Dataset Information

Pretreatment lifestyle behaviors as survival predictors for patients with nasopharyngeal carcinoma.

ABSTRACT:

Background

Lifestyle behaviors have been widely reported to influence the survival of patients with head and neck cancer. However, the relationship between pretreatment lifestyle behaviors and survival among patients with nasopharyngeal carcinoma (NPC) is unclear.

Methods

A prospective cohort study was designed to determine the relationship between lifestyle behaviors and survival in 1,533 NPC patients recruited from October 2005 to October 2007. Pretreatment lifestyle behaviors (such as body-mass index [BMI], smoking, alcohol, diet) of the patients were investigated. Univariate and multivariate proportional-hazards models were used to assess the impact of lifestyle behaviors on patient survival.

Results

Smoking was a predictor of survival; both current smokers (hazard ratio [HR] = 1.88; 95% CI, 1.33 to 2.65) and heavy smokers (? 25 Pack-years; HR = 1.84; 95% CI, 1.30 to 2.60) showed associations with poor survival. Higher BMI was significantly associated with a lower risk of death (P(trend) = 0.002). Compared with under/normal-weight patients (BMI less than 22.99 kg/m(2)), the multivariate HR for survival was 0.66 (95% CI, 0.48 to 0.90) and 0.47 (95% CI, 0.23 to 0.97) for overweight and obese patients, respectively. No alcohol intake and high fruit intake were associated with favorable survival in the univariate analysis but lost significance in the multivariate model.

Conclusion

Our findings indicate that pretreatment lifestyle behaviors, especially smoking status and BMI, as easily available data, provide prognostic value for survival in NPC patients.

SUBMITTER: Shen GP

PROVIDER: S-EPMC3348163 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

Pretreatment lifestyle behaviors as survival predictors for patients with nasopharyngeal carcinoma.

Shen Guo-Ping GP Xu Feng-Hua FH He Fen F Ruan Hong-Lian HL Cui Cui C Chen Li-Zhen LZ Zeng Yi-Xin YX Jia Wei-Hua WH

PloS one 20120508 5

<h4>Background</h4>Lifestyle behaviors have been widely reported to influence the survival of patients with head and neck cancer. However, the relationship between pretreatment lifestyle behaviors and survival among patients with nasopharyngeal carcinoma (NPC) is unclear.<h4>Methods</h4>A prospective cohort study was designed to determine the relationship between lifestyle behaviors and survival in 1,533 NPC patients recruited from October 2005 to October 2007. Pretreatment lifestyle behaviors ( ...[more]

PMID: 22590554

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Project description:ObjectivesTo determine the predictive value of pretreatment MRI texture analysis for progression-free survival (PFS) in patients with primary nasopharyngeal carcinoma (NPC).MethodsEthical approval by the institutional review board was obtained for this retrospective analysis. In 79 patients with primary NPC, texture analysis of the primary tumour was performed on pretreatment T2 and contrast-enhanced T1-weighted images (T2WIs and CE-T1WIs). The Cox proportional hazards model was used to determine the association of texture features, tumour volume and the tumour-node-metastasis (TNM) stage with PFS. Survival curves were plotted using the Kaplan-Meier method. The prognostic performance was evaluated with the receiver operating characteristic (ROC) analyses and C-index.ResultsTumour volume (hazard ratio, 1.054; 95% confidence interval [CI], 1.016-1.093) and CE-T1WI-based uniformity (hazard ratio, 0; 95% CI, 0-0.001) were identified as independent predictors for PFS (p < 0.05). Kaplan-Meier analysis showed that smaller tumour volume (less than the cut-off value, 11.699 cm3) and higher CE-T1WI-based uniformity (greater than the cut-off value, 0.856) were associated with improved PFS (p < 0.05). The combination of CE-T1WI-based uniformity with tumour volume and the overall stage predicted PFS better (area under the curve [AUC], 0.825; Cindex, 0.794) than the tumour volume (AUC, 0.659; C-index, 0.616) or the overall stage (AUC, 0.636; C-index, 0.627) did (p < 0.05).ConclusionsA texture parameter of pretreatment CE-T1WI-based uniformity improves the prediction of PFS in NPC patients.Key points• Higher CE-T1WI-based uniformity and smaller tumour volume are predictive of improved PFS in NPC patients. • The combination of CE-T1WI-based uniformity with tumour volume and the overall stage has a better predictive ability for PFS than the tumour volume or the overall stage alone. • Pretreatment MRI texture analysis has a prognostic value for NPC patients.

