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Zhou X, Qian Y, Ling C, He Z, Shi P, Gao Y, Sui X. An integrated framework for prognosis prediction and drug response modeling in colorectal liver metastasis drug discovery. J Transl Med 2024; 22:321. [PMID: 38555418 PMCID: PMC10981831 DOI: 10.1186/s12967-024-05127-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 03/23/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is the third most prevalent cancer globally, and liver metastasis (CRLM) is the primary cause of death. Hence, it is essential to discover novel prognostic biomarkers and therapeutic drugs for CRLM. METHODS This study developed two liver metastasis-associated prognostic signatures based on differentially expressed genes (DEGs) in CRLM. Additionally, we employed an interpretable deep learning model utilizing drug sensitivity databases to identify potential therapeutic drugs for high-risk CRLM patients. Subsequently, in vitro and in vivo experiments were performed to verify the efficacy of these compounds. RESULTS These two prognostic models exhibited superior performance compared to previously reported ones. Obatoclax, a BCL-2 inhibitor, showed significant differential responses between high and low risk groups classified by prognostic models, and demonstrated remarkable effectiveness in both Transwell assay and CT26 colorectal liver metastasis mouse model. CONCLUSIONS This study highlights the significance of developing specialized prognostication approaches and investigating effective therapeutic drugs for patients with CRLM. The application of a deep learning drug response model provides a new drug discovery strategy for translational medicine in precision oncology.
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Affiliation(s)
- Xiuman Zhou
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong Province, 518107, China
| | - Yuzhen Qian
- School of Life Sciences, Zhengzhou University, Zhengzhou, 450001, China
| | - Chen Ling
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong Province, 518107, China
| | - Zhuoying He
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong Province, 518107, China
| | - Peishang Shi
- School of Life Sciences, Zhengzhou University, Zhengzhou, 450001, China
| | - Yanfeng Gao
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong Province, 518107, China.
| | - Xinghua Sui
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong Province, 518107, China.
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Wu X, Zhang X, Ge J, Li X, Shi C, Zhang M. Development and validation of a prognostic model for esophageal cancer patients with liver metastasis: a cohort study based on surveillance, epidemiology, and end results database. J Cancer Res Clin Oncol 2023; 149:13501-13510. [PMID: 37493687 DOI: 10.1007/s00432-023-05175-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 07/10/2023] [Indexed: 07/27/2023]
Abstract
PURPOSE Our objective is to examine the independent prognostic risk factors for patients with Esophageal Cancer with Liver Metastasis (ECLM) and to develop a predictive model. METHODS In this study, clinical data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Cox regression analysis was employed to identify independent prognostic factors and construct nomograms based on the results of multivariate regression. The predictive performance of the nomograms was assessed using several methods, including the consistency index (C-index), calibration curve, time-dependent receiver-operating characteristic curve (ROC), and decision curve analysis (DCA). Additionally, Kaplan-Meier survival curves were generated to demonstrate the variation in overall survival between groups. RESULTS A total of 1163 ECLM patients were included in the study. Multivariate Cox analysis revealed that age, tumor differentiation grade, bone metastasis, therapy, and income were independently associated with overall survival (OS) in the training set. Subsequently, a prognostic nomogram was constructed based on these independent predictors. The C-index values were 0.739 and 0.715 in the training and validation sets, respectively. The area under the curve (AUC) values at 0.5, 1, and 2 years were all higher than 0.700. Calibration curves indicated that the nomogram accurately predicted OS. Decision curve analysis (DCA) showed moderately positive net benefits. Kaplan-Meier survival curves demonstrated significant differences in survival between high- and low-risk groups, which were divided based on the nomogram risk score. CONCLUSIONS The nomogram we developed for ECLM patients has demonstrated good predictive capability, allowing clinicians to accurately evaluate patient prognosis and identify those at high risk, thereby facilitating the development of personalized treatment plans.
