0.774 in the training and validation cohorts. The calibration curve for the survival probability showed good agreement between the nomogram predictions and the actual observations. Conclusions The nomograms established in this study can effectively predict LDFS and has good clinical application potential for OS in LARC patients treated with neoadjuvant therapy.
Collapse
Affiliation(s)
- Jiaming Zhou
- Department of Colon and Rectum Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Tuoyang Li
- Department of Colon and Rectum Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yuanlv Xiao
- Department of General Surgery, Panyu Central Hospital, Guangzhou, China
| | - Jinxin Lin
- Department of Colon and Rectum Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiaoqiong Chen
- Department of Colon and Rectum Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shaoyong Peng
- Department of Colon and Rectum Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Mingzhe Huang
- Department of Colon and Rectum Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xuebin Shi
- Department of Colon and Rectum Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Linbin Cai
- Department of Colon and Rectum Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Pinzhu Huang
- Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Meijin Huang
- Department of Colon and Rectum Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| |
Collapse
|
14
|
Wang J, Tang J, Liu X, He D. A web-based prognostic nomogram for the cancer specific survival of elderly patients with T1-T3N0M0 renal pelvic transitional cell carcinoma based on the surveillance, epidemiology, and end results database. BMC Urol 2022; 22:78. [PMID: 35610606 PMCID: PMC9131540 DOI: 10.1186/s12894-022-01028-1] [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: 10/09/2021] [Accepted: 04/20/2022] [Indexed: 11/25/2022] Open
Abstract
Background At present, there are few studies on renal pelvic transitional cell carcinoma (RPTCC) in elderly patients in the literature. The study aims to establish a new nomogram of cancer-specific survival (CSS) in elderly patients with T1-T3N0M0 RPTCC and validate its reliability. Methods This study downloaded the data of 1375 elderly patients with T1-T3N0M0 RPTCC in the Surveillance, Epidemiology, and Final Results (SEER) database from 2004 to 2018. Patients were randomly divided into training cohort (n = 977) and validation cohort (n = 398). Proportional subdistribution hazard analyse was applied to determine independent prognostic factors. Based on these factors, we constructed a compting risk model nomogram. We use the calibration plots, the area under the receiver operating characteristics curve (AUC), concordance index (C-index), and decision curve analysis (DCA) to validate predictive performance and clinical applicability. Patients were divided into low-risk group and high-risk group based on nomogram risk score. Kaplan–Meier curve was applied to analyze the difference in survival curve between the two groups of patients. Results We found that the risk factors affecting CSS in elderly patients with T1-T3N0M0 RPTCC are surgery, AJCC stage, laterality, tumor size, histological grade, and tumour laterality. Based on these factors, we established a nomogram to predict the CSS of RPTCC patients at 1-, 3-, and 5-year. The calibration plots showed that the predicted value was highly consistent with the observed value. In the training cohort and validation cohort, the C-index of the nomogram were 0.671(95% CI 0.622–0.72) and 0.679(95% CI 0.608–0.750), respectively, the AUC showed similar results. The DCA suggests that namogram performs better than the AJCC stage system. The Kaplan–Meier curve showed that CSS of patients was significantly higher in the low-risk group. Conclusions In this study, the SEER database was used for the first time to create and validate a new nomogram prediction model for elderly patients with T1-T3N0M0 RPTCC. Compared with the traditional AJCC stage system, our new nomogram can more accurately predict the CSS of elderly patients with T1-T3N0M0 RPTCC, which is helpful for patient prognosis assessment and treatment strategies selection. Supplementary Information The online version contains supplementary material available at 10.1186/s12894-022-01028-1.
Collapse
Affiliation(s)
- Jinkui Wang
- Department of Urology; Ministry of Education Key Laboratory of Child Development and Disorders; National Clinical Research Center for Child Health and Disorders (Chongqing); China International Science and Technology Cooperation Base of Child Development and Critical Disorders; Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, 2 ZhongShan Rd, Chongqing, 400013, People's Republic of China
| | - Jie Tang
- Department of Epidemiology, Public Health School, Shenyang Medical College, Shenyang, China
| | - Xiaozhu Liu
- Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Dawei He
- Department of Urology; Ministry of Education Key Laboratory of Child Development and Disorders; National Clinical Research Center for Child Health and Disorders (Chongqing); China International Science and Technology Cooperation Base of Child Development and Critical Disorders; Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, 2 ZhongShan Rd, Chongqing, 400013, People's Republic of China.
