scholarly journals SOURCE: A Registry-Based Prediction Model for Overall Survival in Patients with Metastatic Oesophageal or Gastric Cancer

Cancers ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 187 ◽  
Author(s):  
Héctor van den Boorn ◽  
Ameen Abu-Hanna ◽  
Emil ter Veer ◽  
Jessy van Kleef ◽  
Florian Lordick ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Decision Making ◽  
Overall Survival ◽  
Prediction Model ◽  
Prediction Models ◽  
Cox Regression ◽  
Added Value ◽  
Cancer Data ◽  
Netherlands Cancer Registry ◽  
Oesophagogastric Cancer

Prediction models are only sparsely available for metastatic oesophagogastric cancer. Because treatment in this setting is often preference-based, decision-making with the aid of a prediction model is wanted. The aim of this study is to construct a prediction model, called SOURCE, for the overall survival in patients with metastatic oesophagogastric cancer. Data from patients with metastatic oesophageal (n = 8010) or gastric (n = 4763) cancer diagnosed during 2005–2015 were retrieved from the nationwide Netherlands cancer registry. A multivariate Cox regression model was created to predict overall survival for various treatments. Predictor selection was performed via the Akaike Information Criterion and a Delphi consensus among experts in palliative oesophagogastric cancer. Validation was performed according to a temporal internal-external scheme. The predictive quality was assessed with the concordance-index (c-index) and calibration. The model c-indices showed consistent discriminative ability during validation: 0.71 for oesophageal cancer and 0.68 for gastric cancer. The calibration showed an average slope of 1.0 and intercept of 0.0 for both tumour locations, indicating a close agreement between predicted and observed survival. With a fair c-index and good calibration, SOURCE provides a solid foundation for further investigation in clinical practice to determine its added value in shared decision making.

SOURCE: Prediction models for overall survival in patients with metastatic and potentially curable esophageal and gastric cancer.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 301-301
Author(s):  
Héctor G. van den Boorn ◽  
Ameen Abu-Hanna ◽  
Nadia Haj Mohammad ◽  
Maarten C.C.M. Hulshof ◽  
Suzanne S. Gisbertz ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Decision Making ◽  
Overall Survival ◽  
Shared Decision Making ◽  
Prediction Models ◽  
Cox Regression ◽  
Future Research ◽  
Shared Decision ◽  
Netherlands Cancer Registry ◽  
Treatment Type

301 Background: Prediction models in cancer care can provide personalized prediction outcomes and can aid in shared decision making. Existing prediction models for esophageal and gastric cancer (EGC), however, are mostly aimed at predicting survival after a curative treatment has already been completed. The aim of this study is to develop prediction models, called SOURCE, to predict overall survival at diagnosis in potentially curable and metastatic EGC patients. Methods: The data from 12,756 EGC patients diagnosed between 2014-2017 were retrieved from the prospective Netherlands Cancer Registry. Four Cox regression models were created for potentially curable and metastatic cancers of the esophagus and stomach. Predictors, including treatment type, were selected using the Akaike Information Criterion. The models were validated with temporal cross-validation on their concordance-index (c-index) and calibration. Results: The validated model’s c-index is 0.76 for potentially curable cancer. For the metastatic models, the c-indices are 0.71 and 0.70 for esophageal and gastric cancer, respectively. The calibration intercepts and slopes lie in the 95% confidence interval of 0 and 1, respectively. The included model variables are given in Table. Conclusions: The SOURCE prediction models show fair c-indices and an overall good calibration. The models are the first in EGC to include treatment as a predictor. The models predict survival at diagnosis for a variety of treatments and therefore could have a high clinical applicability. Future research is needed to demonstrate the effect on shared decision making in clinical practice. [Table: see text]

Superior prognosis prediction performance of deep learning for gastric cancer compared to Yonsei prognosis prediction model using Cox regression.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 164-164 ◽  
Author(s):  
Woo Jin Hyung ◽  
Taeil Son ◽  
Minseok Park ◽  
Hansang Lee ◽  
Youn Nam Kim ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Deep Learning ◽  
Prediction Model ◽  
Survival Probability ◽  
Cox Regression ◽  
External Validation ◽  
Prognosis Prediction ◽  
Average Accuracy ◽  
Staging Systems