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Affiliation(s)
- Xiaolong Wu
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, People's Republic of China
| | - Xudong Zhang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, People's Republic of China
| | - Jingjing Ge
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, People's Republic of China
| | - Xin Li
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, People's Republic of China
| | - Cunzhen Shi
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, People's Republic of China
| | - Mingzhi Zhang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, People's Republic of China.
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Pîrvu EE, Severin E, Pătru RI, Niță I, Toma SA, Macarie RR, Cocioabă CE, Florescu I, Coniac S. Correlations between Demographic, Clinical, and Paraclinical Variables and Outcomes in Patients with KRAS-Mutant or KRAS Wild-Type Metastatic Colorectal Cancer-A Retrospective Study from a Tertiary-Level Center in Romania. Diagnostics (Basel) 2023; 13:2930. [PMID: 37761297 PMCID: PMC10528401 DOI: 10.3390/diagnostics13182930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/16/2023] [Accepted: 09/10/2023] [Indexed: 09/29/2023] Open
Abstract
Colorectal cancer (CRC) is a significant global public health concern and its characteristics in Eastern Europe are underexplored. In this retrospective study, data of 225 patients with metastatic colorectal cancer (mCRC) from the Colțea Clinical Hospital's Oncology Department in Bucharest were analyzed between 2015 and 2023. They were divided into two groups based on the presence of KRAS mutation. The primary objective of the study was to investigate whether the presence of KRAS mutations influenced the prognosis of mCRC and to identify any demographic, clinical, or paraclinical factors associated with KRAS mutations in stage IV CRC. The overall survival for the entire study population was 29 months. There was a trend towards increased survival in the KRAS wild-type group (31 months) compared to the KRAS-mutant group (26 months), but this difference did not reach statistical significance. We found that lower levels of education, advanced T stage, advanced N stage, and M1 stage at diagnosis negatively impacted prognosis. Real-world data are crucial in shaping public policy strategies to better support patients with metastatic CRC. Understanding the correlations between the demographic, clinical, and paraclinical variables and the outcomes in mCRC patients with KRAS-mutant and KRAS wild-type colorectal cancer is essential for improving patient care and treatment strategies in Romania and beyond.
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Affiliation(s)
- Edvina Elena Pîrvu
- Department of Genetics, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Department of Medical Oncology, “Coltea” Clinical Hospital, 030167 Bucharest, Romania
| | - Emilia Severin
- Department of Genetics, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania
| | - Raluca Ileana Pătru
- Department of Medical Oncology, “Coltea” Clinical Hospital, 030167 Bucharest, Romania
| | - Irina Niță
- Department of Medical Oncology, Medicover Hospital, 020331 Bucharest, Romania
| | - Stefania Andreea Toma
- Department of Medical Oncology, Ponderas Academic Hospital, 014142 Bucharest, Romania
| | - Roxana Rodica Macarie
- Department of Medical Oncology, “Coltea” Clinical Hospital, 030167 Bucharest, Romania
| | | | - Ioana Florescu
- Department of Medical Oncology, “Coltea” Clinical Hospital, 030167 Bucharest, Romania
| | - Simona Coniac
- Department of Medical Oncology, “Coltea” Clinical Hospital, 030167 Bucharest, Romania
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Ku CY, Yang XK, Xi LJ, Wang RZ, Wu BB, Dai M, Liu L, Ping ZG. Competing risks analysis of external versus internal radiation in patients with hepatocellular carcinoma after controlling for immortal time bias. J Cancer Res Clin Oncol 2023; 149:9927-9935. [PMID: 37249648 DOI: 10.1007/s00432-023-04915-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 05/22/2023] [Indexed: 05/31/2023]
Abstract
PURPOSE In cohort studies on liver cancer, there are often immortal time bias and interference of competing risk events. This study proposes to explore the role of internal and external radiotherapy for hepatocellular carcinoma using SEER data, using a competing risk model and controlling immortal time bias. METHODS Data of SEER from 2004 till 2015 was included. To analyze whether there was a difference in survival between HCC (hepatocellular carcinoma) patients receiving external radiation and internal radiation, we used a competing risk analysis after excluding immortal time bias, and created a nomogram to assess the risk of cancer-specific death (CSD) in hepatocellular carcinoma patients receiving radiotherapy. RESULTS Potential confounding factors adjusted, there was no significant difference in CSD between external and internal radiation therapy [HR and its 95% CI = 1.098 (0.874-1.380)]. The constructed nomogram performed better than the traditional AJCC model. The AUC and calibration curve results showed that this well-calibrated nomogram could be used to make clinical decisions regarding the prognosis and personalized treatment of hepatocellular carcinoma treated. There was no difference in the cumulative risk of death between patients with liver cancer treated with external radiation therapy and internal radiation therapy. CONCLUSION There is no difference in the cumulative risk of death between patients with liver cancer treated with external radiation therapy and internal radiation therapy. The nomogram predicts the results more accurately. These results can be used to guide the choice of treatment options for patients with HCC and to predict their survival prognosis.
