Prediction Model for Estimating the Survival Benefit of Adjuvant Radiotherapy for Gallbladder Cancer

2008 ◽  
Vol 26 (13) ◽  
pp. 2112-2117 ◽  
Author(s):  
Samuel J. Wang ◽  
C. David Fuller ◽  
Jong-Sung Kim ◽  
Dean F. Sittig ◽  
Charles R. Thomas ◽  
...  
Keyword(s):  
Prediction Model ◽  
Gallbladder Cancer ◽  
Adjuvant Radiotherapy ◽  
Survival Benefit ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Survival Prediction ◽  
Published Data ◽  
Net Survival

Purpose The benefit of adjuvant radiotherapy (RT) for gallbladder cancer remains controversial because most published data are from small, single-institution studies. The purpose of this study was to construct a survival prediction model to enable individualized predictions of the net survival benefit of adjuvant RT for gallbladder cancer patients based on specific tumor and patient characteristics. Methods A multivariate Cox proportional hazards model was constructed using data from 4,180 patients with resected gallbladder cancer diagnosed from 1988 to 2003 from the Surveillance, Epidemiology, and End Results database. Patient and tumor characteristics were included as covariates and assessed for association with overall survival (OS) with and without adjuvant RT. The model was internally validated for discrimination and calibration using bootstrap resampling. Results On multivariate regression analysis, the model showed that age, sex, papillary histology, stage, and adjuvant RT were significant predictors of OS. The survival prediction model demonstrated good calibration and discrimination, with a bootstrap-corrected concordance index of 0.71. The model predicts that adjuvant RT provides a survival benefit in node-positive or ≥ T2 disease. A nomogram and a browser-based software tool were built from the model that can calculate individualized estimates of predicted net survival gain attributable to adjuvant RT, given specific input parameters. Conclusion In the absence of large, prospective, randomized, clinical trial data, a regression model can be used to make individualized predictions of the expected survival improvement from the addition of adjuvant RT after gallbladder cancer resection.

Proliferation-dominant high-grade astrocytoma: survival benefit associated with extensive resection of FLAIR abnormality region

2020 ◽  
Vol 132 (4) ◽  
pp. 998-1005 ◽  
Author(s):  
Haihui Jiang ◽  
Yong Cui ◽  
Xiang Liu ◽  
Xiaohui Ren ◽  
Mingxiao Li ◽  
...  
Keyword(s):  
Survival Benefit ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Characteristic Curve ◽  
Extent Of Resection ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
High Grade ◽  
Extensive Resection ◽  
High Grade Astrocytoma

OBJECTIVEThe aim of this study was to investigate the relationship between extent of resection (EOR) and survival in terms of clinical, molecular, and radiological factors in high-grade astrocytoma (HGA).METHODSClinical and radiological data from 585 cases of molecularly defined HGA were reviewed. In each case, the EOR was evaluated twice: once according to contrast-enhanced T1-weighted images (CE-T1WI) and once according to fluid attenuated inversion recovery (FLAIR) images. The ratio of the volume of the region of abnormality in CE-T1WI to that in FLAIR images (VFLAIR/VCE-T1WI) was calculated and a receiver operating characteristic curve was used to determine the optimal cutoff value for that ratio. Univariate and multivariate analyses were performed to identify the prognostic value of each factor.RESULTSBoth the EOR evaluated from CE-T1WI and the EOR evaluated from FLAIR could divide the whole cohort into 4 subgroups with different survival outcomes (p < 0.001). Cases were stratified into 2 subtypes based on VFLAIR/VCE-T1WIwith a cutoff of 10: a proliferation-dominant subtype and a diffusion-dominant subtype. Kaplan-Meier analysis showed a significant survival advantage for the proliferation-dominant subtype (p < 0.0001). The prognostic implication has been further confirmed in the Cox proportional hazards model (HR 1.105, 95% CI 1.078–1.134, p < 0.0001). The survival of patients with proliferation-dominant HGA was significantly prolonged in association with extensive resection of the FLAIR abnormality region beyond contrast-enhancing tumor (p = 0.03), while no survival benefit was observed in association with the extensive resection in the diffusion-dominant subtype (p=0.86).CONCLUSIONSVFLAIR/VCE-T1WIis an important classifier that could divide the HGA into 2 subtypes with distinct invasive features. Patients with proliferation-dominant HGA can benefit from extensive resection of the FLAIR abnormality region, which provides the theoretical basis for a personalized resection strategy.

