Establishing a Nomogram for Stage IA-IIB Cervical Cancer Patients after Complete Resection

Zhou, Huaqiang; Xiong, Li; Zhang, Yuan; Jia, Yulin; Hu, Ting; Yang, Ru; Huang, Kecheng; Chen, Zhilan; Wang, Shaoshuai; Tang, Fangxu; Zhou, Jin; Chen, Yi-Le; Li, Wu; Han, Xiaozhe; Lin, Zhongqiu; Lü, Xiaomei; Xing, Hui; Qu, Pengpeng; Cai, Hongwei; Song, Xiaojie; Tian, Xiaoyu; Zhang, Qinghua; Shen, Jian; Liú, Dan; Wang, Zehua; Xu, Hongbing; Wang, Changyu; Liu, Xi; Deng, Dan; Wang, Hui; Lü, Weiguo; Shen, Keng; Wang, Shixuan; Xie, Xing; Cheng, Xiaodong; Ma, Ding; Li, Shuang

doi:10.7314/apjcp.2015.16.9.3773

Cited by 17 publications

(21 citation statements)

References 28 publications

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“…The value of the c-index, which is conceptually similar to receiver operating characteristics curve analysis [ 79 , 80 ], was 0.723. The most recent study by Zhou et al establishes a nomogram predicting 5–year overall survival of surgically-treated stage IA-IIB cervical cancer patients [ 4 ]. The authors used a number of metastatic lymph nodes, lymph-vascular space invasion, stromal invasion, parametrial invasion, tumor diameter, and histology as input variables.…”

Section: Discussionmentioning

confidence: 99%

“…Although the FIGO staging system serves as the main tool for estimating the general prognosis, it does not include all established prognostic factors, such as lymph node metastases, lymph-vascular space invasion, deep stromal infiltration, or histologic subtype. Another method for individual prediction of survival in cervical carcinoma is the recently developed nomograms based on selected demographic and clinical parameters [ 3 , 4 ]. None of these systems take into account intra/postoperative complications and concomitant diseases, however, which could alter patients outcome [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of 5–year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods

et al. 2017

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BackgroundComputational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool. This study aimed to evaluate the usefulness of artificial intelligence models for 5–year overall survival prediction in patients with cervical cancer treated by radical hysterectomy.MethodsThe data set was collected from 102 patients with cervical cancer FIGO stage IA2-IIB, that underwent primary surgical treatment. Twenty-three demographic, tumor-related parameters and selected perioperative data of each patient were collected. The simulations involved six computational intelligence methods: the probabilistic neural network (PNN), multilayer perceptron network, gene expression programming classifier, support vector machines algorithm, radial basis function neural network and k-Means algorithm. The prediction ability of the models was determined based on the accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve. The results of the computational intelligence methods were compared with the results of linear regression analysis as a reference model.ResultsThe best results were obtained by the PNN model. This neural network provided very high prediction ability with an accuracy of 0.892 and sensitivity of 0.975. The area under the receiver operating characteristics curve of PNN was also high, 0.818. The outcomes obtained by other classifiers were markedly worse.ConclusionsThe PNN model is an effective tool for predicting 5–year overall survival in cervical cancer patients treated with radical hysterectomy.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of 5–year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods

et al. 2017

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“…Several nomograms have been reported to predict prognosis in patients with cervical cancer. 15,16,34 Zhou et al 35 developed a nomogram predicting 5-year OS of 1563 surgically treated cervical cancer patients with a C-index of 0.71. In their study, clinical and pathological parameters such as lymph node metastasis, LVSI, stromal invasion, parametrial invasion, tumor diameter, and histology were identified as independent prognostic factors.…”

Section: Discussionmentioning

confidence: 99%

“…However, different from other studies, stage IIB patients also received surgeries and were included in their study. 35 Je et al 36 However, in our study, clinical, hematogical, and pathological data were included to construct a nomogram for OS with a high C-index of 0.74. Polterauer et al 16 developed a nomogram for OS with a C-index of 0.723, and 6 parameters such as FIGO stage, tumor size, age, histologic subtype, lymph node ratio, and parametrial involvement were selected as nomogram covariates.…”

Section: Discussionmentioning

confidence: 99%

Nomogram Predicting Overall Survival in Operable Cervical Cancer Patients

Zheng¹,

Huang²,

Liu

et al. 2017

International Journal of Gynecological Cancer

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“…Nomogram is a practical model for accurately predicting the survival outcome of individual clinical patients [15]. The FIGO staging system is the most commonly used for the clinical evaluation of cervical cancer [16]. However, the updated system of patients with lymph node-positive status into a single-stage could produce a highly heterogeneous population with signi cant differences in survival.…”

Section: Introductionmentioning

confidence: 99%

Nomogram for predicting the overall survival of cervical cancer patients with nonsurgical treatment

Song

et al. 2020

Preprint

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Background: Surgery is an important treatment for cervical cancer, but some patients choose not to operate on the primary site. The purpose of this study was to establish nomogram for predicting overall survival (OS) in nonsurgical cervical cancer on the primary site(NSCC) patients by using cases in the public database.Methods: All patients diagnosed between 2004 and 2015 were randomly divided into training (n=4283) or validation (n=1832) cohort. Cox regression analysis was used on the training cohort to select independent prognostic factors to construct nomogram for predicting 3- and 5-year OS. In two cohorts, receiver operating characteristic curve (ROC), Harrell’s concordance index (C-index), calibration curves and decision curve analysis (DCA) were used to evaluate the accuracy and the reliability of the model. We made a risk stratification system based on the model and performed Kaplan-Meier survival analysis on the risk level.Results: In the training set, ten clinicopathological factors were significantly correlated with survival outcomes and were included in the nomogram. In the training cohort and validation cohort, the C-index, ROC and calibration plots exhibited an excellent consistency between actual OS and model predictions. DCA indicated great clinical benefit. Compared with FIGO staging, nomogram's evaluation effect is superior.Conclusions: Age, histological type, grade, tumor size, extension, lymph node involvement, distant metastasis, lymph node removed, chemotherapy and radiotherapy were independent prognostic factors for OS in NSCC patients. The model showed more satisfactory accuracy and clinical practicability than FIGO staging system, and may provide integration and improvement for therapeutic decisions.

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Establishing a Nomogram for Stage IA-IIB Cervical Cancer Patients after Complete Resection

Cited by 17 publications

References 28 publications

Prediction of 5–year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods

Prediction of 5–year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods

Nomogram Predicting Overall Survival in Operable Cervical Cancer Patients

Nomogram for predicting the overall survival of cervical cancer patients with nonsurgical treatment

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