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Affiliation(s)
- Chao-Yue Ku
- Department of Health Statistics, College of Public Health, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, 450001, Henan Province, People's Republic of China
| | - Xue-Ke Yang
- Department of Health Statistics, College of Public Health, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, 450001, Henan Province, People's Republic of China
| | - Li-Jing Xi
- Department of Health Statistics, College of Public Health, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, 450001, Henan Province, People's Republic of China
| | - Rui-Zhe Wang
- Department of Health Statistics, College of Public Health, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, 450001, Henan Province, People's Republic of China
| | - Bin-Bin Wu
- Department of Health Statistics, College of Public Health, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, 450001, Henan Province, People's Republic of China
| | - Man Dai
- Department of Health Statistics, College of Public Health, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, 450001, Henan Province, People's Republic of China
| | - Li Liu
- School of Basic Medical Sciences, Zhengzhou University, No.100 Science Avenue, Zhengzhou, 450001, Henan Province, People's Republic of China.
| | - Zhi-Guang Ping
- Department of Health Statistics, College of Public Health, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, 450001, Henan Province, People's Republic of China.
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Knewitz D, Almerey T, Gabriel E. A narrative review of prognostic indices in the evaluation of gastrointestinal cancers. J Gastrointest Oncol 2023; 14:1849-1855. [PMID: 37720450 PMCID: PMC10502552 DOI: 10.21037/jgo-23-159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/26/2023] [Indexed: 09/19/2023] Open
Abstract
Background and Objective Accurate cancer prognostication allows for conscious decision-making. There is a need for precise indices, along with predictive biomarkers, which aid cancer prognostication. We sought to conduct an overview of the current state of prognostic indices and biomarkers in the evaluation of gastrointestinal (GI) cancers, specifically esophageal, colon and rectal. Methods We conducted a comprehensive review of articles in the PubMed database between September 2001 and February 2022. Only articles written in English were included. We reviewed retrospective analyses and prospective observational studies. Key Content and Findings Nomograms are well-described tools that provide estimates of specific cancer-related events, such as overall survival (OS). They are also useful in unroofing specific patient-related variables, which may be associated with cancer survival. Certain prognostic indices have been tested against each other with the goal of discerning superiority. Finally, specific biomarkers have emerged as promising prognostic indicators. Conclusions Nomograms play a significant role in the prognostication of GI cancer. The identification of specific biomarkers in cancer prognostication is evolving. As we embark on the era of precision medicine, further investigation of reliable prognostic indices and biomarkers is needed.