Predicting self-reported depression after the onset of multiple sclerosis using genetic and non-genetic factors

2020 ◽  
pp. 135245852092107
Author(s):  
Frances M Wang ◽  
Mary F Davis ◽  
Farren BS Briggs
Keyword(s):  
Multiple Sclerosis ◽  
Prediction Model ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Genetic Risk Score ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Depression Risk ◽  
History Of ◽  
History Of Depression

Background: Persons with multiple sclerosis (PwMS) are disproportionately burdened by depression compared to the general population. While several factors associated with depression and depression severity in PwMS have been identified, a prediction model for depression risk has not been developed. In addition, it is unknown if depression-related genetic variants, including Apolipoprotein E ( APOE), would be informative for predicting depression in PwMS. Objective: To develop a depression prediction model for PwMS who did not have a history of depression prior MS onset. Methods: The study population included 917 non-Hispanic white PwMS. An optimized multivariable Cox proportional hazards model for time to depression was generated using non-genetic variables, to which APOE and a depression-related genetic risk score were included. Results: Having a mother who had a history of depression, having obstructive pulmonary disease, obesity and other physical disorders at MS onset, and affect-related symptoms at MS onset predicted depression risk (hazards ratios (HRs): 1.6–2.3). Genetic variables improved the prediction model’s performance. APOE ε4/ε4 and ε2/x conferred increased (HR = 2.5, p = 0.026) and decreased (HR = 0.65, p = 0.046) depression risk, respectively. Conclusion: We present a prediction model aligned with The Precision Medicine Initiative, which integrates genetic and non-genetic predictors to inform depression risk stratification after MS onset.

Multiple chemotherapy (CT) lines in metastatic breast cancer (MBC): Which survival benefit for women with hormone receptor (HR)-positive disease?

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 589-589 ◽  
Author(s):  
Raffaella Palumbo ◽  
Antonio Bernardo ◽  
Alberto Riccardi ◽  
Federico Sottotetti ◽  
Cristina Teragni ◽  
...  
Keyword(s):  
Survival Benefit ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Univariate Analysis ◽  
Metastatic Breast ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Factors Affecting ◽  
Significant Difference ◽  
Third Line

589 Background: Although the development of modern systemic therapies has clearly improved outcome of patients with MBC, the true impact of further CT on overall survival (OS) and QoL of these women is still debated. The aim of this study was to determine which benefit could be brought by successive CT lines in patients with HR-positive disease, aiming to identify factors affecting outcome and survival. Methods: This retrospective analysis included 980 women treated with CT for MBC at our Institution over a eight year period (July 2000-July 2008). With OS data updated in March 2010, the median follow-up was 146 months (range 48-198), OS and time to treatment failure (TTF) were calculated according to the Kaplan-Meyer method for each CT line. Cox proportional hazards model was used to identify factors that could influence TTF and OS. Results: Median OS evaluated from day 1 of each CT line decreased with the line number from 34.8 months for first line to 8.2 months for 7 or more lines). Median TTF ranged from 9.2 months to 7.8 and 6.4 months for the first, second and third line, respectively, with no significant decrease observed beyond the third line (median 5.2 months, range 4.8-6.2). No statistically significant difference was found between HR-positive and HR-negative patients in terms of OS and TTF by each CT line. In univariate analysis factors positively linked to a longer duration of TTF for each CT line were positive hormonal receptor status, more than 3 hormonotherapy lines, absence of liver metastasis, adjuvant CT exposure, response to CT for the metastatic disease; in the multivariate analysis the duration of TTF for each CT line was the only one factor with significant impact on survival benefit for subsequent treatments, in both HR-positive and negative populations (p<0.001). Conclusions: Our results support the benefit of multiple lines of CT in a significant subset of women treated for MBC, since each CT line may contribute to a longer OS. Of interest, such a benefit was also observed for patients with HR-positive disease, although the number of hormonotherpy lines received did not significantly influence the outcome.

The adjuvant radiotherapy benefits on pure mucinous breast carcinoma patients in different age stages: A SEER-based study.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e12519-e12519
Author(s):  
Xiaoqing Wei ◽  
Li ping Dian ◽  
Fan Tang ◽  
Huijun Zhao ◽  
Honglin Situ ◽  
...  
Keyword(s):  
Breast Carcinoma ◽  
Adjuvant Radiotherapy ◽  
Clinical Decision Making ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Breast Cancer Patients ◽  
Significant Difference ◽  
Mucinous Breast Carcinoma