Project description:Background and purpose: Adaptive radiotherapy (ART) can compensate for the dosimetric impacts induced by anatomic and geometric variations in patients with nasopharyngeal carcinoma (NPC); Yet, the need for ART can only be assessed during the radiation treatment and the implementation of ART is resource intensive. Therefore, we aimed to determine tumoral biomarkers using pre-treatment MR images for predicting ART eligibility in NPC patients prior to the start of treatment. Methods: Seventy patients with biopsy-proven NPC (Stage II-IVB) in 2015 were enrolled into this retrospective study. Pre-treatment contrast-enhanced T1-w (CET1-w), T2-w MR images were processed and filtered using Laplacian of Gaussian (LoG) filter before radiomic features extraction. A total of 479 radiomics features, including the first-order (n = 90), shape (n = 14), and texture features (n = 375), were initially extracted from Gross-Tumor-Volume of primary tumor (GTVnp) using CET1-w, T2-w MR images. Patients were randomly divided into a training set (n = 51) and testing set (n = 19). The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for radiomic model construction in training set to select the most predictive features to predict patients who were replanned and assessed in the testing set. A double cross-validation approach of 100 resampled iterations with 3-fold nested cross-validation was employed in LASSO during model construction. The predictive performance of each model was evaluated using the area under the receiver operator characteristic (ROC) curve (AUC). Results: In the present cohort, 13 of 70 patients (18.6%) underwent ART. Average AUCs in training and testing sets were 0.962 (95%CI: 0.961-0.963) and 0.852 (95%CI: 0.847-0.857) with 8 selected features for CET1-w model; 0.895 (95%CI: 0.893-0.896) and 0.750 (95%CI: 0.745-0.755) with 6 selected features for T2-w model; and 0.984 (95%CI: 0.983-0.984) and 0.930 (95%CI: 0.928-0.933) with 6 selected features for joint T1-T2 model, respectively. In general, the joint T1-T2 model outperformed either CET1-w or T2-w model alone. Conclusions: Our study successfully showed promising capability of MRI-based radiomics features for pre-treatment identification of ART eligibility in NPC patients.

Project description:BackgroundCarcinoma penis is a rare neoplasm, and the literature is scarce on long-term survival and its predictors. The aim of the study was to determine the clinical profile and management patterns, identify predictors of survival, and the impact of education and rural/urban dwelling on survival.MethodsPatients with a histological diagnosis of carcinoma penis from January 2015 to December 2019 were included in the study. Demographics, clinical profile, education status, primary residence address, and outcomes were obtained from the case records. Distance from the treatment centre was obtained from the postal code. The primary objectives were to assess relapse-free survival (RFS) and overall survival (OS). The secondary objectives were to identify the predictors of RFS and OS and to determine the clinical profile and treatment patterns in patients with carcinoma penis in India. Time-to-event was calculated by Kaplan-Meir analysis and survival was compared by the log-rank test. Univariate and multivariable Cox regression analyses were used to find independent predictors of relapse and mortality. Logistic regression analyses to examine the associations of rural residence, education status, and distance from the treatment centre with the relapse adjusting for measured confounding variables.ResultsCase records of 102 patients treated during the above period were retrieved. The median age was 55.5 (interquartile range [IQR] 42-65 years). Ulcero-proliferative growth (65%), pain (57%), and dysuria (36%) were the most common presenting features. Clinical examination or imaging revealed inguinal lymphadenopathy in 70.6% of patients, however, only 42% of these lesions were pathologically involved. A total of 58.8% of patients were from rural areas, 46.9% had no formal education, and 50.9% had a primary residence ≥100 km from the hospital. Patients with lower education and rural households had higher TNM stages and nodal involvement. Median RFS and OS were 57.6 months (15.8 months to not reached) and 83.9 months (32.5 months to not reached), respectively. On univariate analysis tumor stage, involvement of lymph nodes, T stage, performance status, and albumin was predictive for relapse and survival. However, on multivariate analysis, the stage remained the only predictor of RFS and nodal involvement, and metastatic disease was a predictor of OS. Education status, rural habitation, and distance from the treatment centre were not predictors for relapse or survival.ConclusionsPatients with carcinoma have locally advanced disease at presentation. Rural dwellings and lower education were associated with the advanced stage but did not have a significant bearing on the survival outcomes. The stage at diagnosis and nodal involvement is the most important predictor of RFS and OS.

Project description:BackgroundTo establish and validate a predictive model combining pretreatment multiparametric MRI-based radiomic signatures and clinical characteristics for the risk evaluation of early rapid metastasis in nasopharyngeal carcinoma (NPC) patients.MethodsThe cutoff time was used to randomly assign 219 consecutive patients who underwent chemoradiation treatment to the training group (n = 154) or the validation group (n = 65). Pretreatment multiparametric magnetic resonance (MR) images of individuals with NPC were employed to extract 428 radiomic features. LASSO regression analysis was used to select radiomic features related to early rapid metastasis and develop the Rad-score. Blood indicators were collected within 1 week of pretreatment. To identify independent risk variables for early rapid metastasis, univariate and multivariate logistic regression analyses were employed. Finally, multivariate logistic regression analysis was applied to construct a radiomics and clinical prediction nomogram that integrated radiomic features and clinical and blood inflammatory predictors.ResultsThe NLR, T classification and N classification were found to be independent risk indicators for early rapid metastasis by multivariate logistic regression analysis. Twelve features associated with early rapid metastasis were selected by LASSO regression analysis, and the Rad-score was calculated. The AUC of the Rad-score was 0.773. Finally, we constructed and validated a prediction model in combination with the NLR, T classification, N classification and Rad-score. The area under the curve (AUC) was 0.936 (95% confidence interval (95% CI): 0.901-0.971), and in the validation cohort, the AUC was 0.796 (95% CI: 0.686-0.905).ConclusionsA predictive model that integrates the NLR, T classification, N classification and MR-based radiomics for distinguishing early rapid metastasis may serve as a clinical risk stratification tool for effectively guiding individual management.

Dataset Information

Pretreatment lifestyle behaviors as survival predictors for patients with nasopharyngeal carcinoma.

Background

Methods

Results

Conclusion

Publications

Pretreatment lifestyle behaviors as survival predictors for patients with nasopharyngeal carcinoma.

Similar Datasets

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