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Baseline nutritional status could be a predictor for radiation esophagitis in esophageal cancer patients undergoing radiotherapy.

ABSTRACT: Background:To investigate the relationship between baseline nutrition status and radiation esophagitis in patients with esophageal cancer treated by radiation therapy. Methods:A retrospective study was performed on 100 patients with esophageal cancer who was treated with definitive chemoradiotherapy, preoperative chemoradiation and definitive radiotherapy at the Tianjin Medical University Cancer Institute and Hospital from October 2018 and October 2019. We documented the clinical characteristics of patients, including tumor location, clinical stage, treatment, radiation dose, gross tumor volume (GTV), planning tumor volume (PTV) and Atkinson's Dysphagia score (ADS), and we recorded the nutrition status before radiation, including Patient-Generated Subjective Global Assessment (PG-SGA), body mass index (BMI), weight loss percentage in 3 mouths (WL), the level of albumin (ALB), hemoglobin (HB), C-reactive protein (CRP) and Glasgow prognostic score (GPS). These factors were correlated with radiation esophagitis using univariate and multivariate regression analyses. Results:Of 100 patients, 44% patients with PG-SGA score ?9 at baseline, suggesting severe malnutrition, 41% patients developed grade ?2 radiation esophagitis. In univariate analysis, dose >40 Gy (P=0.015), PTV ?495 cm3 (P=0.049), PG-SGA score ?9 (P=0.001), WL ?10% (P=0.019) and ALB level <35 g/L (P=0.043) were significantly associated with grade ?2 radiation esophagitis. Multivariate analysis revealed that PG-SGA score ?9 (P=0.042) was the independent predictor of radiation esophagitis. Conclusions:Baseline nutritional status associated with development of grade ?2 radiation esophagitis in patients with esophageal cancer undergoing radiotherapy.

SUBMITTER: Dong J

PROVIDER: S-EPMC7576065 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Baseline nutritional status could be a predictor for radiation esophagitis in esophageal cancer patients undergoing radiotherapy.

Dong Jie J Zhang Wencheng W Zhang Tian T Chen Xi X Zhao Jingjing J Zeng Yaqi Y Chen Yajun Y Wei Xiaoying X Lei Tongda T Wang Ping P Zhao Lujun L Wang Jun J Yuan Zhiyong Z Song Yongchun Y Liu Ningbo N Wang Kun K Pang Qingsong Q

Annals of translational medicine 20200901 18

<h4>Background</h4>To investigate the relationship between baseline nutrition status and radiation esophagitis in patients with esophageal cancer treated by radiation therapy.<h4>Methods</h4>A retrospective study was performed on 100 patients with esophageal cancer who was treated with definitive chemoradiotherapy, preoperative chemoradiation and definitive radiotherapy at the Tianjin Medical University Cancer Institute and Hospital from October 2018 and October 2019. We documented the clinical ...[more]

PMID: 33240997

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Project description:BackgroundThe machine learning models with dose factors and the deep learning models with dose distribution matrix have been used to building lung toxics models for radiotherapy and achieve promising results. However, few studies have integrated clinical features into deep learning models. This study aimed to explore the role of three-dimension dose distribution and clinical features in predicting radiation pneumonitis (RP) in esophageal cancer patients after radiotherapy and designed a new hybrid deep learning network to predict the incidence of RP.MethodsA total of 105 esophageal cancer patients previously treated with radiotherapy were enrolled in this study. The three-dimension (3D) dose distributions within the lung were extracted from the treatment planning system, converted into 3D matrixes and used as inputs to predict RP with ResNet. In total, 15 clinical factors were normalized and converted into one-dimension (1D) matrixes. A new prediction model (HybridNet) was then built based on a hybrid deep learning network, which combined 3D ResNet18 and 1D convolution layers. Machine learning-based prediction models, which use the traditional dosiomic factors with and without the clinical factors as inputs, were also constructed and their predictive performance compared with that of HybridNet using tenfold cross validation. Accuracy and area under the receiver operator characteristic curve (AUC) were used to evaluate the model effect. DeLong test was used to compare the prediction results of the models.ResultsThe deep learning-based model achieved superior prediction results compared with machine learning-based models. ResNet performed best in the group that only considered dose factors (accuracy, 0.78 ± 0.05; AUC, 0.82 ± 0.25), whereas HybridNet performed best in the group that considered both dose factors and clinical factors (accuracy, 0.85 ± 0.13; AUC, 0.91 ± 0.09). HybridNet had higher accuracy than that of Resnet (p = 0.009).ConclusionBased on prediction results, the proposed HybridNet model could predict RP in esophageal cancer patients after radiotherapy with significantly higher accuracy, suggesting its potential as a useful tool for clinical decision-making. This study demonstrated that the information in dose distribution is worth further exploration, and combining multiple types of features contributes to predict radiotherapy response.

