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Yu X, Dong S, Wang W, Sun X, Wang Y, Yu F. Case report: A case of recurrent cervical cancer with bronchial and esophageal metastases presenting with hemoptysis and dysphagia. Front Oncol 2024; 14:1375035. [PMID: 38706596 PMCID: PMC11066155 DOI: 10.3389/fonc.2024.1375035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/01/2024] [Indexed: 05/07/2024] Open
Abstract
Background The treatment outcomes and prognosis for recurrent cervical cancer are generally poor, with a 5-year survival rate of only 10%-20%. Case presentation In this case, the patient is a young woman who experienced a recurrence 5 years after the initial treatment of cervical cancer. Her primary symptoms were hemoptysis and dysphagia, indicative of hilar and mediastinal lymph node metastases, with further involvement of the bronchus and esophagus. Additionally, the patient also presented with tumor-associated dermatomyositis. Following combined treatment with albumin-bound paclitaxel, carboplatin, bevacizumab, and cadonilimab, the patient's tumor was effectively controlled.
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Affiliation(s)
- Xiao Yu
- Department of Gynecology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Shixiang Dong
- Department of Gynecology, The Affiliated Hospital of Qingdao University, Qingdao, China
- Medical College, Qingdao University, Qingdao, China
| | - Wenjie Wang
- Department of Gynecology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xin Sun
- Department of Gynecology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yankui Wang
- Department of Gynecology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Fengsheng Yu
- Department of Gynecology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Zeng S, Yang P, Xiao S, Liu L. Development and validation of prognostic nomographs for patients with cervical cancer: SEER-based Asian population study. Sci Rep 2024; 14:7681. [PMID: 38561337 PMCID: PMC10984919 DOI: 10.1038/s41598-024-57609-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 03/20/2024] [Indexed: 04/04/2024] Open
Abstract
To develop and validate a nomograph to predict the long-term survival probability of cervical cancer (CC) patients in Asia, Surveillance, Epidemiology, and End Results (SEER) were used to collect information about CC patients in Asia. The patient data were randomly sampled and divided into a training group and a validation group by 7:3. Least absolute shrinkage and selection operator (LASSO) regression was used to screen key indicators, and multivariate Cox regression model was used to establish a prognostic risk prediction model for CC patients. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were adopted to comprehensively evaluate the nomogram model. LASSO regression and multivariate Cox proportional hazards model analysis showed that age, American Joint Committee on Cancer (AJCC) Stage, AJCC T, tumor size, and surgery were independent risk factors for prognosis. The ROC curve results proved that the area under curve (AUC) values of the training group in 3 and 5 years were 0.837 and 0.818, The AUC values of the validation group in 3 and 5 years were 0.796 and 0.783. DCA showed that the 3- and 5-year overall survival (OS) nomograms had good clinical potential value. The nomogram model developed in this study can effectively predict the prognosis of Asian patients with CC, and the risk stratification system based on this nomogram prediction model has some clinical value for discriminating high-risk patients.
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Affiliation(s)
- Siyuan Zeng
- Department of Obstetrics and Gynecology, Dalian Municipal Central Hospital, Dalian, Liaoning, China
- Dalian Municipal Central Hospital, China Medical University, Shenyang, Liaoning, China
| | - Ping Yang
- Department of Radiation Oncology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Simin Xiao
- Department of Radiology, Chengdu Xindu District Traditional Chinese Medicine Hospital, Chengdu, Sichuan, China
| | - Lifeng Liu
- Department of Obstetrics and Gynecology, Dalian Municipal Central Hospital, Dalian, Liaoning, China.
- Dalian Municipal Central Hospital, China Medical University, Shenyang, Liaoning, China.
