Overall and cancer-specific survival in patients with breast Paget disease: A population-based study

2021 ◽  
pp. 153537022110562
Author(s):  
Tingting Hu ◽  
Zhiyuan Chen ◽  
Meng Hou ◽  
Kezhi Lin
Keyword(s):  
Predictive Accuracy ◽  
Tumor Grade ◽  
Curve Analysis ◽  
Histological Type ◽  
Related Factors ◽  
Cancer Specific Survival ◽  
Decision Curve Analysis ◽  
Paget Disease ◽  
Ajcc Stage ◽  
Calibration Curves

Paget disease of the breast is an uncommon malignant tumor with an inferior outcome. Therefore, establishing nomograms to predict the survival outcomes of breast Paget disease patients is urgent. Clinicopathological and follow-up data of breast Paget disease patients diagnosed between 2010 and 2016 were retrieved through the Surveillance, Epidemiology, and End Result (SEER) database. The significant factors were screened out, and then those factors were utilized to build two valuable nomograms. The discriminative ability of nomograms was investigated using concordance-index (C-index), while the predictive accuracy and benefits were evaluated using calibration curves and decision curve analysis. Finally, a total of 417 breast Paget disease patients were enrolled. Tumor grade, histological type, American Joint Committee on Cancer (AJCC) stage, surgery, chemotherapy, and marital status were confirmed as independent overall survival (OS)-related factors; tumor grade, histological type, AJCC stage, and age were associated with independent cancer-specific survival (CSS)-related factors. The values of the C-index for OS nomogram acquired were 0.827 and 0.745 for training and validation cohorts, respectively. Meanwhile, the corresponding values of the C-index to CSS nomogram were 0.890 and 0.655, respectively. The calibration curves and decision curve analysis indicated that both nomograms had an excellent performance. Finally, the nomogram-based risk stratification system indicated that all breast Paget disease patients could be classified into low- and high-risk groups and showed distinct outcomes. In conclusion, two valuable nomograms incorporating various clinicopathological indicators were established for breast Paget disease patients. These prognostic nomograms provide accurate prognostic assessment for breast Paget disease patients and help clinicians select appropriate treatment strategies.

scholarly journals Nomograms-based prediction of overall and cancer-specific survivals for patients with chromophobe renal cell carcinoma

2020 ◽  
pp. 153537022097710
Author(s):  
Chunyang Chen ◽  
Xinyu Geng ◽  
Rui Liang ◽  
Dongze Zhang ◽  
Meiyun Sun ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Operating Characteristic ◽  
Tumor Grade ◽  
Curve Analysis ◽  
Cancer Specific Survival ◽  
Decision Curve Analysis

This study built and tested two effective nomograms for the purpose of predicting cancer-specific survival and overall survival of chromophobe renal cell carcinoma (chRCC) patients. Multivariate Cox regression analysis was employed to filter independent prognostic factors predictive of cancer-specific survival and overall survival, and the nomograms were built based on a training set incorporating 2901 chRCC patients in a retrospective study (from 2004 to 2015) downloaded from the surveillance, epidemiology, and end results (SEER) database. The nomograms were verified on a validation cohort of 1934 patients, subsequently the performances of the nomograms were examined according to the receiver operating characteristic curve, calibration curves, the concordance (C-index), and decision curve analysis. The results showed that tumor grade, AJCC and N stages, race, marital status, age, histories of chemotherapy, radiotherapy and surgery were the individual prognostic factors for overall survival, and that AJCC, N and SEER stages, histories of surgery, radiotherapy and chemotherapy, age, tumor grade were individual prognostic factors for cancer-specific survival. According to C-indexes, receiver operating characteristic curves, and decision curve analysis outcomes, the nomograms showed a higher accuracy in predicting overall survival and OSS when compared with TNM stage and SEER stage. All the calibration curves were significantly consistent between predictive and validation sets. In this study, the nomograms, which were validated to be highly accurate and applicable, were built to facilitate individualized predictions of the cancer-specific survival and overall survival to patients diagnosed with chRCC between 2004 and 2015.

scholarly journals Development and Validation of Nomograms Based on Gamma-Glutamyl Transpeptidase to Platelet Ratio for Hepatocellular Carcinoma Patients Reveal Novel Prognostic Value and the Ratio Is Negatively Correlated With P38MAPK Expression