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Affiliation(s)
| | | | - Emmanuel Gabriel
- Mayo Clinic, Jacksonville, FL, USA
- Department of Surgical Oncology, Mayo Clinic, Jacksonville, FL, USA
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Ma Z, Yang S, Yang Y, Luo J, Zhou Y, Yang H. Development and validation of prediction models for the prognosis of colon cancer with lung metastases: a population-based cohort study. Front Endocrinol (Lausanne) 2023; 14:1073360. [PMID: 37583430 PMCID: PMC10424923 DOI: 10.3389/fendo.2023.1073360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 04/20/2023] [Indexed: 08/17/2023] Open
Abstract
Background Current studies on the establishment of prognostic models for colon cancer with lung metastasis (CCLM) were lacking. This study aimed to construct and validate prediction models of overall survival (OS) and cancer-specific survival (CSS) probability in CCLM patients. Method Data on 1,284 patients with CCLM were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly assigned with 7:3 (stratified by survival time) to a development set and a validation set on the basis of computer-calculated random numbers. After screening the predictors by the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression, the suitable predictors were entered into Cox proportional hazard models to build prediction models. Calibration curves, concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were used to perform the validation of models. Based on model-predicted risk scores, patients were divided into low-risk and high-risk groups. The Kaplan-Meier (K-M) plots and log-rank test were applied to perform survival analysis between the two groups. Results Building upon the LASSO and multivariate Cox regression, six variables were significantly associated with OS and CSS (i.e., tumor grade, AJCC T stage, AJCC N stage, chemotherapy, CEA, liver metastasis). In development, validation, and expanded testing sets, AUCs and C-indexes of the OS and CSS prediction models were all greater than or near 0.7, which indicated excellent predictability of models. On the whole, the calibration curves coincided with the diagonal in two models. DCA indicated that the models had higher clinical benefit than any single risk factor. Survival analysis results showed that the prognosis was worse in the high-risk group than in the low-risk group, which suggested that the models had significant discrimination for patients with different prognoses. Conclusion After verification, our prediction models of CCLM are reliable and can predict the OS and CSS of CCLM patients in the next 1, 3, and 5 years, providing valuable guidance for clinical prognosis estimation and individualized administration of patients with CCLM.
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Affiliation(s)
| | | | | | | | | | - Huiyong Yang
- School of Medicine, Huaqiao University, Quanzhou, China
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Zhou H, Gao P, Liu F, Shi L, Sun L, Zhang W, Xu X, Liu X. Development and validation of a novel nomogram to predict the overall survival of patients with large cell lung cancer: A surveillance, epidemiology, and end results population-based study. Heliyon 2023; 9:e15924. [PMID: 37223713 PMCID: PMC10200837 DOI: 10.1016/j.heliyon.2023.e15924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 04/12/2023] [Accepted: 04/26/2023] [Indexed: 05/25/2023] Open
Abstract
Background Large cell lung cancer (LCLC) is a rare subtype of non-small cell lung carcinoma (NSCLC), and little is known about its clinical and biological characteristics. Methods LCLC patient data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. All patients were randomly divided into a training group and a validation group at a ratio of 7:3. The independent prognostic factors that were identified (P < 0.01) by stepwise multivariate Cox analysis were incorporated into an overall survival (OS) prediction nomogram, and risk-stratification systems, C-index, time-ROC, calibration curve, and decision curve analysis (DCA) were applied to evaluate the quality of the model. Results Nine factors were incorporated into the nomogram: age, sex, race, marital status, 6th AJCC stage, chemotherapy, radiation, surgery and tumor size. The C-index of the predicting OS model in the training dataset and in the test dataset was 0.757 ± 0.006 and 0.764 ± 0.009, respectively. The time-AUCs exceeded 0.8. The DCA curve showed that the nomogram has better clinical value than the TNM staging system. Conclusions Our study summarized the clinical characteristics and survival probability of LCLC patients, and a visual nomogram was developed to predict the 1-year, 3-year and 5-year OS of LCLC patients. This provides more accurate OS assessments for LCLC patients and helps clinicians make personal management decisions.