e12519 Background: The aim of this analysis is to study the prognostic impacts of adjuvant radiotherapy on pure mucinous breast carcinoma patients in different age stages. Methods: Patients diagnosed pure mucinous breast carcinoma between 1998 and 2015 were identified from the Serveillance,Epidemiology,and Results End database. Chi-square, Kaplan-Meier method, Multivariate Cox proportional hazards models were used for statistical analysis. Results: We enrolled 10656 patients, including 5758 (54.0%) and 4898 (46.0%) in the with radiotherapy and without radiotherapy cohorts, respectively.The K-M method shows age is an independent prognostic factor in pure mucinous breast carcinoma patients with radiotherapy( p= 0.000 ). Compared with the younger groups (<45y and 46-54y),the older groups (55-64y, 65-75y and >75y) show a greater benefits with radiotherapy. The multivariate Cox proportional hazards model shows significant difference (all p= 0.000), the group 55-64y((HR = 0.58, 95%CI:0.453–0.742),the group 65-75y(HR = 0.709, 95% CI: 0.610–0.825),the group>75y (HR = 0.613, 95% CI: 0.543–0.691), which indicates a better radiotherapy benefits in older groups(especially in 65-75y). Conclusions: This study shows a better benefits of postoperative adjuvant radiotherapy in pure mucinous breast carcinoma older patients. Owing to the common incompleteness of systematic basic treatments in older breast cancer patients,our real world analysis results may be helpful for the clinical decision-making.

scholarly journals Application of Data Science Technology on Research of Circulatory System Disease Prediction Based on a Prospective Cohort

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 162
Author(s):  
Haijing Tang ◽  
Guo Chen ◽  
Yu Kang ◽  
Xu Yang
Keyword(s):  
Prediction Model ◽  
Chronic Diseases ◽  
Prediction Models ◽  
Proportional Hazards Model ◽  
Circulatory System ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Disease Prediction ◽  
System Disease ◽  
Circulatory System Disease

Chronic diseases represented by circulatory diseases have gradually become the main types of diseases affecting the health of our population. Establishing a circulatory system disease prediction model to predict the occurrence of diseases and controlling them is of great significance to the health of our population. This article is based on the prospective population cohort data of chronic diseases in China, based on the existing medical cohort studies, the Kaplan–Meier method was used for feature selection, and the traditional medical analysis model represented by the Cox proportional hazards model was used and introduced. Support vector machine research methods in machine learning establish circulatory system disease prediction models. This paper also attempts to introduce the proportion of the explanation variation (PEV) and the shrinkage factor to improve the Cox proportional hazards model; and the use of Particle Swarm Optimization (PSO) algorithm to optimize the parameters of SVM model. Finally, the experimental verification of the above prediction models is carried out. This paper uses the model training time, Accuracy rate(ACC), the area under curve (AUC)of the Receiver Operator Characteristic curve (ROC) and other forecasting indicators. The experimental results show that the PSO-SVM-CSDPC disease prediction model and the S-Cox-CSDPC circulation system disease prediction model have the advantages of fast model solving speed, accurate prediction results and strong generalization ability, which are helpful for the intervention and control of chronic diseases.

scholarly journals Survival Benefit of Adjuvant Chemotherapy After Pancreatoduodenectomy for Ampullary Adenocarcinoma: a Propensity-Matched National Cancer Database (NCDB) Analysis

2020 ◽  
Author(s):  
Sivesh K. Kamarajah ◽  
Filip Bednar ◽  
Clifford S. Cho ◽  
Hari Nathan
Keyword(s):  
Adjuvant Chemotherapy ◽  
Survival Benefit ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Margin Status ◽  
Cox Proportional Hazards Model ◽  
National Cancer Database ◽  
Neoadjuvant Radiotherapy ◽  
Ampullary Adenocarcinoma

Abstract Background The benefit of adjuvant chemotherapy (AC) after pancreatoduodenectomy (PD) for ampullary adenocarcinoma is uncertain. We aimed to evaluate the association of AC with survival in patients with resected ampullary adenocarcinoma. Methods Using the National Cancer Database (NCDB) data from 2004 to 2016, patients with non-metastatic ampullary adenocarcinoma who underwent PD were identified. Patients with neoadjuvant radiotherapy and chemotherapy and survival < 6 months were excluded. Propensity score matching was used to account for treatment selection bias. A multivariable Cox proportional hazards model was then used to analyze the association of AC with survival. Results Of 3186 (43%) AC and 4172 (57%) no AC (noAC) patients, 1720 AC and 1720 noAC patients remained in the cohort after matching. Clinicopathologic variables were well balanced after matching. After matching, AC was associated with improved survival (median 47.5 vs 39.6 months, p = 0.003), which remained after multivariable adjustment (HR: 0.83, CI95%: 0.76–0.91, p < 0.001). Multivariable interaction analyses showed that this benefit was seen irrespective of nodal status: N0 (HR: 0.81, CI95%: 0.68–0.97, p < 0.001), N1 (HR: 0.65, CI95%: 0.61–0.70, p < 0.001), N2 (HR: 0.73, CI95%: 0.59–0.90, p = 0.003), N3 (HR: 0.59, CI95%: 0.44–0.78, p < 0.001); and margin status: R0 (HR: 0.85, CI95%: 0.77–0.94, p < 0.001), R1 (HR: 0.69, CI95%: 0.48–1.00, p < 0.001). Stratified analyses by nodal and margin status demonstrated consistent results. Conclusion In this large retrospective cohort study, AC after resected ampullary adenocarcinoma was associated with a survival benefit in patients, including patients with node-negative and margin-negative disease.