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Liu Y, Zhang N, Yang Q. Predicting the recurrence of usual-type cervical adenocarcinoma using a nomogram based on clinical and pathological factors: a retrospective observational study. Front Oncol 2024; 14:1320265. [PMID: 38384815 PMCID: PMC10879399 DOI: 10.3389/fonc.2024.1320265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 01/16/2024] [Indexed: 02/23/2024] Open
Abstract
Background Usual-type cervical adenocarcinoma is the most frequent type of adenocarcinoma, and its prevalence is increasing worldwide. Tumor recurrence is the leading cause of mortality; therefore, recognizing the risk factors for cervical cancer recurrence and providing effective therapy for recurrent cervical cancer are critical steps in increasing patient survival rates. This study aimed to retrospectively analyze the clinicopathological data of patients with usual-type cervical adenocarcinoma by combining the diagnosis and treatment records after the initial treatment and recurrence. Methods We retrospectively analyzed patients diagnosed with usual-type cervical adenocarcinoma who underwent radical hysterectomy and pelvic lymph node dissection at Shengjing Hospital of China Medical University between June 2013 and June 2022. We constructed a nomogram-based postoperative recurrence prediction model, internally evaluated its efficacy, and performed internal validation. Results This study included 395 participants, including 87 individuals with recurrence. At a 7:3 ratio, the 395 patients were divided into two groups: a training set (n = 276) and a validation set (n = 119). The training set was subjected to univariate analysis, and the risk variables for recurrence included smoking, ovarian metastasis, International Federation of Gynaecology and Obstetrics (FIGO) staging, lymphovascular space invasion, perineural invasion, depth of muscular invasion, tumor size, lymph node metastasis, and postoperative HPV infection months. The aforementioned components were analyzed using logistic regression analysis, and the results showed that the postoperative HPV infection month, tumor size, perineural invasion, and FIGO stage were independent risk factors for postoperative recurrence (p<0.05). The aforementioned model was represented as a nomogram. The training and validation set consistency indices, calculated using the bootstrap method of internal validation, were 0.88 and 0.86, respectively. The model constructed in this study predicted the postoperative recurrence of usual-type cervical cancer, as indicated by the receiver operating characteristic curve. The model demonstrated good performance, as evidenced by the area under the curve, sensitivity, and specificity values of 0.90, 0.859, and 0.844, respectively. Conclusion Based on the FIGO staging, peripheral nerve invasion, tumor size, and months of postoperative HPV infection, the predictive model and nomogram for postoperative recurrence of usual-type cervical adenocarcinoma are precise and effective. More extensive stratified evaluations of the risk of cervical adenocarcinoma recurrence are still required, as is a thorough assessment of postoperative recurrence in the future.
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Affiliation(s)
| | | | - Qing Yang
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
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Wang S, Wang Y, Luo J, Wang H, Zhao Y, Nie Y, Yang J. Development and validation of a prognostic nomogram for gastrointestinal stromal tumors in the postimatinib era: A study based on the SEER database and a Chinese cohort. Cancer Med 2023; 12:15970-15982. [PMID: 37329178 PMCID: PMC10469741 DOI: 10.1002/cam4.6240] [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: 02/20/2023] [Revised: 05/27/2023] [Accepted: 06/02/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND After the standardization, recording and follow-up of imatinib use that significantly prolongs survival of gastrointestinal stromal tumors (GISTs), a comprehensive reassessment of the prognosis of GISTs is necessary and more conductive to treatment options. METHODS A total of 2185 GISTs between 2013 and 2016 were obtained from the Surveillance, Epidemiology, and End Results database and comprised our training (n = 1456) and internal validation cohorts (n = 729). The risk factors extracted from univariate and multivariate analyses were used to establish a predictive nomogram. The model was evaluated and tested in the validation cohort internally and in 159 patients with GIST diagnosed between January 2015 and June 2017 in Xijing Hospital externally. RESULTS The median OS was 49 months (range, 0-83 months) in the training cohort and 51 months (0-83 months) in the validation cohort. The concordance index (C-index) of the nomogram was 0.777 (95% CI, 0.752-0.802) and 0.7787 (0.7785, bootstrap corrected) in training and internal validation cohorts, respectively, and 0.7613 (0.7579, bootstrap corrected) in the external validation cohort. Receiver operating characteristic curves and calibration curves for 1-, 3-, and 5-year overall survival (OS) showed a high degree of discrimination and calibration. The area under the curve showed that the new model performed better than the TNM staging system. In addition, the model could be dynamically visualized on a webpage. CONCLUSION We developed a comprehensive survival prediction model for assessing the 1-, 3- and 5-year OS of patients with GIST in the postimatinib era. This predictive model outperforms the traditional TNM staging system and sheds light on the improvement of the prognostic prediction and the selection of treatment strategies for GISTs.
