scholarly journals Development and Validation of a Simple-to-Use Nomogram to Predict 3-Year Recurrence rate of Ovarian Cancer

Author(s):  
Yaduan Lin ◽  
Shuo Zhu ◽  
Fanchen He ◽  
Jiachun Wei ◽  
Han Lu ◽  
...  

Abstract Objective: To establish a reliable nomogram model to predict the recurrence rate of ovarian cancer after surgery. Methods: We retrospectively reviewed 216 patients diagnosed with ovarian cancer in our hospital, of which 164 cases were considered valid. Logistic regression model was used to analyze the possible predictors. After that, a nomogram model based on those significantly related predictors was established. We used the bootstrap to internally validate the predictive ability of the nomogram model and used the decision curve analysis (DCA) to compare the performance of the FIGO stage with this model. Results: The nomogram included seven significant recurrence predictors: age, histology type, FIGO stage, omentum involvement, lymphovascular space invasion (LVSI), liver metastasis, and serum CA125. The measurement values for accuracy were Brier score 0.131, correction slope 1.00, and c-index 0.870. which demonstrated this model had a good predictive ability. Compared with the FIGO stage, this hybrid model is more superior in predicting recurrence risk in ovarian cancer patients. Conclusions: We developed and validated a non-invasion and user-friendly nomogram model to predict the recurrence risk of patients with ovarian cancer after surgery.

2021 ◽  
Vol 17 (8) ◽  
pp. 893-906
Author(s):  
Ruiqi Wang ◽  
Guilan Xie ◽  
Li Shang ◽  
Cuifang Qi ◽  
Liren Yang ◽  
...  

Aim: To develop and internally validate nomograms to predict the overall survival (OS) and the cancer-specific survival (CSS) of patients with epithelial ovarian cancer (EOC). Methods: A total of 9001 EOC patients diagnosed between 2010 and 2013 were randomly divided into the training (n = 6301) and validation (n = 2700) cohorts. Nomogram and bootstrap validation were used to assess the predictive values of the models, including discrimination, calibration and clinical benefit. Results: In the validation cohort, the concordance statistic values were 0.733 for OS and 0.747 for CSS. Calibration plots and decision curve analyses demonstrated moderate accuracy and clinical applicability. Conclusion: Nomograms were user-friendly tools for guiding clinical treatment and estimating prognosis.


2020 ◽  
Vol 48 (10) ◽  
pp. 030006052095880
Author(s):  
Jianping Wu ◽  
Sulai Liu ◽  
Xiaoming Chen ◽  
Hongfei Xu ◽  
Yaoping Tang

Objective Colorectal cancer (CRC) is the most common cancer worldwide. Patient outcomes following recurrence of CRC are very poor. Therefore, identifying the risk of CRC recurrence at an early stage would improve patient care. Accumulating evidence shows that autophagy plays an active role in tumorigenesis, recurrence, and metastasis. Methods We used machine learning algorithms and two regression models, univariable Cox proportion and least absolute shrinkage and selection operator (LASSO), to identify 26 autophagy-related genes (ARGs) related to CRC recurrence. Results By functional annotation, these ARGs were shown to be enriched in necroptosis and apoptosis pathways. Protein–protein interactions identified SQSTM1, CASP8, HSP80AB1, FADD, and MAPK9 as core genes in CRC autophagy. Of 26 ARGs, BAX and PARP1 were regarded as having the most significant predictive ability of CRC recurrence, with prediction accuracy of 71.1%. Conclusion These results shed light on prediction of CRC recurrence by ARGs. Stratification of patients into recurrence risk groups by testing ARGs would be a valuable tool for early detection of CRC recurrence.


Author(s):  
Li Cheng ◽  
Lin Li ◽  
Liling Wang ◽  
Xiaofang Li ◽  
Hui Xing ◽  
...  

2021 ◽  
Author(s):  
Yijun Wu ◽  
Hongzhi Liu ◽  
Jianxing Zeng ◽  
Yifan Chen ◽  
Guoxu Fang ◽  
...  

Abstract Background and Objectives Combined hepatocellular cholangiocarcinoma (cHCC) has a high incidence of early recurrence. The objective of this study is to construct a model predicting very early recurrence (VER)(ie, recurrence within 6 months after surgery) of cHCC. Methods 131 consecutive patients from Eastern Hepatobiliary Surgery Hospital served as a development cohort to construct a nomogram predicting VER by using multivariable logistic regression analysis. The model was internally and externally validated in an validation cohort of 90 patients from Mengchao Hepatobiliary Hospital using the C concordance statistic, calibration analysis and decision curve analysis (DCA). Results The VER nomogram contains microvascular invasion(MiVI), macrovascular invasion(MaVI) and CA19-9>25mAU/mL. The model shows good discrimination with C-indexes of 0.77 (95%CI: 0.69 - 0.85 ) and 0.76 (95%CI:0.66 - 0.86) in the development cohort and validation cohort respectively. Decision curve analysis demonstrated that the model are clinically useful and the calibration of our model was favorable. Our model stratified patients into two different risk groups, which exhibited significantly different VER. Conclusions Our model demonstrated favorable performance in predicting VER in cHCC patients.


2021 ◽  
Vol 12 ◽  
Author(s):  
Min Zhou ◽  
Shasha Hong ◽  
Bingshu Li ◽  
Cheng Liu ◽  
Ming Hu ◽  
...  

Background: DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC).Methods: DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package MethylMix to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features.Results: We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC.Conclusion: The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.


2020 ◽  
Author(s):  
Dai Zhang ◽  
Si Yang ◽  
Yiche Li ◽  
Meng Wang ◽  
Jia Yao ◽  
...  

Abstract Background: Ovarian cancer (OV) is deemed as the most lethal gynecological cancer in women. The aim of this study was construct an effective gene prognostic model for OV patients.Methods: The expression profiles of glycolysis-related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed in training and test sets.Results: Based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4), a gene risk signature was identified to predict the outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high-grade OV, in the TCGA dataset, with areas under the curve of 0.709, 0.762, and 0.808 for 3-, 5- and 10-year survival, respectively. Similar results were found in the test sets, and the signature was also an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was constructed.Conclusion: Our study established a nine-GRG risk model and a nomogram to better perform on OV patients’ survival prediction. The risk model represents a promising and independent prognostic predictor for OV patients. Moreover, our study of GRGs could offer guidances for underlying mechanisms explorations in the future.


2019 ◽  
Vol 20 (4) ◽  
pp. 1207-1213 ◽  
Author(s):  
Watcharin Chirdchim ◽  
Preecha Wanichsetakul ◽  
Phichayut Phinyo ◽  
Jayanton Patumanond ◽  
Komsun Suwannarurk ◽  
...  

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