2020 ◽  
Vol 10 ◽  
Author(s):  
Dingan Luo ◽  
Haoran Li ◽  
Jie Hu ◽  
Mao Zhang ◽  
Shun Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Signaling Pathways ◽  
Predictive Accuracy ◽  
Curve Analysis ◽  
Gamma Glutamyl Transpeptidase ◽  
Decision Curve Analysis ◽  
Gamma Glutamyl ◽  
Calibration Curves ◽  
The Relationship ◽  
Glutamyl Transpeptidase

BackgroundEarly prediction of recurrence and death risks is significant to the treatment of hepatocellular carcinoma (HCC) patients. We aimed to develop and validate prognosis nomogram models based on the gamma-glutamyl transpeptidase (GGT)-to-platelet (PLT) ratio (GPR) for HCC and to explore the relationship between the GPR and inflammation-related signaling pathways.MethodsAll data were obtained from 2000 to 2012 in the Affiliated Hospital of Qingdao University. In the training cohort, factors included in the nomograms were determined by univariate and multivariate analyses. In the training and validation cohorts, the concordance index (C-index) and calibration curves were used to assess predictive accuracy, and receiver operating characteristic curves were used to assess discriminative ability. Clinical utility was evaluated using decision curve analysis. Moreover, improvement of the predictive accuracy of the nomograms was evaluated by calculating the decision curve analysis, the integrated discrimination improvement, and the net reclassification improvement. Finally, the relationship between the GPR and inflammation-related signaling pathways was evaluated using the independent-samples t-test.ResultsA larger tumor size and higher GPR were common independent risk factors for both disease-free survival (DFS) and overall survival (OS) in HCC (P < 0.05). Good agreement between our nomogram models’ predictions and actual observations was detected by the C-index and calibration curves. Our nomogram models showed significantly better performance in predicting the HCC prognosis compared to other models (P < 0.05). Online webserver and scoring system tables were built based on the proposed nomogram for convenient clinical use. Notably, including the GPR greatly improved the predictive ability of our nomogram models (P < 0.05). In the validation cohort, p38 mitogen-activated protein kinase (P38MAPK) expression was significantly negatively correlated with the GPR (P < 0.01) and GGT (P = 0.039), but was not correlated with PLT levels (P = 0.063). And we found that P38MAPK can regulate the expression of GGT by quantitative real-time PCR and Western blotting experiments.ConclusionsThe dynamic nomogram based on the GPR provides accurate and effective prognostic predictions for HCC, and P38MAPK-GGT may be a suitable therapeutic target to improve the prognosis of HCC patients.

scholarly journals Dynamic nomograms combining N classification with ratio-based nodal classifications to predict long-term survival for patients with lung adenocarcinoma after surgery: a SEER population-based study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Suyu Wang ◽  
Yue Yu ◽  
Wenting Xu ◽  
Xin Lv ◽  
Yufeng Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
External Validation ◽  
Predictive Performance ◽  
Information Criterion ◽  
Curve Analysis ◽  
Tnm Stage ◽  
Concordance Index ◽  
Cancer Specific Survival ◽  
Decision Curve Analysis

Abstract Background The prognostic roles of three lymph node classifications, number of positive lymph nodes (NPLN), log odds of positive lymph nodes (LODDS), and lymph node ratio (LNR) in lung adenocarcinoma are unclear. We aim to find the classification with the strongest predictive power and combine it with the American Joint Committee on Cancer (AJCC) 8th TNM stage to establish an optimal prognostic nomogram. Methods 25,005 patients with T1-4N0–2M0 lung adenocarcinoma after surgery between 2004 to 2016 from the Surveillance, Epidemiology, and End Results database were included. The study cohort was divided into training cohort (13,551 patients) and external validation cohort (11,454 patients) according to different geographic region. Univariate and multivariate Cox regression analyses were performed on the training cohort to evaluate the predictive performance of NPLN (Model 1), LODDS (Model 2), LNR (Model 3) or LODDS+LNR (Model 4) respectively for cancer-specific survival and overall survival. Likelihood-ratio χ2 test, Akaike Information Criterion, Harrell concordance index, integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to evaluate the predictive performance of the models. Nomograms were established according to the optimal models. They’re put into internal validation using bootstrapping technique and external validation using calibration curves. Nomograms were compared with AJCC 8th TNM stage using decision curve analysis. Results NPLN, LODDS and LNR were independent prognostic factors for cancer-specific survival and overall survival. LODDS+LNR (Model 4) demonstrated the highest Likelihood-ratio χ2 test, highest Harrell concordance index, and lowest Akaike Information Criterion, and IDI and NRI values suggested Model 4 had better prediction accuracy than other models. Internal and external validations showed that the nomograms combining TNM stage with LODDS+LNR were convincingly precise. Decision curve analysis suggested the nomograms performed better than AJCC 8th TNM stage in clinical practicability. Conclusions We constructed online nomograms for cancer-specific survival and overall survival of lung adenocarcinoma patients after surgery, which may facilitate doctors to provide highly individualized therapy.