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Affiliation(s)
- Shu Wang
- Department of Digestive SurgeryXi Jing Hospital, The Fourth Military Medical UniversityXi'anChina
| | - Yuhao Wang
- Department of Digestive SurgeryXi Jing Hospital, The Fourth Military Medical UniversityXi'anChina
| | - Jialin Luo
- Department of Digestive SurgeryXi Jing Hospital, The Fourth Military Medical UniversityXi'anChina
| | - Haoyuan Wang
- Department of Digestive SurgeryXi Jing Hospital, The Fourth Military Medical UniversityXi'anChina
| | - Yan Zhao
- Department of Digestive SurgeryXi Jing Hospital, The Fourth Military Medical UniversityXi'anChina
| | - Yongzhan Nie
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive DiseasesThe Fourth Military Medical UniversityXi'anChina
| | - Jianjun Yang
- Department of Digestive SurgeryXi Jing Hospital, The Fourth Military Medical UniversityXi'anChina
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Liu G, Yang Z, Wang D. A Bayesian network predicting survival of cervical cancer patients-Based on surveillance, epidemiology, and end results. Cancer Sci 2023; 114:1131-1141. [PMID: 36285478 PMCID: PMC9986069 DOI: 10.1111/cas.15624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/31/2022] [Accepted: 10/14/2022] [Indexed: 12/25/2022] Open
Abstract
This study aimed to build a comprehensive model for predicting the overall survival (OS) of cervical cancer patients who received standard treatments and to build a series of new stages based on the International Federation of Gynecologists and Obstetricians (FIGO) stages for better such predictions. We collected the cervical cancer patients diagnosed since the year 2000 from the Surveillance, Epidemiology, and End Results (SEER) database. Cervical cancer patients who received radiotherapy or surgery were included. Log-rank tests and Cox regression were used to identify potential factors of OS. Bayesian networks (BNs) were built to predict 3- and 5-year survival. We also grouped the patients into new stages by clustering their 5-year survival probabilities based on FIGO stage, age, and tumor differentiation. Cox regression suggested black ethnicity, adenocarcinoma, and single status as risks for poorer prognosis, in addition to age and stage. A total of 43,749 and 39,333 cases were finally eligible for the 3- and 5-year BNs, respectively, with 11 variables included. Cluster analysis and Kaplan-Meier curves indicated that it was best to divide the patients into nine modified stages. The BNs had excellent performance, with area under the curve and maximum accuracy of 0.855 and 0.804 for 3-year survival, and 0.851 and 0.787 for 5-year survival, respectively. Thus, BNs are excellent candidates for predicting cervical cancer survival. It is necessary to consider age and tumor differentiation when estimating the prognosis of cervical cancer using FIGO stages.