164 Background: Staging systems for cancer are critical to predict the prognosis of patients. Current staging systems for gastric cancer have limitations to predict individualized and precise prediction of patient’s survival after treatment. We aimed to develop prediction model based on deep learning by estimating the survival probability of patients who underwent gastrectomy. Methods: To predict the survival probability, we used a deep neural network model which consisted of 5 layers: input layer, 3 fully connected layer, and output layer with 8 characteristics (age, sex, histology, depth of tumor, number of metastatic and examined lymph node, presence of distant metastasis, and resection extent) of patients which was previously published Yonsei prediction model using Cox regression. Each layer functioned as the nonlinear weighted sum of lower layer. Five-year overall survival was predicted using the deep learning method and it was compared to Yonsei prediction model. The average area under the curve (AUC) was compared between the models. For internal validation, 5-fold cross validations were carried out. We also performed external validation with a dataset from another hospital (n = 1549). . Results: Deep learning predicted 5-year overall survival of patients with an average accuracy of 83.5% in the test set. The average AUC of deep learning by integrating 8 characteristics was significantly higher than that of Yonsei prediction model (AUC: 0.844 vs. 0.831, P < 0.001) with the same variables. In the external validation the average accuracy of survival prediction was 84.1%. The AUC was also greater in a dataset from other hospital in Korea (AUC: 0.852 vs. 0.847, P = 0.023) Conclusions: Prognosis prediction with deep learning showed superior survival predictive power compared to prediction model using Cox regression. It can provide individualized and precise stratification based on the risk using characteristics of gastric cancer patients.

scholarly journals SOURCE: Prediction Models for Overall Survival in Patients With Metastatic and Potentially Curable Esophageal and Gastric Cancer

2021 ◽  
Vol 19 (4) ◽  
pp. 403-410
Author(s):  
Héctor G. van den Boorn ◽  
Ameen Abu-Hanna ◽  
Nadia Haj Mohammad ◽  
Maarten C.C.M. Hulshof ◽  
Suzanne S. Gisbertz ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Proportional Hazards ◽  
Prediction Models ◽  
Metastatic Cancer ◽  
Cox Proportional Hazards ◽  
Future Research ◽  
Survival Prediction ◽  
Netherlands Cancer Registry ◽  
Treatment Type

Background: Personalized prediction of treatment outcomes can aid patients with cancer when deciding on treatment options. Existing prediction models for esophageal and gastric cancer, however, have mostly been developed for survival prediction after surgery (ie, when treatment has already been completed). Furthermore, prediction models for patients with metastatic cancer are scarce. The aim of this study was to develop prediction models of overall survival at diagnosis for patients with potentially curable and metastatic esophageal and gastric cancer (the SOURCE study). Methods: Data from 13,080 patients with esophageal or gastric cancer diagnosed in 2015 through 2018 were retrieved from the prospective Netherlands Cancer Registry. Four Cox proportional hazards regression models were created for patients with potentially curable and metastatic esophageal or gastric cancer. Predictors, including treatment type, were selected using the Akaike information criterion. The models were validated with temporal cross-validation on their C-index and calibration. Results: The validated model’s C-index was 0.78 for potentially curable gastric cancer and 0.80 for potentially curable esophageal cancer. For the metastatic models, the c-indices were 0.72 and 0.73 for esophageal and gastric cancer, respectively. The 95% confidence interval of the calibration intercepts and slopes contain the values 0 and 1, respectively. Conclusions: The SOURCE prediction models show fair to good c-indices and an overall good calibration. The models are the first in esophageal and gastric cancer to predict survival at diagnosis for a variety of treatments. Future research is needed to demonstrate their value for shared decision-making in clinical practice.

scholarly journals Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sheng Zheng ◽  
Zizhen Zhang ◽  
Ning Ding ◽  
Jiawei Sun ◽  
Yifeng Lin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
High Efficiency ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Sequencing Data ◽  
Cox Regression Analysis

Abstract Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

scholarly journals Modified Predictive Model and Nomogram by Incorporating Prebiopsy Biparametric Magnetic Resonance Imaging With Clinical Indicators for Prostate Biopsy Decision Making

