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Nomograms predicting survival and patterns of failure in patients with cervical cancer treated with concurrent chemoradiotherapy: A special focus on lymph nodes metastases.

ABSTRACT: OBJECTIVE:To construct nomograms predicting survival and patterns of failure in patients with cervical cancer treated with concurrent chemoradiotherapy (CCRT). METHODS:A total of 833 patients with cervical cancer treated with definitive radiotherapy or CCRT in our institute from January 2011 to December 2014 were included. Cox proportional hazard regression models were used in univariate and multivariate analysis. The following variables were included in the univariate analysis: histology, FIGO stage, lymph node metastases (para-aortic, pelvic, common iliac, binary pelvic, and binary common iliac LNMs), the number of pelvic metastatic lymph nodes (MLNs), and the diameter of pelvic MLNs. Nomograms predicting the 3- and 5-year overall survival (OS), disease-free survival (DFS), local control (LC) and distant metastasis-free (DMF) were constructed. The nomograms were internally validated with respect to discrimination and calibration. RESULTS:The median follow-up period was 36.4 months (range,1.0 to 76.2 months). After univariate and multivariate analysis, histology, FIGO stage, para-aortic LNM, pelvic LNM, number of MLNs and diameter of pelvic MLNs significantly predicted OS, DFS, LC or DMF. Nomograms predicting the 3- and 5-year OS, DFS, LC and DMF were constructed incorporating these significant variables. These nomograms showed good discrimination and calibration, with a concordance index of 0.73 for predicting OS, 0.71 for DFS, 0.73 for LC and 0.67 for DMF. CONCLUSION:We constructed nomograms predicting survival and patterns of failure with a special focus on regional LNM in patients with cervical cancer treated with concurrent chemoradiotherapy.

SUBMITTER: Wang W

PROVIDER: S-EPMC6464190 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Nomograms predicting survival and patterns of failure in patients with cervical cancer treated with concurrent chemoradiotherapy: A special focus on lymph nodes metastases.

Wang Weiping W Liu Xiaoliang X Meng Qingyu Q Zhang Fuquan F Hu Ke K

PloS one 20190415 4

<h4>Objective</h4>To construct nomograms predicting survival and patterns of failure in patients with cervical cancer treated with concurrent chemoradiotherapy (CCRT).<h4>Methods</h4>A total of 833 patients with cervical cancer treated with definitive radiotherapy or CCRT in our institute from January 2011 to December 2014 were included. Cox proportional hazard regression models were used in univariate and multivariate analysis. The following variables were included in the univariate analysis: h ...[more]

PMID: 30986236

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Project description:BackgroundIn 2018, a revised staging system was released for cervical cancer, which defined pelvic and paraaortic lymph node metastasis as stages IIIC1 and IIIC2, respectively. In this study, we constructed and validated nomograms to predict the 3- and 5-year survival of patients with cervical cancer based on the revised International Federation of Gynecology and Obstetrics (FIGO) staging system.MethodsWe retrospectively examined patients with 2009 FIGO stage IB-IVA cervical cancer who were treated at our institute between 2011 and 2015. Patients were randomized into the model development and validation cohorts (2:1). Univariate and multivariate analyses were conducted for the model development cohort to identify prognostic factors. In the multivariate analysis, nomograms were built to predict overall survival (OS) and disease-free survival (DFS) using significant variables. The nomograms were assessed based on the discrimination and calibration in both cohorts. Discrimination was assessed using the concordance index. Calibration was performed by comparing the mean nomogram estimated survival and the mean observed survival.ResultsWe included 1,192 patients, with 795 and 397 patients in the model development and validation cohorts, respectively. In the model development cohort, the median follow-up period was 49.2 months. After multivariate analysis, age, histology, 2018 FIGO stage, and pelvic lymph node number were independent factors for OS. Histology, 2018 FIGO stage, squamous cell carcinoma antigen, and pelvic lymph node number were significant predictors of DFS. The nomograms constructed to predict OS and DFS were based on these factors. In both model cohorts, the concordance index for the nomogram-predicted OS and DFS was 0.78 and 0.75 and 0.74 and 0.67, respectively. The calibration curve revealed good agreement between the nomogram predictions and actual values.ConclusionWe constructed robust nomograms to predict the OS and DFS of patients with cervical cancer undergoing treatment with concurrent chemoradiotherapy based on the 2018 FIGO staging system.

Project description:IntroductionThe axillary lymph node (ALN) status of breast cancer patients is an important prognostic indicator. The use of primary breast mass features for the prediction of ALN status is rare. Two nomograms based on preoperative ultrasound (US) images of breast tumors and ALNs were developed for the prediction of ALN status.MethodsA total of 743 breast cancer cases collected from 2016 to 2019 at the Second Affiliated Hospital of Harbin Medical University were randomly divided into a training set (n = 523) and a test set (n = 220). A primary tumor feature model (PTFM) and ALN feature model (ALNFM) were separately generated based on tumor features alone, and a combination of features was used for the prediction of ALN status. Logistic regression analysis was used to construct the nomograms. A receiver operating characteristic curve was plotted to obtain the area under the curve (AUC) to evaluate accuracy, and bias-corrected AUC values and calibration curves were obtained by bootstrap resampling for internal and external verification. Decision curve analysis was applied to assess the clinical utility of the models.ResultsThe AUCs of the PTFM were 0.69 and 0.67 for the training and test sets, respectively, and the bias-corrected AUCs of the PTFM were 0.67 and 0.67, respectively. Moreover, the AUCs of the ALNFM were 0.86 and 0.84, respectively, and the bias-corrected AUCs were 0.85 and 0.81, respectively. Compared with the PTFM, the ALNFM showed significantly improved prediction accuracy (p < 0.001). Both the calibration and decision curves of the ALNFM nomogram indicated greater accuracy and clinical practicality. When the US tumor size was ≤21.5 mm, the Spe was 0.96 and 0.92 in the training and test sets, respectively. When the US tumor size was greater than 21.5 mm, the Sen was 0.85 in the training set and 0.87 in the test set. Our further research showed that when the US tumor size was larger than 35 mm, the Sen was 0.90 in the training set and 0.93 in the test set.ConclusionThe ALNFM could effectively predict ALN status based on US images especially for different US tumor size.

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Nomograms predicting survival and patterns of failure in patients with cervical cancer treated with concurrent chemoradiotherapy: A special focus on lymph nodes metastases.

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Nomograms predicting survival and patterns of failure in patients with cervical cancer treated with concurrent chemoradiotherapy: A special focus on lymph nodes metastases.

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