| |
Collapse
|
15
|
Tang J, Wang J, Pan X. A Web-Based Prediction Model for Overall Survival of Elderly Patients With Malignant Bone Tumors: A Population-Based Study. Front Public Health 2022; 9:812395. [PMID: 35087789 PMCID: PMC8787310 DOI: 10.3389/fpubh.2021.812395] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [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: 11/10/2021] [Accepted: 12/13/2021] [Indexed: 01/26/2023] Open
Abstract
Background: Malignant bone tumors (MBT) are one of the causes of death in elderly patients. The purpose of our study is to establish a nomogram to predict the overall survival (OS) of elderly patients with MBT. Methods: The clinicopathological data of all elderly patients with MBT from 2004 to 2018 were downloaded from the SEER database. They were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate Cox regression analysis was used to identify independent risk factors for elderly patients with MBT. A nomogram was built based on these risk factors to predict the 1-, 3-, and 5-year OS of elderly patients with MBT. Then, used the consistency index (C-index), calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model was. Decision curve analysis (DCA) was used to assess the clinical potential application value of the nomogram. Based on the scores on the nomogram, patients were divided into high- and low-risk groups. The Kaplan-Meier (K-M) curve was used to test the difference in survival between the two patients. Results: A total of 1,641 patients were included, and they were randomly assigned to the training set (N = 1,156) and the validation set (N = 485). The univariate and multivariate analysis of the training set suggested that age, sex, race, primary site, histologic type, grade, stage, M stage, surgery, and tumor size were independent risk factors for elderly patients with MBT. The C-index of the training set and the validation set were 0.779 [0.759–0.799] and 0.801 [0.772–0.830], respectively. The AUC of the training and validation sets also showed similar results. The calibration curves of the training and validation sets indicated that the observed and predicted values were highly consistent. DCA suggested that the nomogram had potential clinical value compared with traditional TNM staging. Conclusion: We had established a new nomogram to predict the 1-, 3-, 5-year OS of elderly patients with MBT. This predictive model can help doctors and patients develop treatment plans and follow-up strategies.
Collapse
Affiliation(s)
- Jie Tang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenyang Medical College, Shenyang, China
| | - JinKui Wang
- Department of Orthopedics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Xiudan Pan
- Department of Biostatistics and Epidemiology, School of Public Health, Shenyang Medical College, Shenyang, China
| |
Collapse
|
16
|
Tan X, Wang J, Tang J, Tian X, Jin L, Li M, Zhang Z, He D. A Nomogram for Predicting Cancer-Specific Survival in Children With Wilms Tumor: A Study Based on SEER Database and External Validation in China. Front Public Health 2022; 10:829840. [PMID: 35462822 PMCID: PMC9021525 DOI: 10.3389/fpubh.2022.829840] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 02/28/2022] [Indexed: 11/29/2022] Open
Abstract
Background Wilms tumor (WT) is the most common tumor in children. We aim to construct a nomogram to predict the cancer-specific survival (CSS) of WT in children and externally validate in China. Methods We downloaded the clinicopathological data of children with WT from 2004 to 2018 in the SEER database. At the same time, we used the clinicopathological data collected previously for all children with WT between 2013 and 2018 at Children's Hospital of Chongqing Medical University (Chongqing, China). We analyzed the difference in survival between the patients in the SEER database and our hospital. Cox regression analysis was used to screen for significant risk factors. Based on these factors, a nomogram was constructed to predict the CSS of children with WT. Calibration curve, concordance index (C-index), the area under the receiver operating curve (AUC) and decision curve analysis (DCA) was used to evaluate the accuracy and reliability of the model. Results We included 1,045 children with WT in the SEER database. At the same time, we collected 112 children with WT in our hospital. The Kaplan-Meier curve suggested that children in China with WT had a higher mortality rate than those in the United States. Cox regression analysis revealed that age, lymph node density (LND), and tumor stage were significant prognostic factors for the patients in the SEER database. However, the patients in our hospital only confirmed that the tumor stage and the number of positive regional lymph nodes were significant factors. The prediction model established by the SEER database had been validated internally and externally to prove that it had good accuracy and reliability. Conclusion We have constructed a survival prognosis prediction model for children with WT, which has been validated internally and externally to prove accuracy and reliability.