2021 ◽  
Vol 11 ◽  
Author(s):  
Jin-feng Pan ◽  
Rui Su ◽  
Jian-zhou Cao ◽  
Zhen-ya Zhao ◽  
Da-wei Ren ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Prediction Model ◽  
Prostate Biopsy ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Elevated Serum ◽  
Training Group ◽  
Validation Group ◽  
Clinical Indicators

PurposeThe purpose of this study is to explore the value of combining bpMRI and clinical indicators in the diagnosis of clinically significant prostate cancer (csPCa), and developing a prediction model and Nomogram to guide clinical decision-making.MethodsWe retrospectively analyzed 530 patients who underwent prostate biopsy due to elevated serum prostate specific antigen (PSA) levels and/or suspicious digital rectal examination (DRE). Enrolled patients were randomly assigned to the training group (n = 371, 70%) and validation group (n = 159, 30%). All patients underwent prostate bpMRI examination, and T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were collected before biopsy and were scored, which were respectively named T2WI score and DWI score according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v.2) scoring protocol, and then PI-RADS scoring was performed. We defined a new bpMRI-based parameter named Total score (Total score = T2WI score + DWI score). PI-RADS score and Total score were separately included in the multivariate analysis of the training group to determine independent predictors for csPCa and establish prediction models. Then, prediction models and clinical indicators were compared by analyzing the area under the curve (AUC) and decision curves. A Nomogram for predicting csPCa was established using data from the training group.ResultsIn the training group, 160 (43.1%) patients had prostate cancer (PCa), including 128 (34.5%) with csPCa. Multivariate regression analysis showed that the PI-RADS score, Total score, f/tPSA, and PSA density (PSAD) were independent predictors of csPCa. The prediction model that was defined by Total score, f/tPSA, and PSAD had the highest discriminatory power of csPCa (AUC = 0.931), and the diagnostic sensitivity and specificity were 85.1% and 87.5%, respectively. Decision curve analysis (DCA) showed that the prediction model achieved an optimal overall net benefit in both the training group and the validation group. In addition, the Nomogram predicted csPCa revealed good estimation when compared with clinical indicators.ConclusionThe prediction model and Nomogram based on bpMRI and clinical indicators exhibit a satisfactory predictive value and improved risk stratification for csPCa, which could be used for clinical biopsy decision-making.

scholarly journals Efficacy and Safety of FLOT regimen vs. DCF, FOLFOX, and ECF regimens as Perioperative Chemotherapy Treatments for Resectable Gastric Cancer Patients

2021 ◽  
Author(s):  
Pegah Farrokhi ◽  
Alireza Sadeghi ◽  
Mehran sharifi ◽  
Payam Dadvand ◽  
Rachel Riechelmann ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Cox Regression ◽  
Progression Free Survival ◽  
R0 Resection ◽  
P Value ◽  
Perioperative Chemotherapy ◽  
Resectable Gastric Cancer ◽  
Gastric Cancer Patients

AbstractAimThis study aimed to evaluate and compare the efficacy and toxicity of common regimens used as perioperative chemotherapy including ECF, DCF, FOLFOX, and FLOT to identify the most effective chemotherapy regimen with less toxicity.Material and MethodsThis retrospective cohort study was based on 152 eligible gastric cancer patients recruited in a tertiary oncology hospital in Isfahan, Iran (2014-2019). All resectable gastric cancer patients who had received one of the four chemotherapy regimens including ECF, DCF, FOLFOX, or FLOT, and followed for at least one year (up to five years) were included. The primary endpoint of this study was Overall Survival (OS), Progression-Free Survival (PFS), Overall Response Rate (ORR), and R0 resection. We also considered toxicity according to CTCAE (v.4.0) criteria as a secondary endpoint. Cox -regression models were used applied to estimate OS and PFS time, controlled for relevant covariates.ResultsOf included patients, 32(21%), 51(33.7%), 37(24.3%), and 32(21%) had received ECF, DCF, FOLFOX and FLOT, respectively. After the median 25 months follow-up, overall survival was higher with the FLOT regimen in comparison with other regimens (hazard ratio [HR] = 0. 052). The median OS of the FLOT regimen was not reachable in Kaplan-Meier analysis and the median OS was 28, 26, and 23 months for DCF, FOLOFX, and ECF regimens, respectively. On the other hand, a median PFS of 25, 17, 15, and 14 months was observed for FLOT, DCF, FOLFOX, and ECF regimens, respectively (Log-rank = 0. 021). FLOT regimen showed 84. 4% ORR which was notably higher than other groups (p-value<0. 01).ConclusionsFor resectable gastric cancer patients, the perioperative FLOT regimen seemed to lead to a significant improvement in patients’ OS and PFS in comparison with ECF, DCF, and FOLFOX regimens. As such, the FLOT regimen could be considered as the optimal option for managing resectable gastric cancer patients.