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Affiliation(s)
- Guangcong Liu
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute Shenyang, Shenyang, People's Republic of China
| | - Zhuo Yang
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute Shenyang, Shenyang, People's Republic of China
| | - Danbo Wang
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute Shenyang, Shenyang, People's Republic of China
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Hou C, Yin F, Liu Y. Developing and validating nomograms for predicting the survival in patients with clinical local-advanced gastric cancer. Front Oncol 2022; 12:1039498. [PMID: 36387146 PMCID: PMC9644132 DOI: 10.3389/fonc.2022.1039498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 10/14/2022] [Indexed: 12/24/2022] Open
Abstract
Background Many patients with gastric cancer are at a locally advanced stage during initial diagnosis. TNM staging is inaccurate in predicting survival. This study aims to develop two more accurate survival prediction models for patients with locally advanced gastric cancer (LAGC) and guide clinical decision-making. Methods We recruited 2794 patients diagnosed with LAGC (2010–2015) from the Surveillance, Epidemiology, and End Results (SEER) database and performed external validation using data from 115 patients with LAGC at Yantai Affiliated Hospital of Binzhou Medical University. Univariate and multifactorial survival analyses were screened for meaningful independent prognostic factors and were used to build survival prediction models. Concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were evaluated for nomograms. Finally, the differences and relationships of survival and prognosis between the three different risk groups were described using the Kaplan–Meier method. Results Cox proportional risk regression model analysis identified independent prognostic factors for patients with LAGC, and variables associated with overall survival (OS) included age, race, marital status, T-stage, N-stage, grade, histologic type, surgery, and chemotherapy. Variables associated with cancer-specific survival (CSS) included age, race, T-stage, N-stage, grade, histological type, surgery, and chemotherapy. In the training cohort, C-index of nomogram for predicting OS was 0.722 (95% confidence interval [95% CI]: 0.708–0.736] and CSS was 0.728 (95% CI: 0.713–0.743). In the external validation cohort, C-index of nomogram for predicted OS was 0.728 (95% CI:0.672–0.784) and CSS was 0.727 (95% CI:0.668–0.786). The calibration curves showed good concordance between the predicted and actual results. C-index, ROC, and DCA results indicated that our nomograms could more accurately predict OS and CSS than TNM staging and had a higher clinical benefit. Finally, to facilitate clinical use, we set up two web servers based on nomograms. Conclusion The nomograms established in this study have better risk assessment ability than the clinical staging system, which can help clinicians predict the individual survival of LAGC patients more accurately and thus develop appropriate treatment strategies.
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Affiliation(s)
- Chong Hou
- Department of Gastroenterology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
| | - Fangxu Yin
- Department of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China
| | - Yipin Liu
- Department of Gastroenterology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
- *Correspondence: Yipin Liu,
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Jiang K, Ai Y, Li Y, Jia L. Nomogram models for the prognosis of cervical cancer: A SEER-based study. Front Oncol 2022; 12:961678. [PMID: 36276099 PMCID: PMC9583406 DOI: 10.3389/fonc.2022.961678] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 09/21/2022] [Indexed: 11/29/2022] Open
Abstract
Background Cervical cancer (CC) is one of the most common cancers in women. This study aimed to investigate the clinical and non-clinical features that may affect the prognosis of patients with CC and to develop accurate prognostic models with respect to overall survival (OS) and cancer-specific survival (CSS). Methods We identified 11,148 patients with CC from the SEER (Surveillance, Epidemiology, and End Results) database from 2010 to 2016. Univariate and multivariate Cox regression models were used to identify potential predictors of patients’ survival outcomes (OS and CSS). We selected meaningful independent parameters and developed nomogram models for 1-, 3-, and 5-year OS and CSS via R tools. Model performance was evaluated by C-index and receiver operating characteristic curve. Furthermore, calibration curves were plotted to compare the predictions of nomograms with observed outcomes, and decision curve analysis (DCA) and clinical impact curves (CICs) were used to evaluate the clinical effectiveness of the nomograms. Results All eligible patients (n=11148) were randomized at a 7:3 ratio into training (n=7803) and validation (n=3345) groups. Ten variables were identified as common independent predictors of OS and CSS: insurance status, grade, histology, chemotherapy, metastasis number, tumor size, regional nodes examined, International Federation of Obstetrics and Gynecology stage, lymph vascular space invasion (LVSI), and radiation. The C-index values for OS (0.831 and 0.824) and CSS (0.844 and 0.841) in the training cohorts and validation cohorts, respectively, indicated excellent discrimination performance of the nomograms. The internal and external calibration plots indicated excellent agreement between nomogram prediction and actual survival, and the DCA and CICs reflected favorable potential clinical effects. Conclusions We constructed nomograms that could predict 1-, 3-, and 5-year OS and CSS in patients with CC. These tools showed near-perfect accuracy and clinical utility; thus, they could lead to better patient counseling and personalized and tailored treatment to improve clinical prognosis.