scholarly journals Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis

2020 ◽  
Vol 7 ◽  
Author(s):  
Bin Zhang ◽  
Qin Liu ◽  
Xiao Zhang ◽  
Shuyi Liu ◽  
Weiqi Chen ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Clinical Utility ◽  
Predictive Accuracy ◽  
Fasting Blood Glucose ◽  
External Validation ◽  
Predictive Performance ◽  
Curve Analysis ◽  
Discriminative Ability ◽  
Decision Curve Analysis ◽  
Poor Outcome

Aim: Early detection of coronavirus disease 2019 (COVID-19) patients who are likely to develop worse outcomes is of great importance, which may help select patients at risk of rapid deterioration who should require high-level monitoring and more aggressive treatment. We aimed to develop and validate a nomogram for predicting 30-days poor outcome of patients with COVID-19.Methods: The prediction model was developed in a primary cohort consisting of 233 patients with laboratory-confirmed COVID-19, and data were collected from January 3 to March 20, 2020. We identified and integrated significant prognostic factors for 30-days poor outcome to construct a nomogram. The model was subjected to internal validation and to external validation with two separate cohorts of 110 and 118 cases, respectively. The performance of the nomogram was assessed with respect to its predictive accuracy, discriminative ability, and clinical usefulness.Results: In the primary cohort, the mean age of patients was 55.4 years and 129 (55.4%) were male. Prognostic factors contained in the clinical nomogram were age, lactic dehydrogenase, aspartate aminotransferase, prothrombin time, serum creatinine, serum sodium, fasting blood glucose, and D-dimer. The model was externally validated in two cohorts achieving an AUC of 0.946 and 0.878, sensitivity of 100 and 79%, and specificity of 76.5 and 83.8%, respectively. Although adding CT score to the clinical nomogram (clinical-CT nomogram) did not yield better predictive performance, decision curve analysis showed that the clinical-CT nomogram provided better clinical utility than the clinical nomogram.Conclusions: We established and validated a nomogram that can provide an individual prediction of 30-days poor outcome for COVID-19 patients. This practical prognostic model may help clinicians in decision making and reduce mortality.

scholarly journals Novel nomogram to predict risk of bone metastasis in newly diagnosed thyroid carcinoma: a population-based study

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yuexin Tong ◽  
Chuan Hu ◽  
Zhangheng Huang ◽  
Zhiyi Fan ◽  
Lujian Zhu ◽  
...  
Keyword(s):  
Bone Metastasis ◽  
Thyroid Carcinoma ◽  
Black People ◽  
Predictive Performance ◽  
Clinical Decision ◽  
Curve Analysis ◽  
Newly Diagnosed ◽  
Clinical Value ◽  
Decision Curve Analysis ◽  
Calibration Curves

Abstract Background The aim of this study was to develop and validate a visual nomogram for predicting the risk of bone metastasis (BM) in newly diagnosed thyroid carcinoma (TC) patients. Methods The demographics and clinicopathologic variables of TC patients from 2010 to 2015 in the Surveillance, Epidemiology and End Results (SEER) database were retrospectively reviewed. Chi-squared (χ2) test and logistic regression analysis were performed to identify independent risk factors. Based on that, a predictive nomogram was developed and validated for predicting the risk of BM in TC patients. The C-index was used to compute the predictive performance of the nomogram. Calibration curves and decision curve analysis (DCA) were furthermore used to evaluate the clinical value of the nomogram. Results According to the inclusion and exclusion criteria, the data of 14,772 patients were used to analyze in our study. After statistical analysis, TC patients with older age, higher T stage, higher N stage, poorly differentiated, follicular thyroid carcinoma (FTC) and black people had a higher risk of BM. We further developed a nomogram with a C-index of 0.925 (95%CI,0.895–0.948) in the training set and 0.842 (95%CI,0.777–0.907) in the validation set. The calibration curves and decision curve analysis (DCA) also demonstrated the reliability and accuracy of the clinical prediction model. Conclusions The present study developed a visual nomogram to accurately identify TC patients with high risk of BM, which might help to further provide more individualized clinical decision guidelines.