scholarly journals Two-stage neural-network based prognosis models using pathological image and transcriptomic data: An application in hepatocellular carcinoma patient survival prediction

2020 ◽  
Author(s):  
Zhucheng Zhan ◽  
Noshad Hossenei ◽  
Olivier Poirion ◽  
Maria Westerhoff ◽  
Eun-Young Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Hepatocellular Carcinoma ◽  
Proportional Hazards Model ◽  
Patient Survival ◽  
Complex Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
P Value ◽  
Survival Prediction ◽  
Two Stage

AbstractPathological images are easily accessible data type with potential as prognostic biomarkers. Here we extend Cox-nnet, a neural network based prognosis method previously used for transcriptomics data, to predict patient survival using hepatocellular carcinoma (HCC) pathological images. Cox-nnet based imaging predictions are more robust and accurate than Cox proportional hazards model. Moreover, using a novel two-stage Cox-nnet complex model, we are able to combine histopathology image and transcriptomics RNA-Seq data to make impressively accurate prognosis predictions, with C-index close to 0.90 and log-ranked p-value of 4e-21 in the testing dataset. This work provides a new, biologically relevant and relatively interpretable solution to the challenge of integrating multi-modal and multiple types of data, particularly for survival prediction.

Association between hand-foot skin reaction (HFSR) and survival benefit of fruquintinib in FRESCO trial.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15012-e15012
Author(s):  
Jin Li ◽  
Shukui Qin ◽  
Yuxian Bai ◽  
Yanhong Deng ◽  
Lei Yang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Skin Reaction ◽  
Survival Benefit ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Rank Test ◽  
P Value ◽  
Significant Difference ◽  
Hand Foot Skin Reaction

e15012 Background: In phase 3 FRESCO trial, fruquintinib demonstrated a statistically significant and clinically meaningful overall survival benefit in Chinese metastatic colorectal cancer (mCRC) patients. As a known adverse effect of vascular endothelial growth factor receptor (VEGFR) inhibitors, hand-foot skin reaction (HFSR) was commonly reported as a drug-related adverse event (AE) in fruquintinib group. This retrospective analysis explored whether HFSR in fruquintinib group is associated with survival benefit in FRESCO. Methods: This analysis used a subpopulation of intent-to-treat population who at least completed one cycle and entered cycle two of fruquintinib treatment. Patients randomized to receive fruquintinib 5 mg/day during the first 3 weeks of each 4-week cycle were divided into subgroups based on whether they reported HFSR. Overall survival (OS) and progression-free survival (PFS) were evaluated by Kaplan-Meier method. Hazard ratio (HR) was estimated through Cox proportional hazards model. P-value was generated from log-rank test. Results: Among a total of 255 fruquintinib-treated patients who at least completed one cycle and entered cycle two, 52% (n = 133) reported HFSR of any grade. The median time-to-onset of HFSR (any grade) was 21 days and approximate 75% patients reported HFSR after cycle two treatment completion. The baseline characteristics were well balanced between HFSR reported and non-reported subgroups. Patients who reported HFSR showed both OS and PFS benefit with statistical significant difference comparing with HFSR non-reported patients in fruquintinib group. Fruquintinib significantly decreased 43% death risk in HFSR reported patients and prolonged the median OS to 11.14 months in comparison with HFSR non-reported patients (median: 11.24 vs 7.54 months; HR = 0.57, 95% CI: 0.42-0.78; p < 0.001). Similarly, Patients reported HFSR had a significantly longer PFS than those who did not reported HFSR in the fruquintinib group (median: 5.49 vs 3.48 months; HR = 0.70, 95% CI: 0.54-0.91; p = 0.008). Conclusions: This post-hoc analysis indicates that patients who had HFSR had a greater survival benefit from fruquintinib in Chinese mCRC patients. Clinical trial information: NCT02314819 .