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Affiliation(s)
- Hongxia Zhou
- Department of Nephrology, The 908th Hospital of the People's Liberation Army Joint Logistics Support Force, The Great Wall Affiliated Hospital of Nanchang University, Nanchang City, Jiangxi 330006, China
| | - Pengxiang Gao
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Nanchang University, Nanchang City, Jiangxi Province 330006, China
| | - Fangpeng Liu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Nanchang University, Nanchang City, Jiangxi Province 330006, China
| | - Liangliang Shi
- Medical Center of Burn Plastic and Wound Repair, The First Affiliated Hospital of Nanchang University, Nanchang City, Jiangxi Province 330006, China
| | - Longhua Sun
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Nanchang University, Nanchang City, Jiangxi Province 330006, China
| | - Wei Zhang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Nanchang University, Nanchang City, Jiangxi Province 330006, China
- Jiangxi Institute of Respiratory Diseases, The First Affiliated Hospital of Nanchang University, Nanchang City, Jiangxi Province 330006, China
- Jiangxi Clinical Research Center for Respiratory Diseases, The First Affiliated Hospital of Nanchang University, Nanchang City, Jiangxi Province 330006, China
| | - Xinping Xu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Nanchang University, Nanchang City, Jiangxi Province 330006, China
- Jiangxi Institute of Respiratory Diseases, The First Affiliated Hospital of Nanchang University, Nanchang City, Jiangxi Province 330006, China
- Jiangxi Clinical Research Center for Respiratory Diseases, The First Affiliated Hospital of Nanchang University, Nanchang City, Jiangxi Province 330006, China
| | - Xiujuan Liu
- Department of Nephrology, The 908th Hospital of the People's Liberation Army Joint Logistics Support Force, The Great Wall Affiliated Hospital of Nanchang University, Nanchang City, Jiangxi 330006, China
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Xu Y, Han H, Cao W, Fu H, Liu Y, Yan L, Qin T. Establishment and validation of a predictive model of recurrence in primary hepatocellular carcinoma after resection. J Gastrointest Oncol 2023; 14:278-286. [PMID: 36915435 PMCID: PMC10007949 DOI: 10.21037/jgo-22-1303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 02/02/2023] [Indexed: 02/17/2023] Open
Abstract
Background In recent years, nomogram prediction models have been widely used to evaluate the prognosis of various diseases. However, studies in primary hepatocellular carcinoma (HCC) are limited. This study sought to explore the risk factors of recurrence of patients with primary HCC after surgical resection and establish a nomogram prediction model. Methods The data of 424 patients with primary HCC who had been admitted to the Wuhan Third Hospital were retrospectively collected. The patients were followed-up for 5 years after surgery. The patients were divided into the recurrence group (n=189) and control group (n=235) according to whether the cancer recurred after surgery. The differences in the clinical characteristics between the two groups were analyzed. The risk factors of recurrence after surgical resection of primary HCC were also analyzed, and a prediction model was then established using R4.0.3 statistical software. Results There were significant statistical differences between the two groups in terms of the tumor size, systemic immune-inflammation (SII) index, the number of lesions, tumor differentiation degree, ascites, vascular invasion, and portal vein tumor thrombus (P<0.05). The multivariate regression analysis showed that multiple foci, poorly differentiated tumors, ascites, vascular invasion, and portal vein tumor thrombus were risk factors for the recurrence of primary HCC in patients after surgical resection (P<0.05). The data set was randomly divided into a training set and verification set. The sample size of the training set was 297, and the sample size of the verification set was 127. The area under the receiver operating characteristic (ROC) curve of the training set was 0.866 [95% confidence interval (CI): 0.824-0.907], and the area under the ROC curve of the validation set was 0.812 (95% CI: 0.734-0.890). The Hosmer-Lemeshow Goodness-of-Fit Test was used to test the model with the validation set (χ2=11.243, P=0.188), which indicated that the model had high value in predicting the recurrence of primary HCC after surgical resection. Conclusions This model had high value in predicting the recurrence of primary HCC in patients after surgical resection. This model could assist clinicians to assess the prognosis of patients. Intensive treatment for high-risk patients might improve the prognosis of patients.
