Dataset Information

Nomogram for Predicting Recurrence-Free Survival in Chinese Women with Endometrial Cancer after Initial Therapy: External Validation.

ABSTRACT: This study aimed at developing an available recurrence-free survival (RFS) model of endometrial cancer (EC) for accurate and individualized prognosis assessment. A training cohort of 520 women with EC who underwent initial surgical treatment and an external validation cohort of 445 eligible EC patients from 2006 to 2016 were analyzed retrospectively. Multivariable Cox proportional hazards regression models were used to develop nomograms for predicting recurrence. The concordance index (C-index) and the area under the receiver operating characteristic curve (AUC) were calculated to determine the discrimination of RFS prognostic scoring systems. Calibration plots were generated to examine the performance characteristics of the predictive nomograms. Regression analysis revealed that an advanced International Federation of Gynecology and Obstetrics (FIGO) stage, histological grade 3, primary tumor diameter ?2?cm, and positive peritoneal cytology were independent prognostic factors for RFS in EC in the training set. The nomograms estimated RFS according to these four variables, with a C-index of 0.860, which was superior to that of FIGO stage (2009 criteria), at 0.809 (P=0.034), in the training cohort. Encouragingly, consistent results were observed in the validation set, with a C-index of 0.875 for the nomogram and a C-index of 0.833 for the FIGO staging (P=0.0137). Furthermore, the calibrations of the nomograms predicting 3- and 5-year RFS strongly corresponded to the actual survival outcome. In conclusion, this study developed an available nomogram with effective external validation and relatively appreciable discrimination and conformity for the accurate assessment of 3- and 5-year RFS in Chinese women with EC.

SUBMITTER: Cheng Y

PROVIDER: S-EPMC7275963 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Nomogram for Predicting Recurrence-Free Survival in Chinese Women with Endometrial Cancer after Initial Therapy: External Validation.

Cheng Yuan Y Dong Yangyang Y Tian Wenjuan W Zhang Hua H Li Xiaoping X Wang Zhiqi Z Shan Boer B Ren Yulan Y Wei Lihui L Wang Huaying H Wang Jianliu J

Journal of oncology 20200529

This study aimed at developing an available recurrence-free survival (RFS) model of endometrial cancer (EC) for accurate and individualized prognosis assessment. A training cohort of 520 women with EC who underwent initial surgical treatment and an external validation cohort of 445 eligible EC patients from 2006 to 2016 were analyzed retrospectively. Multivariable Cox proportional hazards regression models were used to develop nomograms for predicting recurrence. The concordance index (C-index) ...[more]

PMID: 32565798

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Project description:BackgroundThe increased use of computed tomography has brought a corresponding increase in the numbers of early-stage lung cancer patients receiving treatment. However, even for stage IA3 lung adenocarcinoma, many patients experience postoperative recurrence and metastasis. The existing TNM staging system for lung cancer does not take many clinical and pathological factors into consideration, resulting in the failure to detect and intervene as soon as possible in those with high recurrence risk. The purpose of this study was to explore the risk factors for postoperative recurrence-free survival (RFS) in patients with stage IA3 lung adenocarcinoma, and to construct and verify a nomogram model for predicting RFS in patients with the disease.MethodsThis study analyzed patients with stage IA3 lung adenocarcinoma who underwent surgical treatment. Univariate and multivariate analysis were used to analyze the independent risk factors for postoperative RFS and establish a nomogram model. Concordance index (C-index), receiver operating characteristic curve, clinical decision analysis, and calibration curve were used to evaluate the discrimination and calibration of the nomogram model. Data from two other institutions were used for external validation, and the nomogram scores were combined with X-tile software to screen high-risk groups of recurrence.ResultsThe internal cohort included 235 eligible patients with stage IA3 lung adenocarcinoma from 7,235 lung cancer. Multivariate analysis showed smoking, solid nodules, mucinous lung adenocarcinoma, and micropapillary component ≥5% were independent risk factors for RFS. A nomogram model was constructed based on the above results and the bootstrap method was used for internal validation. The internal and external validation C-indexes of the nomogram were 0.822 (95% CI: 0.751-0.891) and 0.812, respectively, indicating the obvious prediction performance was good. The X-tile software combined with nomogram scores showed the low-risk group (5-RFS rate, 0.65-0.99) had better RFS than the high-risk group (5-RFS rate, 0.20-0.65) (P<0.0001).ConclusionsWe constructed a nomogram model for predicting postoperative RFS in patients with stage IA3 lung adenocarcinoma which can individually evaluate the risk of postoperative recurrence, screen high-risk groups, and develop individualized follow-up and intervention strategies to improve the survival rate of the patients.

Project description:BackgroundAfter radical surgery, early detection of recurrence and metastasis is a crucial factor in enhancing the prognosis and survival of patients with gastric cancer (GC). Therefore, assessing the risk of recurrence in gastric cancer patients and determining the timing for postoperative recurrence is crucial.MethodsThe clinicopathological data of 521 patients with recurrent gastric cancer, who underwent radical gastrectomy at Zhejiang Cancer Hospital between January 2010 and January 2017, were retrospectively analyzed. These patients were randomly divided into two groups: a training group (n = 365) and a validation group (n = 156). In the training set, patients were further categorized into early recurrence (n = 263) and late recurrence (n = 102) groups based on a 2-year boundary. Comparative analyses of clinicopathological features and prognoses were conducted between these two groups. Subsequently, a nomogram for predicting early recurrence was developed and validated.ResultsIn this study, the developed nomogram incorporated age, serous infiltration, lymph node metastasis, recurrence mode, and the tumour marker CA19-9. In the training cohort, the area under the curve (AUC value) was 0.739 (95% CI, 0.682-0.798), with a corresponding C-index of 0.739. This nomogram was subsequently validated in an independent validation cohort, yielding an AUC of 0.743 (95% CI, 0.652-0.833) and a C-index of 0.743. Furthermore, independent risk factors for prognosis were identified, including age, absence of postoperative chemotherapy, early recurrence, lymph node metastasis, abdominal metastasis, and vascular cancer embolus.ConclusionIndependent risk factors for gastric cancer recurrence following radical surgery were utilized to construct a nomogram for predicting early relapse. This nomogram effectively assesses the risk of recurrence, aids in treatment decision-making and follow-up planning in clinical settings, and demonstrated strong performance in the validation cohort.

Dataset Information

Nomogram for Predicting Recurrence-Free Survival in Chinese Women with Endometrial Cancer after Initial Therapy: External Validation.

Publications

Nomogram for Predicting Recurrence-Free Survival in Chinese Women with Endometrial Cancer after Initial Therapy: External Validation.

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