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Affiliation(s)
- Kaijun Jiang
- Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China
| | - Yiqin Ai
- Department of Radiation Therapy, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yanqing Li
- Department of Radiation Therapy, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
- *Correspondence: Yanqing Li, ; Lianyin Jia,
| | - Lianyin Jia
- Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China
- *Correspondence: Yanqing Li, ; Lianyin Jia,
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Huang J, Zhang Y, Zhou J, Fang M, Wu X, Luo Y, Huang Q, Ouyang Y, Xiao S. Development and validation of a prognostic nomogram for patients with stage II colon mucinous adenocarcinoma. Int J Colorectal Dis 2022; 37:2173-2184. [PMID: 36149446 DOI: 10.1007/s00384-022-04251-2] [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] [Accepted: 09/14/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE Mucinous histology is generally considered as a risk factor of prognosis in stage II colon cancer, but there is no appropriate model for prognostic evaluation and treatment decision in patients with stage II colon mucinous adenocarcinoma (C-MAC) Thus, it is urgent to develop a comprehensive, individualized evaluation tool to reflect the heterogeneity of stage II C-MAC. METHODS Patients with stage II C-MAC who underwent surgical treatment in the Surveillance, Epidemiology, and End Results Program were enrolled and randomly divided into training cohort (70%) and internal validation cohort (30%). Prognostic predictors which were determined by univariate and multivariate analysis in the training cohort were included in the nomogram. The calibration curves, decision curve analysis, X-tile analysis, and Kaplan-Meier curve of the nomogram were validated in the internal validation cohort. RESULTS Three thousand seven hundred sixty-two patients of stage II C-MAC were enrolled. The age, pathological T (pT) stage, tumor number, serum carcinoembryonic antigen (CEA), and perineural invasion (PNI) were independent predictors of overall survival (OS), which were used to establish a nomogram. Calibration curves of the nomogram indicated good consistency between nomogram prediction and actual survival for 1-, 3- and 5-year OS. Besides, patients with stage II C-MAC could be divided into high-, middle-, and low-risk subgroups by the nomogram. Further subgroup analysis indicated that patients in the high-risk group could have a survival benefit from chemotherapy after surgical treatment. CONCLUSIONS We established the first nomogram to accurately predict the survival of stage II C-MAC patients who underwent surgical treatment. In addition, the nomogram identified low-, middle-, and high-risk subgroups of patients and found chemotherapy might improve survival in the high-risk subgroup of stage II C-MAC patients.
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Affiliation(s)
- Jia Huang
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, People's Republic of China.,The First Affiliated Hospital, Department of Ultrasound Medicine, Hengyang Medical School, University of South China Hengyang, Hunan, People's Republic of China
| | - Yiwei Zhang
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, People's Republic of China
| | - Jia Zhou
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, People's Republic of China.,The First Affiliated Hospital, Department of Ultrasound Medicine, Hengyang Medical School, University of South China Hengyang, Hunan, People's Republic of China
| | - Min Fang
- The First Affiliated Hospital, Department of Gastrointestinal Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, People's Republic of China
| | - Xiaofeng Wu
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, People's Republic of China.,The First Affiliated Hospital, Department of Gastrointestinal Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, People's Republic of China
| | - Yuhang Luo
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, People's Republic of China
| | - Qiulin Huang
- The First Affiliated Hospital, Department of Gastrointestinal Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, People's Republic of China.
| | - Yujuan Ouyang
- Nuclear Industrial Hygiene School, University of South China, Hengyang, Hunan, 421001, People's Republic of China.
| | - Shuai Xiao
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, People's Republic of China. .,The First Affiliated Hospital, Department of Gastrointestinal Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, People's Republic of China.