Collapse
Affiliation(s)
- Xiaojun Tan
- Department of Urology, Children's Hospital of Chongqing Medical University, Chongqing, China
- Department of Urology, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical University, Nanchong, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
| | - Jinkui Wang
- Department of Urology, Children's Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
| | - Jie Tang
- Department of Epidemiology, Public Health School, Shenyang Medical College, Shenyang, China
| | - Xiaomao Tian
- Department of Urology, Children's Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
| | - Liming Jin
- Department of Urology, Children's Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
| | - Mujie Li
- Department of Urology, Children's Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
| | - Zhaoxia Zhang
- Department of Urology, Children's Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
| | - Dawei He
- Department of Urology, Children's Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
- *Correspondence: Dawei He
| |
Collapse
|
17
|
Tang J, Wang J, Pan X, Liu X, Zhao B. A Web-Based Prediction Model for Cancer-Specific Survival of Middle-Aged Patients With Non-metastatic Renal Cell Carcinoma: A Population-Based Study. Front Public Health 2022; 10:822808. [PMID: 35284377 PMCID: PMC8907592 DOI: 10.3389/fpubh.2022.822808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 01/17/2022] [Indexed: 11/13/2022] Open
Abstract
Background Renal cell carcinoma (RCC) is one of the most common cancers in middle-aged patients. We aimed to establish a new nomogram for predicting cancer-specific survival (CSS) in middle-aged patients with non-metastatic renal cell carcinoma (nmRCC). Methods The clinicopathological information of all patients from 2010 to 2018 was downloaded from the SEER database. These patients were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate COX regression analyses were used to identify independent risk factors for CSS in middle-aged patients with nmRCC in the training set. Based on these independent risk factors, a new nomogram was constructed to predict 1-, 3-, and 5-year CSS in middle-aged patients with nmRCC. Then, we used the consistency index (C-index), calibration curve, and area under receiver operating curve (AUC) to validate the accuracy and discrimination of the model. Decision curve analysis (DCA) was used to validate the clinical application value of the model. Results A total of 27,073 patients were included in the study. These patients were randomly divided into a training set (N = 18,990) and a validation set (N = 8,083). In the training set, univariate and multivariate Cox regression analysis indicated that age, sex, histological tumor grade, T stage, tumor size, and surgical method are independent risk factors for CSS of patients. A new nomogram was constructed to predict patients' 1-, 3-, and 5-year CSS. The C-index of the training set and validation set were 0.818 (95% CI: 0.802-0.834) and 0.802 (95% CI: 0.777-0.827), respectively. The 1 -, 3 -, and 5-year AUC for the training and validation set ranged from 77.7 to 80.0. The calibration curves of the training set and the validation set indicated that the predicted value is highly consistent with the actual observation value, indicating that the model has good accuracy. DCA also suggested that the model has potential clinical application value. Conclusion We found that independent risk factors for CSS in middle-aged patients with nmRCC were age, sex, histological tumor grade, T stage, tumor size, and surgery. We have constructed a new nomogram to predict the CSS of middle-aged patients with nmRCC. This model has good accuracy and reliability and can assist doctors and patients in clinical decision making.
Collapse
Affiliation(s)
- Jie Tang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenyang Medical College, Shenyang, China
| | - Jinkui Wang
- Department of Urology, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, National Clinical Research Center for Child Health and Disorders (Chongqing), Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Xiudan Pan
- Department of Biostatistics and Epidemiology, School of Public Health, Shenyang Medical College, Shenyang, China
| | - Xiaozhu Liu
- Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Binyi Zhao
- Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Binyi Zhao
| |
Collapse
|
18
|
Wang J, Tang J, Chen T, Yue S, Fu W, Xie Z, Liu X. A web-based prediction model for overall survival of elderly patients with early renal cell carcinoma: a population-based study. J Transl Med 2022; 20:90. [PMID: 35164796 PMCID: PMC8845298 DOI: 10.1186/s12967-022-03287-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 01/31/2022] [Indexed: 12/13/2022] Open
Abstract
Background The number of elderly patients with early renal cell carcinoma (RCC) is on the rise. However, there is still a lack of accurate prediction models for the prognosis of early RCC in elderly patients. It is necessary to establish a new nomogram to predict the prognosis of elderly patients with early RCC. Methods The data of patients aged above 65 years old with TNM stage I and II RCC were downloaded from the SEER database between 2010 and 2018. The patients from 2010 to 2017 were randomly assigned to the training cohort (n = 7233) and validation cohort (n = 3024). Patient data in 2018(n = 1360) was used for external validation. We used univariable and multivariable Cox regression model to evaluate independent prognostic factors and constructed a nomogram to predict the 1-, 3-, and 5-year overall survival (OS) rates of patients with early-stage RCC. Multiple parameters were used to validate the nomogram, including the consistency index (C-index), the calibration plots, the area under the receiver operator characteristics (ROC) curve, and the decision curve analysis (DCA). Results The study included a total of 11,617 elderly patients with early RCC. univariable and multivariable Cox regression analysis based on predictive variables such as age, sex, histologic type, Fuhrman grade, T stage, surgery type, tumors number, tumor size, and marriage were included to establish a nomogram. The C-index of the training cohort and validation cohort were 0.748 (95% CI: 0.760–0.736) and 0.744 (95% CI: 0.762–0.726), respectively. In the external validation cohort, C-index was 0.893 (95% CI: 0.928–0.858). The calibration plots basically coincides with the diagonal, indicating that the observed OS was almost equal to the predicted OS. It was shown in DCA that the nomogram has more important clinical significance than the traditional TNM stage. Conclusion A novel nomogram was developed to assess the prognosis of an elderly patient with early RCC and to predict prognosis and formulate treatment and follow-up strategies.