scholarly journals Multiparameter MRI Radiomics Model Predicts Preoperative Peritoneal Carcinomatosis in Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiao Yu Yu ◽  
Jialiang Ren ◽  
Yushan Jia ◽  
Hui Wu ◽  
Guangming Niu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Ovarian Cancer ◽  
Peritoneal Carcinomatosis ◽  
Calibration Curve ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Imaging Features ◽  
Decision Curve Analysis ◽  
Dce Mri

ObjectivesTo evaluate the predictive value of radiomics features based on multiparameter magnetic resonance imaging (MP-MRI) for peritoneal carcinomatosis (PC) in patients with ovarian cancer (OC).MethodsA total of 86 patients with epithelial OC were included in this retrospective study. All patients underwent FS-T2WI, DWI, and DCE-MRI scans, followed by total hysterectomy plus omentectomy. Quantitative imaging features were extracted from preoperative FS-T2WI, DWI, and DCE-MRI images, and feature screening was performed using a minimum redundancy maximum correlation (mRMR) and least absolute shrinkage selection operator (LASSO) methods. Four radiomics models were constructed based on three MRI sequences. Then, combined with radiomics characteristics and clinicopathological risk factors, a multi-factor Logistic regression method was used to construct a radiomics nomogram, and the performance of the radiomics nomogram was evaluated by receiver operating characteristic curve (ROC) curve, calibration curve, and decision curve analysis.ResultsThe radiomics model from the MP-MRI combined sequence showed a higher area under the curve (AUC) than the model from FS-T2WI, DWI, and DCE-MRI alone (0.846 vs. 0.762, 0.830, 0.807, respectively). The radiomics nomogram (AUC=0.902) constructed by combining radiomics characteristics and clinicopathological risk factors showed a better diagnostic effect than the clinical model (AUC=0.858) and the radiomics model (AUC=0.846). The decision curve analysis shows that the radiomics nomogram has good clinical application value, and the calibration curve also proves that it has good stability.ConclusionRadiomics nomogram based on MP-MRI combined sequence showed good predictive accuracy for PC in patients with OC. This tool can be used to identify peritoneal carcinomatosis in OC patients before surgery.

scholarly journals Development and Validation of Nomograms for Predicting Prognosis in Intrahepatic Cholangiocarcinoma Patients with Lymph Node Metastasis

2021 ◽  
Author(s):  
xianmao shi ◽  
Xing Sun ◽  
Xin Qin ◽  
Ze Su ◽  
Zhaoshan Fang ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Intrahepatic Cholangiocarcinoma ◽  
Clinical Decision Making ◽  
Curve Analysis ◽  
Individual Treatment ◽  
Decision Curve Analysis ◽  
Node Metastasis ◽  
Calibration Curves

Abstract Background. Lymph node metastasis (LNM) is one of the common metastatic sites of in advanced-stage intrahepatic cholangiocarcinoma (ICC), and the prognosis of ICC patients with LNM is worse than patients without it. Our study aimed to identify the prognostic factors of ICC patients with LNM, and develop an effective nomogram to quantify the prognosis of ICC patients with LNM.Methods. We retrospectively reviewed the data of ICC patients between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic analysis were used to determine the independent predictors for LNM in patients with ICC. Univariate and multivariate Cox analyses were used to identify the independent prognostic factors for ICC patients with LNM. Finally, two nomograms for predicting overall survival (OS) and cause-specific survival (CSS) were established, and the nomogram of predicting OS was evaluated by calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA).Results. A total of 1539 patients with ICC were enrolled into our analysis, including 381 cases (24.76%) with LNM at initial diagnosis and 1158 cases (75.24%) without it. The independent risk factors for LNM in newly diagnosed ICC patients are age, T stage, and tumor size. The independent prognostic factors for ICC patients with LNM are grade, chemotherapy, and surgery of primary site. For the prognostic nomogram for OS, the AUCs of 6-, 12-, and 24-months were 0.809, 0.780, and 0.755 in the training set and 0.806, 0.780, and 0.753 in the testing set, respectively. The calibration curves and decision curve analysis indicated the good performance of the nomogram.Conclusions. The individualized nomogram could predict OS of ICC patients with LNM with good performance, which could be served as an effective tool for prognostic evaluation and individual treatment strategies optimization in ICC patients with LNM, and clinical utility may benefit for clinical decision-making.

scholarly journals A nomogram to predict prognosis after surgery for young patients with hepatocellular carcinoma