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Jiang X, Song J, Duan S, Cheng W, Chen T, Liu X. MRI radiomics combined with clinicopathologic features to predict disease-free survival in patients with early-stage cervical cancer. Br J Radiol 2022; 95:20211229. [PMID: 35604668 PMCID: PMC10162065 DOI: 10.1259/bjr.20211229] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 03/21/2022] [Accepted: 05/06/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To establish a comprehensive model including MRI radiomics and clinicopathological features to predict post-operative disease-free survival (DFS) in early-stage (pre-operative FIGO Stage IB-IIA) cervical cancer. METHODS A total of 183 patients with early-stage cervical cancer admitted to our Jiangsu Province Hospital underwent radical hysterectomy were enrolled in this retrospective study from January 2013 to June 2018 and their clinicopathology and MRI information were collected. They were then divided into training cohort (n = 129) and internal validation cohort (n = 54). The radiomic features were extracted from the pre-operative T1 contrast-enhanced (T1CE) and T2 weighted image of each patient. Least absolute shrinkage and selection operator regression and multivariate Cox proportional hazard model were used for feature selection, and the rad-score (RS) of each patient were evaluated individually. The clinicopathology model, T1CE_RS model, T1CE + T2_RS model, and clinicopathology combined with T1CE_RS model were established and compared. Patients were divided into high- and low-risk groups according to the optimum cut-off values of four models. RESULTS T1CE_RS model showed better performance on DFS prediction of early-stage cervical cancer than clinicopathological model (C-index: 0.724 vs 0.659). T1CE+T2_RS model did not improve predictive performance (C-index: 0.671). The combination of T1CE_RS and clinicopathology features showed more accurate predictive ability (C-index=0.773). CONCLUSION The combination of T1CE_RS and clinicopathology features showed more accurate predictive performance for DFS of patients with early-stage (pre-operative IB-IIA) cervical cancer which can aid in the design of individualised treatment strategies and regular follow-up. ADVANCES IN KNOWLEDGE A radiomics signature composed of T1CE radiomic features combined with clinicopathology features allowed differentiating patients at high or low risk of recurrence.
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Affiliation(s)
- Xiaoting Jiang
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiacheng Song
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Wenjun Cheng
- Department of Gynaecology and Obstetrics, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ting Chen
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xisheng Liu
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Wang J, Ren W, Zhang C, Wang X. A New Staging System Based on the Dynamic Prognostic Nomogram for Elderly Patients With Primary Gastrointestinal Diffuse Large B-Cell Lymphoma. Front Med (Lausanne) 2022; 9:860993. [PMID: 35586073 PMCID: PMC9108771 DOI: 10.3389/fmed.2022.860993] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 03/31/2022] [Indexed: 11/16/2022] Open
Abstract
Objective The purpose of this study is to establish an accurate prognostic model based on important clinical parameters to predict the overall survival (OS) of elderly patients with primary gastrointestinal diffuse large B-cell lymphoma (EGI DLBCL). Methods The Cox regression analysis is based on data from the Surveillance, Epidemiology, and End Results (SEER) database. Results A total of 1,783 EGI DLBCL cases were eligible for the study [median (interquartile range, IQR) age, 75 (68–82) years; 974 (54.63%) males], of which 1,248 were randomly assigned to the development cohort, while 535 were into the validation cohort. A more accurate and convenient dynamic prognostic nomogram based on age, stage, radiation, and chemotherapy was developed and validated, of which the predictive performance was superior to that of the Ann Arbor staging system [C-index:0.69 (95% CI:0.67–0.71) vs. 56 (95%CI:0.54–0.58); P < 0.001]. The 3- and 5-year AUC values of ROC curves for 3-year OS and 5-year OS in the development cohort and the validation cohort were were alll above 0.7. Conclusion We establish and validate a more accurate and convenient dynamic prognostic nomogram for patients with EGI DLBCL, which can provide evidence for individual treatment and follow-up.