Collapse
|
19
|
Lu YJ, Duan WM. Establishment and validation of a novel predictive model to quantify the risk of bone metastasis in patients with prostate cancer. Transl Androl Urol 2021; 10:310-325. [PMID: 33532320 PMCID: PMC7844484 DOI: 10.21037/tau-20-1133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Background Patients with prostate cancer (PCa) commonly suffer from bone metastasis during disease progression. This study aims to construct and validate a nomogram to quantify bone metastasis risk in patients with PCa. Methods Clinicopathological data of patients diagnosed with PCa between 2010 and 2015 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Predictors for bone metastasis were identified by logistic regression analyses to establish a nomogram. The concordance index (c-index) and calibration plots were generated to assess the nomogram’s discrimination, and the area under the receiver operating characteristic curve (AUC) was used to compare the precision of the nomogram with routine staging systems. The nomogram’s clinical performance was evaluated by decision curve analysis (DCA) and clinical impact curves (CIC). Independent prognostic factors were identified by Cox regression analysis. Results A total of 168,414 eligible cases were randomly assigned to the training cohort or validation cohort at a ratio of 1:1. The nomogram, which was established based on independent factors, showed good accuracy, with c-indexes of 0.911 in the training set and 0.910 in the validation set. Calibration plots also approached 45 degrees. After other distant metastatic sites were included in the predictive model, the new nomogram displayed superior prediction performance. The AUCs and net benefit of the nomograms were both higher than those of other routine staging systems. Furthermore, bone metastasis prediction points were shown to be a new risk factor for overall survival. Conclusions Novel validated nomograms can effectively predict the risk of bone metastasis in patients with PCa and help clinicians improve cancer management.
Collapse
Affiliation(s)
- Yu-Jie Lu
- Department of Oncology, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Wei-Ming Duan
- Department of Oncology, the First Affiliated Hospital of Soochow University, Suzhou, China
| |
Collapse
|
20
|
Chen D, Qin Y, Dai M, Li L, Liu H, Zhou Y, Qiu C, Chen Y, Jiang Y. BGN and COL11A1 Regulatory Network Analysis in Colorectal Cancer (CRC) Reveals That BGN Influences CRC Cell Biological Functions and Interacts with miR-6828-5p. Cancer Manag Res 2020; 12:13051-13069. [PMID: 33376399 PMCID: PMC7764722 DOI: 10.2147/cmar.s277261] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [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: 08/21/2020] [Accepted: 11/19/2020] [Indexed: 12/20/2022] Open
Abstract
Purpose We explored specific expression profiles of BGN and COL11A1 genes and studied their biological functions in CRC using bioinformatics tools. Patients and Methods A total of 68 pairs of cancer and non-cancerous tissues from CRC patients were enrolled in this study. Methods we used in this articles including: qRT-PCR, Western blot analysis, ELISA, GO and KEGG regulatory network analysis, tumor infiltration, luciferase reporter-based protein and etc. Results According to The Cancer Genome Atlas (TCGA) data, BGN and COL11A1 expression levels were significantly higher in CRC patient samples than in samples from healthy controls. Moreover, levels were much higher in late-stage CRC than in early-stage disease, warranting evaluation of these genes as CRC prognostic biomarkers. Subsequently, qRT-PCR, Western blot analysis, and ELISA results obtained from analyses of CRC cells, tissues, and patient sera aligned with TCGA results. GO and KEGG regulatory network analysis revealed BGN- and COL11A1-associated genes that were functionally related to extracellular matrix (ECM) receptor pathway activation, with transcription factor genes RELA and NFKB1 positively associated with BGN expression and CEBPZ and SIRT1 with COL11A1 expression. Meanwhile, BGN and COL11A1 expression were separately and significantly correlated to tumor infiltration by six immune cell types. Additionally, kinase genes PLK1 and LYN appeared to be downstream targets of differentially expressed BGN and COL11A1, respectively. In addition, the expression of PLK1 mRNA was down-regulated while BGN was down-regulated. Finally, BGN effects on CRC cell proliferation, cycle, apoptosis, invasion, and migration were studied using molecular biological methods, including luciferase reporter-based protein analysis, qRT-PCR, and Western blot results, which revealed that miR-6828-5p may regulate BGN expression. Conclusion We speculate that the use of BGN and COL11A1 as CRC biomarkers would improve CRC staging, while also providing several novel targets for use in the development of more effective CRC treatments.