scholarly journals Survival Analysis and Prediction Model for Pulmonary Sarcomatoid Carcinoma Based on SEER Database

2021 ◽  
Vol 11 ◽  
Author(s):  
Mingjing Chen ◽  
Qiao Yang ◽  
Zihan Xu ◽  
Bangyu Luo ◽  
Feng Li ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Prediction Model ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Clinical Stage ◽  
Sarcomatoid Carcinoma ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Pulmonary Sarcomatoid Carcinoma ◽  
Testing Set

ObjectiveThis study aimed to investigate the incidence of the pulmonary sarcomatoid carcinoma (PSC), to compare the clinical characteristics and overall survival (OS) of patients with PSC and those with other non-small-cell lung cancer (oNSCLC), so as to analyze the factors affecting the OS of patients with PSC and construct a nomogram prediction model.MethodsData of patients with PSC and those with oNSCLC diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results database were collected. The age-adjusted incidence of PSC was calculated. The characteristics of patients with PSC and those with oNSCLC were compared, then the patients were matched 1:2 for further survival analysis. Patients with PSC were randomly divided into training set and testing set with a ratio of 7:3. The Cox proportional hazards model was used to identify the covariates associated with the OS. Significant covariates were used to construct the nomogram, and the C-index was calculated to measure the discrimination ability. The accuracy of the nomogram was compared with the tumor–node–metastasis (TNM) clinical stage, and the corresponding area under the curve was achieved.ResultsA total of 1049 patients with PSC were enrolled, the incidence of PSC was slowly decreased from 0.120/100,000 in 2004 to 0.092/100,000 in 2015. Before PSM, 793 PSC patients and 191356 oNSCLC patients were identified, the proportion of male, younger patients (&lt;65 years), grade IV, TNM clinical stage IV was higher in the PSC. The patients with PSC had significantly poorer OS compared with those with oNSCLC. After PSM, PSC still had an extremely inferior prognosis. Age, sex, TNM clinical stage, chemotherapy, radiotherapy, and surgery were independent factors for OS. Next, a nomogram was established based on these factors, and the C-indexs were 0.775 and 0.790 for the training and testing set, respectively. Moreover, the nomogram model indicated a more comprehensive and accurate prediction than the TNM clinical stage.ConclusionsThe incidence of PSC was slowly decreased. PSC had a significantly poor prognosis compared with oNSCLC. The nomogram constructed in this study accurately predicted the prognosis of PSC, performed better than the TNM clinical stage.

scholarly journals A Prediction Model for Prediabetes Risk in Middle-Aged and Elderly Populations: A Prospective Cohort Study in China

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jiahua Wu ◽  
Jiaqiang Zhou ◽  
Xueyao Yin ◽  
Yixin Chen ◽  
Xihua Lin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Plasma Glucose ◽  
Proportional Hazards Model ◽  
Predictive Accuracy ◽  
Serum Insulin ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Discriminative Ability ◽  
Predictive Values ◽  
Family History Of Diabetes

Background. To investigate indicators for prediabetes risk and construct a prediction model for prediabetes incidences in China. Methods. In this study, 551 adults aged 40–70 years had normal glucose tolerance (NGT) and normal hemoglobin A1c (HbA1c) levels at baseline. Baseline data including demographic information, anthropometric measurements, and metabolic profile measurements were collected. The associations between possible indicators and prediabetes were assessed by the Cox proportional-hazards model. The predictive values were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). Results. During an average of 3.35 years of follow-up, the incidence of prediabetes was found to be 19.96% (n = 110). In the univariate analyses, fasting plasma glucose (FPG), fasting serum insulin (FINS), 2 h plasma glucose (2hPG), HbA1c, serum uric acid (SUA), waist circumference (WC), smoking, and family history of diabetes (FHD) were found to be significantly correlated with prediabetes. In the multivariable analyses, WC (hazard ratio (HR): 1.032; 95% confidence interval (CI): 1.010, 1.053; p = 0.003 ), FHD (HR: 1.824; 95% CI: 1.250, 2.661; p = 0.002 ), HbA1c (HR: 1.825; 95% CI: 1.227, 2.714; p = 0.003 ), and FPG (HR: 2.284; 95% CI: 1.556, 3.352; p < 0.001 ) were found to be independent risk factors for prediabetes. A model that encompassed WC, FHD, HbA1c, and FPG for predicting prediabetes exhibited the largest discriminative ability (AUC: 0.702). Conclusions. WC, FHD, HbA1c, and FPG are independently correlated with the risk of prediabetes. Furthermore, the combination of these predictors enhances the predictive accuracy of prediabetes.

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