Overexpression of a novel oncogene Cadherin 6 correlates with tumor progression and poor prognosis in gastric cancer

2021 ◽  
Author(s):  
Zongxian Zhao ◽  
Shuliang Li ◽  
Shilong Li ◽  
Jun Wang ◽  
Hai Lin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Roc Curve ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Expression Level ◽  
Diagnostic Efficiency ◽  
Gastric Tissue ◽  
Diagnosis And Prognosis ◽  
T Classification

Abstract BackgroundGastric cancer (GC) is one of the most common and fatal cancers worldwide and effective biomarkers aids in GC management and prognosis. Hence, we explored the role and function of cadherin 6 (CDH6) in diagnosis and prognosis of gastric cancer. MethodsThe expression level of CDH6 in GC tissue and normal gastric tissue were analyzed using multiple public databases. Gene set enrichment analysis (GSEA) was performed using The Cancer Genome Atlas dataset (TCGA). The diagnostic efficiency of CDH6 expression in GC patients was determined through receiver operating characteristic (ROC) curve analysis. The associations between clinical variables and expression of CDH6 were evaluated statistically and the prognostic factors for overall survival were analyzed by univariate and multivariate Cox regression. Forty-four GC tissues, corresponding adjacent normal tissues (n=20), and detailed clinical information were collected from Tianjin Medical University General Hospital, CDH6 expression level was detected for further validation. ResultsCDH6 was upregulated in GC samples compared with normal gastric tissue, and GSEA identified the citrate cycle tricarboxylic (TCA) cycle, extracellular matrix (ECM) receptor interaction, glyoxylate and dicarboxylate metabolism oxidative phosphorylation, and pentose phosphate pathway as differentially enriched in GCs. According to the area under the ROC curve (AUC) (AUC=0.829 in TCGA and 0.966 in GSE54129), CDH6 had high diagnostic efficiency. Patients with high expression of CDH6 was associated with higher T classification and worse prognoses than those with low CDH6 expression in GC. Univariate and multivariate Cox regression analysis showed that CDH6 was an independent risk factor for overall survival (univariate: HR = 1.305, P = 0.002, multivariate: HR = 1.481, P < 0.001). ConclusionCDH6 was upregulated in GC and high CDH6 expression indicated higher T classification and worse prognoses. CDH6 could be a potentially independent molecular biomarker for diagnosis and prognosis of GC.

Nomograms Predicting Overall Survival and Cancer Special Survival for Cervical Cancer in stage IIIC1

2021 ◽  
Author(s):  
Yifan Feng ◽  
Ye Wang ◽  
Yangqin Xie ◽  
Shuwei Wu ◽  
Yuyang Li ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Overall Survival ◽  
Tumor Size ◽  
Random Sampling ◽  
Prediction Models ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Training Group ◽  
Data Sets ◽  
External Data