Collapse
Affiliation(s)
- Danqi Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, People's Republic of China
| | - Ying Qin
- Department of Gastrointestinal Surgery, Shenzhen Second People's Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Mengmeng Dai
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, People's Republic of China
| | - Lulu Li
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, People's Republic of China
| | - Hongpeng Liu
- Department of Gastrointestinal Surgery, Shenzhen Second People's Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Yaoyao Zhou
- National & Local United Engineering Laboratory for Personalized Anti-Tumor Drugs, Shenzhen Kivita Innovative Drug Discovery Institute, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, People's Republic of China
| | - Cheng Qiu
- National & Local United Engineering Laboratory for Personalized Anti-Tumor Drugs, Shenzhen Kivita Innovative Drug Discovery Institute, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, People's Republic of China
| | - Yan Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, People's Republic of China
| | - Yuyang Jiang
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, People's Republic of China.,School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, People's Republic of China
| |
Collapse
|
21
|
Zhou G, Xiao K, Gong G, Wu J, Zhang Y, Liu X, Jiang Z, Ma C. A novel nomogram for predicting liver metastasis in patients with gastrointestinal stromal tumor: a SEER-based study. BMC Surg 2020; 20:298. [PMID: 33238982 PMCID: PMC7689971 DOI: 10.1186/s12893-020-00969-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [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: 08/02/2020] [Accepted: 11/17/2020] [Indexed: 02/07/2023] Open
Abstract
Background Liver metastasis (LIM) of gastrointestinal stromal tumor (GIST) is associated with poor prognosis. The present study aimed at developing and validating nomogram to predict LIM in patients with GIST, thus helping clinical diagnosis and treatment. Methods The data of GIST patients derived from Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2016, which were then screened by univariate and multivariate logistic regression for the construction of LIM nomogram. The model discrimination of LIM nomogram was evaluated by concordance index (C-index) and calibration plots, while the predictive accuracy and clinical values were measured by decision curve analysis (DCA) and clinical impact plot. Furthermore, we validated predictive nomogram in the internal testing set. Results A total of 3797 patients were enrolled and divided randomly into training and validating groups in a 3-to-1 ratio. After logistic regression, the significant variables were sex, tumor location, tumor size, N stage and mitotic rate. The calibration curves showed the perfect agreement between nomogram predictions and actual observations, while the DCA and clinical impact plot showed the clinical utility of LIM nomogram. C-index of the nomogram was 0.812. What’s more, receiver operating characteristic curves (ROC) also showed good discrimination and calibration in the training set (AUC = 0.794, 95% CI 0.778–0.808) and the testing set (AUC = 0.775, 95% CI 0.748–0.802). Conclusion The nomogram for patients with GIST can effectively predict the individualized risk of liver metastasis and provide insightful information to clinicians to optimize therapeutic regimens.
Collapse
Affiliation(s)
- Guowei Zhou
- Department of General Surgery, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu Province, China
| | - Keshuai Xiao
- Department of General Surgery, Xinyang Central Hospital, Xin Yang, 464000, Henan Province, China
| | - Guanwen Gong
- Department of General Surgery, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu Province, China.
| | - Jiabao Wu
- Department of Pediatrics, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu Province, China
| | - Ya Zhang
- Department of Gynecology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu Province, China
| | - Xinxin Liu
- Department of General Surgery, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu Province, China
| | - Zhiwei Jiang
- Department of General Surgery, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu Province, China
| | - Chaoqun Ma
- Department of General Surgery, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu Province, China.