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Affiliation(s)
- Yang Xu
- Department of Integrated Traditional Chinese and Western Medicine, Wuhan Third Hospital, Wuhan, China
| | - Huimin Han
- Department of Integrated Traditional Chinese and Western Medicine, Wuhan Third Hospital, Wuhan, China
| | - Wei Cao
- Department of Integrated Traditional Chinese and Western Medicine, Wuhan Third Hospital, Wuhan, China
| | - Hongxing Fu
- Department of Integrated Traditional Chinese and Western Medicine, Wuhan Third Hospital, Wuhan, China
| | - Yang Liu
- Department of Integrated Traditional Chinese and Western Medicine, Wuhan Third Hospital, Wuhan, China
| | - Li Yan
- Department of Traditional Chinese Medicine, Wuhan Third Hospital, Wuhan, China
| | - Tingting Qin
- Department of Integrated Traditional Chinese and Western Medicine, Wuhan Third Hospital, Wuhan, China
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Establishment and Validation of a Nomogram Prognostic Model for Epithelioid Hemangioendothelioma. JOURNAL OF ONCOLOGY 2022; 2022:6254563. [PMID: 36245980 PMCID: PMC9560816 DOI: 10.1155/2022/6254563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/14/2022] [Accepted: 08/20/2022] [Indexed: 11/17/2022]
Abstract
Background. Epithelioid hemangioendothelioma (EHE) is an ultrarare vascular sarcoma. At present, the epidemiological and clinical characteristics and prognostic factors are still unclear. Our study attempted to describe clinical features, investigate the prognostic indicators, and establish the nomogram prediction model based on the Surveillance, Epidemiology, and End Results (SEER) database for EHE patients. Methods. The patients diagnosed with EHE from 1986 to 2018 were collected from the SEER database and were randomly divided into a training group and a validation group at a ratio of 7 : 3. The Cox proportional hazard models were used to determine the independent factors affecting prognosis and establish a nomogram prognostic model to predict the survival rates for patients with EHE. The accuracy and discriminative ability of the model were measured using the concordance index, receiver operating characteristic curves, and calibration curves. The clinical applicability and application value of the model were evaluated by decision curve analysis. Results. The overall age-adjusted incidence of EHE was 0.31 patients per 1,000,000 individuals, with a statistically significant difference per year. Overall survival at 1, 5, and 10 years for all patients was 76.5%, 57.4%, and 48.2%, respectively. Multivariate Cox regression analysis identified age, tumour stage, degree of tissue differentiation, surgical treatment, chemotherapy, and radiotherapy as independent factors affecting prognosis (
). The C-index values for our nomogram model of training group and validation group were 0.752 and 0.753, respectively. The calibration curve was in good agreement with the actual observation results, suggesting that the prediction model has good accuracy. The decision curve analysis indicated a relatively large net benefit. Conclusions. The nomogram model may play an important role in predicting the survival rate for EHE patients, with good concordance and accuracy, and can be applied in clinical practice.
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He Y, Xu J, Shang X, Fang X, Gao C, Sun D, Yao L, Zhou T, Pan S, Zou X, Shu H, Yang X, Shang Y. Clinical characteristics and risk factors associated with ICU-acquired infections in sepsis: A retrospective cohort study. Front Cell Infect Microbiol 2022; 12:962470. [PMID: 35967847 PMCID: PMC9366915 DOI: 10.3389/fcimb.2022.962470] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
Intensive care unit (ICU)-acquired infection is a common cause of poor prognosis of sepsis in the ICU. However, sepsis-associated ICU-acquired infections have not been fully characterized. The study aims to assess the risk factors and develop a model that predicts the risk of ICU-acquired infections in patients with sepsis.