2020 ◽  
Author(s):  
Xingchen Li ◽  
Xinyu Bi ◽  
Jianjun Zhao ◽  
Zhiyu Li ◽  
Jianguo Zhou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Young Patients ◽  
Risk Groups ◽  
Curve Analysis ◽  
Rank Test ◽  
Cancer Center ◽  
Cancer Specific Survival ◽  
Decision Curve Analysis ◽  
Log Rank Test ◽  
Kaplan Meier

Abstract Background Only few studies have been evaluated the clinical characteristics and prognosis of hepatocellular carcinoma in young patients. The purpose of this study is to identify prognostic factors and develop an efficient and practical nomogram to predict cancer-specific survival in young patients with hepatocellular carcinoma.Methods Four-hundred-and-forty-one young patients with hepatocellular carcinoma who had undergone surgery from 2004-2015 were selected from the Surveillance, Epidemiology, and End Results database. The competing risk model, Lasso and Cox regression were used to screen prognostic factors for cancer-specific survival, and a prognostic nomogram was established using these factors. Thirty-nine young patients with hepatocellular carcinoma from the National Cancer Center, Cancer Hospital, Chinese Academy of Medical Science were used to validate our model. To further evaluate the predictive performance of our model, the concordance index was calculated and the calibration curves were drawn. The clinical usefulness was evaluated by decision curve analysis(DCA). Finally, all patients were grouped by our nomogram. The survival of different risk groups was analyzed using the Kaplan-Meier method, and the differences among survival curves were compared by the log-rank test.Results The median survival times of the Surveillance, Epidemiology, and End Results training group and the external National Cancer Center validation group were 41 and 52 months, respectively. Histological grade, tumor size, Alpha-fetoprotein, T stage, and M stage were selected as independent factors for cancer-specific survival, and a prognostic nomogram was established. The concordance indices of the training and external validation groups were 0.76 (95% CI, 0.72 to 0.80) and 0.92 (se=0.085), respectively. The calibration plots showed good agreement. Decision curve analysis revealed that our nomogram resulted in a better clinical net benefit than the AJCC 7th edition and Barcelona Clinic Liver Cancer staging systems. Patients were divided into two risk groups according to the cut-off value of 125 of the total points from our nomogram. Kaplan-Meier plots for cancer-specific survival were performed using the log-rank test, the p-value of which was <0.001.Conclusions The practical nomogram resulted in a more-accurate prognostic prediction for young hepatocellular carcinoma patients after curative liver resection.

scholarly journals MRI-Based Radiomics Nomogram To Predict Intraoperative Hemorrhage of Placenta Previa

2021 ◽  
Author(s):  
Ye Song ◽  
Liping Zhu ◽  
Dali Chen ◽  
Yongmei Li ◽  
Qi Xi ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Placenta Previa ◽  
Preoperative Evaluation ◽  
Curve Analysis ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Preoperative Prediction ◽  
Mri Features ◽  
Wide Range ◽  
Validation Set

Abstract Background: Placenta previa is associated with higher percentage of intraoperative and postpartum hemorrhage, increased obstetric hysterectomy, significant maternal morbidity and mortality. We aimed to develop and validate a magnetic resonance imaging (MRI)-based nomogram to preoperative prediction of intraoperative hemorrhage (IPH) for placenta previa, which might contribute to adequate assessment and preoperative preparation for the obstetricians.Methods: Between May 2015 and December 2019, a total of 125 placenta previa pregnant women were divided into a training set (n = 80) and a validation set (n = 45). Radiomics features were extracted from MRI images of each patient. A MRI-based model comprising seven features was built for the classification of patients into IPH and non-IPH groups in a training set and validation set. Multivariate nomograms based on logistic regression analyses were built according to radiomics features. Receiver operating characteristic (ROC) curve was used to assess the model. Predictive accuracy of nomogram were assessed by calibration plots and decision curve analysis. Results: In multivariate analysis, placenta position, placenta thickness, cervical blood sinus and placental signals in the cervix were signifcantly independent predictors for IPH (all p < 0.05). The MRI-based nomogram showed favorable discrimination between IPH and non-IPH groups. The calibration curve showed good agreement between the estimated and the actual probability of IPH. Decision curve analysis also showed a high clinical benefit across a wide range of probability thresholds. The AUC was 0.918 ( 95% CI, 0.857-0.979 ) in the training set and 0.866( 95% CI, 0.748-0.985 ) in the validation set by the combination of four MRI features.Conclusions: The MRI-based nomograms might be a useful tool for the preoperative prediction of IPH outcomes for placenta previa. Our study enables obstetricians to perform adequate preoperative evaluation to minimize blood loss and reduce the rate of caesarean hysterectomy.

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