| |
Collapse
|
22
|
Zhou J, Zhou J, Wu ZQ, Goyal H, Xu HG. A novel prognostic model for adult patients with Hemophagocytic Lymphohistiocytosis. Orphanet J Rare Dis 2020; 15:215. [PMID: 32819431 PMCID: PMC7439554 DOI: 10.1186/s13023-020-01496-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [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: 05/24/2020] [Accepted: 08/07/2020] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Hemophagocytic Lymphohistiocytosis (HLH) is a type of rare disease with low survival rate. We aimed to develop a model to evaluate the six-month prognosis in adult HLH patients. The data at discharge (will be called as post-treatment) for newly diagnosed adult HLH patients was collected and independent prognostic variables were selected for inclusion in the model. RESULTS Three laboratory markers were confirmed to be the independent risk factors (ferritin: hazard ratio (HR) 0.101, 95% confidence interval (CI) 0.036-0.282, P<0.001; platelets: HR 4.799, 95% CI 1.884-12.223, P = 0.001; alanine aminotransferase (ALT): HR 0.423, 95% CI 0.180-0.997, P = 0.049). These were included in the final clinical prediction model. Receiver operating characteristic (ROC) curves disclosed that this model had a better discrimination (area under the curve (AUC) = 0.842, 95% CI 0.773-0.910, P < 0.001) than each of them alone and the calibration curves aligned completely with the model predictions and actual observations. Kaplan-Meier curves revealed a significant difference in the overall survival (OS) in patients stratified by the model with higher values associated with a better OS. CONCLUSION These results point out that serum ferritin, platelets and ALT levels are independent elements of OS in adult patients with HLH, and that the proposed model have a better prognostic value than any of these markers alone.
Collapse
Affiliation(s)
- Jun Zhou
- Department of Laboratory Medicine, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jing Zhou
- Department of Laboratory Medicine, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Zhi-Qi Wu
- Department of Laboratory Medicine, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Hemant Goyal
- The Wright Center of Graduate Medical Education, Scranton, USA
| | - Hua-Guo Xu
- Department of Laboratory Medicine, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
| |
Collapse
|
23
|
Chen K, Deng X, Yang Z, Yu D, Zhang X, Zhang J, Xie D, He Z, Cheng D. Survival nomogram for patients with metastatic siewert type II adenocarcinoma of the esophagogastric junction: a population-based study. Expert Rev Gastroenterol Hepatol 2020; 14:757-764. [PMID: 32552040 DOI: 10.1080/17474124.2020.1784726] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND The aim of this study was to construct a nomogram to predict the survival of patients with metastatic Siewert Type II adenocarcinomas of the esophagogastric junction (AEG). METHODS Patients were identified using the Surveillance, Epidemiology, and End Results (SEER) database. Cox regression analysis was performed to assess the prognostic factors. A nomogram comprising independent prognostic factors was established and evaluated using C-indexes, calibration curves, and decision curve analyses. RESULTS In total 1616 eligible patients were enrolled. Race, age, bone metastasis, liver metastasis, lung metastasis, other metastasis sites, and distant lymph nodes metastasis were independent prognostic factors and were integrated to construct the nomogram. The nomogram had a C-index of 0.590 (95% CI: 0.569-0.611) in the training cohort and 0.569 (95% CI: 0.532-0.606) in the validation cohort. The calibration plots for the probabilities of 6-month and 1-year overall survival demonstrated there was an optimum between nomogram prediction and actual observation. CONCLUSION We developed and validated a nomogram to predict individual prognosis for patients with metastatic Siewert Type II AEG, and the risk stratification system based on the nomogram could effectively stratify the patients into two risk subgroups, which can help clinicians accurately predict mortality risk and recommend personalized treatment modalities.
Collapse
Affiliation(s)
- Kun Chen
- Department of Thoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University , Wenzhou, China
| | - Xiaofang Deng
- Department of Thoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University , Wenzhou, China
| | - Zhihao Yang
- Department of Thoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University , Wenzhou, China
| | - Dongdong Yu
- Department of Urology, The First Affiliated Hospital of Wenzhou Medical University , Wenzhou, China
| | - Xiang Zhang
- Department of Thoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University , Wenzhou, China
| | - Jiandong Zhang
- Department of Thoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University , Wenzhou, China
| | - Deyao Xie
- Department of Thoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University , Wenzhou, China
| | - Zhifeng He
- Department of Thoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University , Wenzhou, China
| | - Dezhi Cheng
- Department of Thoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University , Wenzhou, China
| |
Collapse
|
24
|