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Affiliation(s)
- Yajun He
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Institute of Anesthesiology and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiqian Xu
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Institute of Anesthesiology and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaopu Shang
- Department of Information Management, Beijing Jiaotong University, Beijing, China
| | - Xiangzhi Fang
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chenggang Gao
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Institute of Anesthesiology and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Deyi Sun
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Institute of Anesthesiology and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lu Yao
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Institute of Anesthesiology and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ting Zhou
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shangwen Pan
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaojing Zou
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huaqing Shu
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaobo Yang
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - You Shang
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Institute of Anesthesiology and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: You Shang,
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Tai Q, Xue W, Li M, Zhuo S, Zhang H, Fang F, Zhang J. Survival Nomogram for Metastasis Colon Cancer Patients Based on SEER Database. Front Genet 2022; 13:832060. [PMID: 35222547 PMCID: PMC8864078 DOI: 10.3389/fgene.2022.832060] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 01/13/2022] [Indexed: 12/20/2022] Open
Abstract
Introduction: A prediction model for the 1-, 3-, and 5-year survival rates of metastatic colon cancer (mCC) patients was developed by analyzing important risk factors for the prognosis of mCC patients based on the SEER database. Method: The characteristic of 10,946 patients diagnosed with mCC between 2010 and 2015 was obtained from the SEER database. The population was randomly divided into a training cohort and an internal validation cohort in a 7:3 ratio. Univariate and multivariate cox for independent predictors of mCC prognosis were performed, and nomogram was constructed. The accuracy of the model was verified by calibration curves, ROC curves, and C-index, and the clinical utility of the model was analyzed using decision analysis curves. Result: Age, primary site, grade, surgery, and other eight factors were significantly associated with the prognosis of mCC patients, and these predictors were included in the construction of the nomogram. The C-index was 0.731 (95% CI 0.725–0.737) and 0.736 (95% CI 0.726–0.746) for the training cohort and the validation set, respectively. The results of the ROC curve analysis indicated that the area under the curve (AUC) exceeded 0.7 for both the training cohort and the validation set at 1, 3, and 5 years. Conclusion: The constructed prediction model had an excellent predictive accuracy, which will help clinical decision-making of mCC patients after surgery and individualized treatment.
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Affiliation(s)
- Qinwen Tai
- Department of General Surgery, Shenzhen Hospital, Southern Medical University, Shenzhen, China
- *Correspondence: Qinwen Tai, ; Jinhui Zhang,
| | - Wei Xue
- Department of Pharmacy, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, China
| | - Mengying Li
- The First College of Clinical Science, Anhui Medical University, Hefei, China
| | - Shuli Zhuo
- Medical College of Shaoguan University, Shaoguan, China
| | - Heng Zhang
- Department of General Surgery, Shenzhen Hospital, Southern Medical University, Shenzhen, China
| | - Fa Fang
- Department of General Surgery, Shenzhen Hospital, Southern Medical University, Shenzhen, China
| | - Jinhui Zhang
- Department of General Surgery, Shenzhen Hospital, Southern Medical University, Shenzhen, China
- *Correspondence: Qinwen Tai, ; Jinhui Zhang,
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Yang QY, Tang CT, Huang YF, Shao DT, Shu X. Development and validation of a nomogram for primary duodenal carcinoma: a multicenter, population-based study. Future Oncol 2022; 18:1245-1258. [PMID: 35114801 DOI: 10.2217/fon-2021-0622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Aim: This study aimed to develop a predictive model for patients with duodenal carcinoma. Methods: Duodenal carcinoma patients from the Surveillance, Epidemiology, and End Results database (2010-2015) and the First Affiliated Hospital of Nanchang University (2010-2021) were enrolled. A nomogram was constructed according to least absolute shrinkage and selection operator regression analysis, the Akaike information criterion approach and Cox regression analysis. Results: Five independent prognostic factors were significantly associated with the prognosis of the duodenal carcinoma patients. A nomogram was constructed with a C-index in the training and validation cohorts of 0.671 (95% CI: 0.578-0.716) and 0.662 (95% CI: 0.529-0.773), respectively. Conclusion: The established nomogram model provided visualization of the risk of each prognostic factor.