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Affiliation(s)
- Junmin Wang
- Department of Gastroenterology, The Third Hospital of Hebei Medical University, Shijiazhuang, China
- *Correspondence: Junmin Wang,
| | - Weirui Ren
- Department of Gastroenterology, The Third Hospital of Hebei Medical University, Shijiazhuang, China
| | - Chuang Zhang
- Department of Pediatric Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xiaoya Wang
- Jitang College of North China University of Science and Technology, Tangshan, China
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Yi J, Liu Z, Wang L, Zhang X, Pi L, Zhou C, Mu H. Development and Validation of Novel Nomograms to Predict the Overall Survival and Cancer-Specific Survival of Cervical Cancer Patients With Lymph Node Metastasis. Front Oncol 2022; 12:857375. [PMID: 35372011 PMCID: PMC8968041 DOI: 10.3389/fonc.2022.857375] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 02/21/2022] [Indexed: 01/17/2023] Open
Abstract
Objective The objective of this study was to establish and validate novel individualized nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in cervical cancer patients with lymph node metastasis. Methods A total of 2,956 cervical cancer patients diagnosed with lymph node metastasis (American Joint Committee on Cancer, AJCC N stage=N1) between 2000 and 2018 were included in this study. Univariate and multivariate Cox regression models were applied to identify independent prognostic predictors, and the nomograms were established to predict the OS and CSS. The concordance index (C-index), calibration curves, and receiver operating characteristic (ROC) curves were applied to estimate the precision and discriminability of the nomograms. Decision-curve analysis (DCA) was used to assess the clinical utility of the nomograms. Results Tumor size, log odds of positive lymph nodes (LODDS), radiotherapy, surgery, T stage, histology, and grade resulted as significant independent predictors both for OS and CSS. The C-index value of the prognostic nomogram for predicting OS was 0.788 (95% CI, 0.762–0.814) and 0.777 (95% CI, 0.758–0.796) in the training and validation cohorts, respectively. Meanwhile, the C-index value of the prognostic nomogram for predicting CSS was 0.792 (95% CI, 0.767–0.817) and 0.781 (95% CI, 0.764–0.798) in the training and validation cohorts, respectively. The calibration curves for the nomograms revealed gratifying consistency between predictions and actual observations for both 3- and 5-year OS and CSS. The 3- and 5-year area under the curves (AUCs) for the nomogram of OS and CSS ranged from 0.781 to 0.828. Finally, the DCA curves emerged as robust positive net benefits across a wide scale of threshold probabilities. Conclusion We have successfully constructed nomograms that could predict 3- and 5-year OS and CSS of cervical cancer patients with lymph node metastasis and may assist clinicians in decision-making and personalized treatment planning.
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Affiliation(s)
- Jianying Yi
- Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Zhili Liu
- Department of Clinical Laboratory, The Third Central Hospital, Tianjin, China
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China
- Artificial Cell Engineering Technology Research Center, Tianjin, China
- Tianjin Institute of Hepatobiliary Disease, Tianjin, China
| | - Lu Wang
- Department of Gynecology and Obstetrics, Traditional Chinese Medicine Hospital of Xiaoyi City, Xiaoyi, China
| | - Xingxin Zhang
- Department of Clinical Laboratory, People’s Hospital of Xiaoyi City, Xiaoyi, China
| | - Lili Pi
- Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Chunlei Zhou
- Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Hong Mu
- Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
- *Correspondence: Hong Mu,
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