Guo K, Feng Y, Yuan L, Wasan HS, Sun L, Shen M, Ruan S. Risk factors and predictors of lymph nodes metastasis and distant metastasis in newly diagnosed T1 colorectal cancer. Cancer Med 2020; 9:5095-5113. [PMID: 32469151 PMCID: PMC7367623 DOI: 10.1002/cam4.3114] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [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: 02/14/2020] [Revised: 04/09/2020] [Accepted: 04/22/2020] [Indexed: 12/13/2022] Open
Abstract
Background Lymph nodes metastasis (LNM) and distant metastasis (DM) are important prognostic factors in colorectal cancer (CRC) and determine the following treatment approaches. We aimed to find clinicopathological factors associated with LNM and DM, and analyze the prognosis of CRC patients with T1 stage. Methods A total of 17 516 eligible patients with T1 CRC were retrospectively enrolled in the study based on the Surveillance, Epidemiology, and End Results (SEER) database during 2004‐2016. Logistic regression analysis was performed to identify risk factors for LNM and DM. Unadjusted and adjusted Cox proportional hazard models were used to identify prognostic factors for overall survival. We performed the cumulative incidence function (CIF) to further determine the prognostic role of LNM and DM in colorectal cancer‐specific death (CCSD). LNM, DM, and OS nomogram were constructed based on these models and evaluated by the C‐index and calibration plots for discrimination and accuracy, respectively. The clinical utility of the nomograms was measured by decision curve analyses (DCAs) and subgroups with different risk scores. Results Tumor grade, mucinous adenocarcinoma, and age accounted for the first three largest proportion among the LNM nomogram scores (all, P < .001), whereas N stage, carcinoembryonic antigen (CEA), and tumor size occupied the largest percentage in DM nomogram (all, P < .001). OS nomogram was formulated to visually to predict 3‐, 5‐, and 10‐ year overall survivals for patients with T1 CRC. The calibration curves showed an effectively predictive accuracy of prediction nomograms, of which the C‐index were 0.666, 0.874, and 0.760 for good discrimination, respectively. DCAs and risk subgroups revealed the clinical effectiveness of these nomograms. Conclusions Novel population‐based nomograms for T1 CRC patients could objectively and accurately predict the risk of LNM and DM, as well as OS for different stages. These predictive tools may help clinicians to make individual clinical decisions, before clinical management.
Collapse
Affiliation(s)
- Kaibo Guo
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, P.R. China
| | - Yuqian Feng
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, P.R. China
| | - Li Yuan
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, P.R. China
| | - Harpreet S Wasan
- Department of Cancer Medicine, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Leitao Sun
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, P.R. China.,Department of Medical Oncology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, P.R. China
| | - Minhe Shen
- Department of Medical Oncology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, P.R. China
| | - Shanming Ruan
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, P.R. China.,Department of Medical Oncology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, P.R. China
| |
Collapse
|
25
|
Liu H, Yan Y, Chen R, Zhu M, Lin J, He C, Shi B, Wen K, Mao K, Xiao Z. Integrated nomogram based on five stage-related genes and TNM stage to predict 1-year recurrence in hepatocellular carcinoma. Cancer Cell Int 2020; 20:140. [PMID: 32368186 PMCID: PMC7189530 DOI: 10.1186/s12935-020-01216-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [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: 03/03/2020] [Accepted: 04/16/2020] [Indexed: 12/24/2022] Open
Abstract
Background The primary tumor, regional lymph nodes and distant metastasis (TNM) stage is an independent risk factor for 1-year hepatocellular carcinoma (HCC) recurrence but has insufficient predictive efficiency. We attempt to develop and validate a nomogram to predict 1-year recurrence in HCC and improve the predictive efficiency of the TNM stage. Methods A total of 541 HCC patients were enrolled in the study. The risk score (RS) model was established with the logistic least absolute shrinkage and selector operation algorithm. The predictive nomogram was further validated in the internal testing cohort and external validation cohort. The area under the receiver operating characteristic curves (AUCs), decision curves and clinical impact curves were used to evaluate the predictive accuracy and clinical value of the nomogram. Results In the training cohort, we identified a RS model consisting of five stage-related genes (NUP62, EHMT2, RANBP1, MSH6 and FHL2) for recurrence at 1 year. The 1-year disease-free survival of patients was worse in the high-risk group than in the low-risk group (P < 0.0001), and 1-year recurrence was more likely in the high-risk group (Hazard ratio: 3.199, P < 0.001). The AUC of the nomogram was 0.739, 0.718 and 0.693 in the training, testing and external validation cohort, respectively, and these values were larger than the corresponding AUC of the TNM stage (0.681, 0.688 and 0.616, respectively). Conclusions A RS model consisting of five stage-related genes was successfully identified for predicting 1-year HCC recurrence. Then, a novel nomogram based on the RS model and TNM stage to predict 1-year HCC recurrence was also developed and validated.