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Affiliation(s)
- Qin-Yu Yang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang, China.,Human Genetic Resources Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Chao-Tao Tang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang, China.,Human Genetic Resources Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yun-Feng Huang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang, China.,Human Genetic Resources Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Dan-Ting Shao
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang, China.,Human Genetic Resources Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xu Shu
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang, China.,Human Genetic Resources Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
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Li Z, Wei J, Cao H, Song M, Zhang Y, Jin Y. A predictive web-based nomogram for the early death of patients with lung adenocarcinoma and bone metastasis: a population-based study. J Int Med Res 2021; 49:3000605211047771. [PMID: 34590874 PMCID: PMC8489788 DOI: 10.1177/03000605211047771] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Objective To identify risk factors and develop predictive web-based nomograms for the early death of patients with bone metastasis of lung adenocarcinoma (LUAD). Methods Patients in the Surveillance, Epidemiology, and End Results database diagnosed with bone metastasis of LUAD between 2010 and 2016 were included and randomly divided into training and validation sets. Early death-related risk factors (survival time ≤7 months) were evaluated by logistic regression. Two predictive nomograms were established and validated by calibration curves, receiver operating characteristic curves, and decision curve analysis. Results A total of 9189 patients (56.59%) died from all causes within 7 months of being diagnosed, including 8585 patients (56.67%) who died from cancer-specific causes. Age >65 years, sex (men), T stage (T3 and T4), N stage (N2 and N3), brain metastasis, and liver metastasis were risk factors for all-cause and cancer-specific early death. The area under the curves of the nomograms for all-cause and cancer-specific early death prediction were 0.754 and 0.753 (training set) and 0.747 and 0.754 (validation set), respectively. Further analysis showed that the two nomograms performed well. Conclusions Our two web-based nomograms for all-cause and cancer-specific early death provide valuable tools for predicting early death in these patients.
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Affiliation(s)
| | | | | | | | | | - Yu Jin
- Yu Jin, Department of Traumatology and Orthopedics, Affiliated Hospital of Chengde Medical College, No. 36 Nanyingzi Street, Chengde, Hebei 067000, China.
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Guo Z, Wang Z, Liu Y, Han J, Liu J, Zhang C. Nomograms-based prediction of overall and cancer-specific survivals for patients diagnosed with major salivary gland carcinoma. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1230. [PMID: 34532367 PMCID: PMC8421927 DOI: 10.21037/atm-21-1725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 06/20/2021] [Indexed: 01/18/2023]
Abstract
Background Major salivary glands carcinoma (MSGC) is a relatively rare cancer with diverse histological types and biological behavior. The treatment planning and prognosis prediction are challenging for clinicians. The aim of the current study was to establish a reliable and effective nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) for MSGC patients. Methods Patients pathologically diagnosed with MSGC were recruited from Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation groups (7:3 ratio). Univariate, multivariate Cox proportional hazard models, and least absolute shrinkage and selection operator (LASSO) regression were adopted for the selection of risk factors. Nomograms were developed using R software. The model performance was evaluated by drawing receiver operating characteristic (ROC), overtime C-index curves, and calibration curves. Harrell C-index, areas under the curves (AUC), and Brier score were also calculated. The decision curve analysis (DCA) was conducted to measure the net clinical benefit. Results A total of 11,362 patients were identified and divided into training (n=7,953) and validation (n=3,409) dataset. Sex, age, race, marital status, site, differentiation grade, American Joint Committee on Cancer (AJCC) stage, T/N/M stage, tumor size, surgery, and histological type were incorporated into the Cox hazard model for OS prediction after variable selection, while all predictors, except for marital status and site, were selected for CSS prediction. For 5-year prediction, the AUC of the nomogram for OS and CSS was 83.5 and 82.7 in the training and validation dataset, respectively. The C-index was 0.787 for OS and 0.798 for CSS in the validation group. The Brier score was 0.0153 and 0.0130 for OS and CSS, respectively. The calibration curves showed that the nomogram had well prediction accuracy. From the perspective of DCA, a nomogram was superior to the AJCC stage and TNM stage in net benefit. In general, the performance of the nomogram was consistently better compared to the AJCC stage and TNM stage across all settings. Conclusions The performance of the novel nomogram for predicting OS and CSS of MSGC patients was further verified, revealing that it could be used as a valuable tool in assisting clinical decision-making.
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Affiliation(s)
- Zhiyong Guo
- Department of Oromaxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China
| | - Zilin Wang
- Department of Oromaxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China
| | - Yige Liu
- Department of Oromaxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China
| | - Jing Han
- Department of Oromaxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China
| | - Jiannan Liu
- Department of Oromaxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China
| | - Chenping Zhang
- Department of Oromaxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China
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