Abstract BackgroundThe purpose of this study is to explore the factors that affect the prognosis of overall survival (OS) and cancer special survival (CSS) in cervical cancer with stage IIIC1 and establish nomogram models to predict this prognosis.MethodsData from The Surveil-lance, Epidemiology, and End Results (SEER) Program meeting the inclusion criterions were classified into training group, and data of validation were obtained from the First Affiliated Hospital of Anhui Medical University from 2010 to 2019. The incidence, Kaplan‐Meier curves, OS and CSS of stage IIIC1 were evaluated according to the training group. Nomograms were established according to the results of univariate and multivariate Cox regression models. Harrell’s C-index and receiver operating characteristic curve (ROC) were calculated to measure the accuracy of the prediction models. Calibration plots show the relationship between the predicted probability and the actual outcome. Decision-curve analysis (DCA) was applied to evaluate the clinical applicability of the constructed nomogram.ResultsThe incidence of pelvic lymph node metastasis, a high-risk factor for prognosis in cervical cancer, decreased slightly over time. There are eight independent prognostic variables for OS, including age, race, histology, differentiation, extension range, tumor size, radiation recode and surgery, but seven for CSS with age excluded. Nomograms of OS and CSS were established based on the results. The C-index for the nomograms of OS and CSS were 0.692, 0.689 respectively when random sampling of SEER data sets, and 0.706, 0.737 respectively when random sampling of external data sets. AUCs for the nomogram of OS were 0.648, 0.644 respectively, and 0.683, 0.675 for the nomogram of CSS. Calibration plots for the nomograms were almost identical to the actual observations. The DCA also proved the value of the two models.ConclusionAge, race, histology, differentiation, extension range, tumor size, radiation recode and surgery were all independent prognosis factors for OS. Only age excepts in CSS. OS and CSS nomograms were established in our study based on the result of multivariate Cox proportional hazard regression, and both own good predictive and clinical application value after validation.

P5645Lifetime sex-specific sudden cardiac death prediction using ECG global electrical heterogeneity: the atherosclerosis risk in communities (ARIC) study

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S Howell ◽  
E Perez-Alday ◽  
D German ◽  
A Bender ◽  
N Rogovoy ◽  
...  
Keyword(s):  
Sudden Cardiac Death ◽  
Prediction Model ◽  
Cardiac Death ◽  
Prediction Models ◽  
Cox Regression ◽  
Density Lipoprotein ◽  
Screening Methods ◽  
Atherosclerosis Risk In Communities ◽  
Clinical Variables ◽  
Atherosclerosis Risk

Abstract Background Sex-based differences in sudden cardiac death (SCD) exist and screening methods for SCD are inadequate. Purpose To develop sex-specific lifetime risk prediction models using electrocardiographic (ECG) global electrical heterogeneity (GEH) and clinical characteristics. Methods Participants from the Atherosclerosis Risk in Communities study with analyzable ECGs (n=14,725; age, 54.2±5.8 yrs; 55% female, 74% white) were followed up for 24.4 years (median). Traditional ECG and GEH variables were measured on 12-lead ECGs. A Cox regression model was used to develop a prediction model. In women, the final model included race, age, coronary heart disease (CHD), stroke, hypertension, diabetes, smoking, high-density lipoprotein, albumin, uric acid, education level, heart rate, QTc, sum absolute QRST integral, spatial peak QRS-T angle. In men, the final prediction model included age, race, CHD, stroke, hypertension, diabetes, total cholesterol, physical activity, smoking, serum phosphorus, albumin, chronic kidney disease, spatial area QRS-T angle, area spatial ventricular gradient (SVG) elevation and magnitude, and peak SVG magnitude. Results There were a total of 530 SCDs. Our prediction models showed robust prediction of SCD in both sexes [(Harrell's C-statistic women 0.863 (95% CI 0.845–0.882), men 0.786 (95% CI 0.786–0.803)]. In women when ECG and GEH variables were added to clinical variables, the net reclassification improved by 9% (P=0.001) (Table). In men there was no significant reclassification improvement. Net reclassification Lifetime SCD Risk: Clinical + ECG + GEH Variables Women Men <5% 5–15% >15% Total <5% 5–15% >15% Total SCD Cases <5% 82 14 0 96 103 16 0 119 5–15% 7 59 10 76 12 116 12 140 >15% 0 0 20 20 0 5 74 79 Lifetime SCD Risk: Total 89 73 30 192 115 137 86 338 Clinical Variables Only Non-Cases <5% 6,956 131 2 7,089 4,411 264 0 4,675 5–15% 180 509 42 731 210 1,059 48 1,317 >15% 0 28 84 112 0 56 214 270 Total 7,136 668 128 7,932 4,621 1,379 262 6,262 Conclusions We were the first to develop sex-specific lifetime SCD prediction models. The addition of ECG GEH to clinical variables improved SCD risk reclassification in women, but not in men. Prediction of SCD was more accurate in women as compared to men.

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