Collapse
Affiliation(s)
- Haohan Liu
- 1Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120 China.,2Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120 China
| | - Yongcong Yan
- 1Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120 China.,2Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120 China
| | - Ruibing Chen
- 1Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120 China.,2Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120 China
| | - Mengdi Zhu
- 1Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120 China
| | - Jianhong Lin
- 1Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120 China.,2Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120 China
| | - Chuanchao He
- 2Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120 China
| | - Bingchao Shi
- 2Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120 China
| | - Kai Wen
- 2Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120 China
| | - Kai Mao
- 2Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120 China
| | - Zhiyu Xiao
- 2Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120 China
| |
Collapse
|
26
|
Shi N, Zou Y, Zhang Y, Han H, Chen Z, Ruan S, Jin L, Ma Z, Chen Z, Lou Q, Jin H. Construction of Nomograms for Predicting Lung and Bone Metastases in Patients with Intrahepatic Cholangiocarcinoma and Identification of Patients Who Can Benefit from Chemotherapy. J Oncol 2020; 2020:8889571. [PMID: 33343665 PMCID: PMC7725572 DOI: 10.1155/2020/8889571] [Citation(s) in RCA: 2] [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] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 11/10/2020] [Accepted: 11/18/2020] [Indexed: 02/05/2023]
Abstract
OBJECTIVE The purpose of our study is to build nomograms for predicting the possibility of lung metastasis (LM) and bone metastasis (BM) in patients with intrahepatic cholangiocarcinoma (ICC). METHODS 1527 patients diagnosed with ICC between 2010 and 2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariable and multivariable logistic regression analyses were used to recognize the predictors of LM and BM, respectively. Then two nomograms were established. We applied the C-index, calibration plot, receiver-operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the novel nomograms. The maximum values of the Youden indexes from the ROC curves were utilized to select the cutoff points of the nomograms. The Kaplan-Meier survival curves were used to evaluate the effect of chemotherapy in different groups. The bootstrap resampling method was chosen for internal validation. RESULTS Five predictors for LM and three predictors for BM were identified, and two nomograms were constructed. The nomograms had high values of C-indexes, reaching 0.821 (95% CI 0.772-0.871) for LM and 0.759 (95% CI 0.700-0.818) for BM. C-indexes of 0.814 for LM and 0.749 for BM were also observed in internal validation. The calibration plots, ROC curves, and DCAs exhibited favorable performances for predicting LM and BM. The cutoff points of total points in nomograms were 108 for LM and 144 for BM, which could distinguish between high-risk and low-risk groups for LM and BM. Chemotherapy is suggested to undergo for patients in high-risk groups. CONCLUSIONS The nomograms could assess the possibility of LM and BM in ICC patients and determine the optimal treatment.
Collapse
Affiliation(s)
- Ning Shi
- Department of General Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yiping Zou
- Department of General Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Yuanpeng Zhang
- Department of General Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Hongwei Han
- Department of General Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Zhihong Chen
- Department of General Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Shiye Ruan
- Department of General Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Liang Jin
- Department of General Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zuyi Ma
- Department of General Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Zhenrong Chen
- Department of General Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Qi Lou
- Department of General Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou,China
| | - Haosheng Jin
- Department of General Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| |
Collapse
|
27
|
Yan Q, Zheng W, Luo H, Wang B, Zhang X, Wang X. Incidence and survival trends for appendiceal mucinous adenocarcinoma: an analysis of 3237 patients in the Surveillance, Epidemiology, and End Results database. Future Oncol 2019; 15:3945-3961. [PMID: 31746646 DOI: 10.2217/fon-2019-0229] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: To characterize the epidemiology of appendiceal mucinous adenocarcinoma. Methods: Prognostic factors were evaluated with univariate and multivariate analyses. The results were used to generate a nomogram. Results: The incidence of appendiceal mucinous adenocarcinoma showed a significant upward trend. Multivariate Cox analysis identified 11 independent prognostic factors. The nomogram was based on independent risk factors that were significant in multivariate Cox analysis, and the concordance-index for overall survival and cancer-specific survival were 0.76 (95% CI: 0.71-0.79) and 0.74 (95% CI: 0.70-0.79), respectively. Conclusion: Advanced age, single relationship status, male sex, black race, the presence of distant and regional lymph node metastases, poor differentiation or lack of differentiation, advanced SEER extent of disease, cancer-directed surgery and chemotherapy were independently associated with prognosis.
Collapse
Affiliation(s)
- Qian Yan
- The First Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, 510400, PR China
| | - Wenjiang Zheng
- The First Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, 510400, PR China
| | - Huiyan Luo
- The First Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, 510400, PR China
| | - Boqing Wang
- The First Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, 510400, PR China
| | - Xiaoying Zhang
- The First Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, 510400, PR China
| | - Xiongwen Wang
- Department of Oncology,The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, PR China
| |
Collapse
|