Project description:PurposePatients with esophageal cancer treated with chemoradiation and surgery can develop pulmonary complications. Four-dimensional computed tomography-ventilation (4DCT-ventilation) is a developing imaging modality that uses 4DCT data to calculate lung ventilation. 4DCT-ventilation has been studied in the lung-cancer population but has yet to be extended to patients with esophageal cancer. The purpose of this study was to characterize 4DCT-ventilation-based spatial lung function for patients with esophageal cancer.Methods and materialsThirty-five patients with esophageal cancer who underwent 4DCT scans participated in the study. A 4DCT-ventilation map was calculated using the patient's 4DCT imaging and a density change-based algorithm. To assess each patient's ventilation profile, radiologist interpretations and quantitative metrics were used. A radiologist interpreted the 4DCT-ventilation images for lobar-based defects and gravity-dependent atelectasis. The 4DCT-ventilation maps were reduced to single metrics intended to reflect the degree of ventilation heterogeneity. The quantitative metrics included the coefficient of variation and metrics based on the ventilation in each lung and each lung third (superior-inferior ventilation [Vent-SI] and anteroposterior ventilation). The functional profile of patients with esophageal cancer was characterized and compared (using the Mann-Whitney test) for cohorts based on thoracic comorbidities and radiologist-identified defects.ResultsRadiologist observations revealed that 26% of patients with esophageal cancer had lobar-based defects and 46% had gravity-dependent atelectasis. The baseline values were 0.52 ± 0.20 (mean ± SD), 11.2 ± 12.5, and 72.5 ± 14.6 for the coefficient of variation, the ventilation ratio of right to left lung, and Vent-SI metrics, respectively. The Vent-SI values were significantly different between patients with and without thoracic comorbidities (P = .05), and the anteroposterior ventilation metric was able to delineate patients with and without gravity-dependent atelectasis (P < .01).ConclusionsOur data demonstrate that approximately 30% of patients with esophageal cancer have significant ventilation heterogeneities. The current work uses radiologist observations and quantitative metrics to characterize 4DCT ventilation-based lung function for patients with esophageal cancer and presents data that can be used for future applications of 4DCT-ventilation to reduce thoracic toxicity for patients with esophageal cancer.

Project description:BackgroundMalnutrition and sarcopenia are frequently observed in patients undergoing maintenance hemodialysis (MHD). To elucidate whether malnutrition is associated with sarcopenia in those cases, the relationship of nutritional status with sarcopenia was investigated.MethodsNutritional status was assessed using a nutritional risk index (NRI) developed for patients undergoing MHD. This retrospective cross-sectional study included 315 MHD patients (199 males, 116 females), who were divided into low-risk (score 0-7) and medium-/high-risk (score 8-13) groups. Sarcopenia and severe sarcopenia, along with low muscle mass, low muscle strength, and low physical performance were defined using the Asian Working Group for Sarcopenia 2019 criteria.ResultsThe median NRI score was 5.0, while the prevalence of medium-/high-risk cases among the patients was 31.1%. Additionally, the rates of those with low muscle mass, low muscle strength, and low physical performance were 55.9, 60.6, and 31.4%, respectively, while those of sarcopenia and severe sarcopenia were 44.1 and 20.0%, respectively. Multivariable logistic regression analyses revealed a significant (P < 0.001) association of NRI score with sarcopenia [odds ratio (OR) 1.255, 95% confidence interval (CI) 1.143-1.377] and severe sarcopenia (OR 1.257, 95% CI 1.122-1.407), as well as low muscle mass (OR 1.260, 95% CI 1.157-1.374), low muscle strength (OR 1.310, 95% CI 1.178-1.457), and low physical performance (OR 1.216, 95% CI 1.104-1.339). Furthermore, medium-/high-risk status showed a significant (P < 0.05) association with sarcopenia (OR 2.960, 95% CI 1.623-5.401) and severe sarcopenia (OR 2.241, 95% CI 1.151-4.362), as well as low muscle mass (OR 2.141, 95% CI 1.219-3.760), low muscle strength (OR 7.665, 95% CI 3.438-17.091), and low physical performance (OR 2.570, 95% CI 1.401-4.716).ConclusionsThese results suggest that malnutrition contributes to sarcopenia/severe sarcopenia in MHD patients by reducing muscle mass and strength, and physical performance.

Project description:Purpose: Radiation-induced lung disease (RILD), defined as dyspnea in this study, is a risk for patients receiving high-dose thoracic irradiation. This study is a TRIPOD (Transparent Reporting of A Multivariable Prediction Model for Individual Prognosis or Diagnosis) Type 4 validation of previously-published dyspnea models via secondary analysis of esophageal cancer SCOPE1 trial data. We quantify the predictive performance of these two models for predicting the maximal dyspnea grade ≥ 2 within 6 months after the end of high-dose chemo-radiotherapy for primary esophageal cancer. Materials and methods: We tested the performance of two previously published dyspnea risk models using baseline, treatment and follow-up data on 258 esophageal cancer patients in the UK enrolled into the SCOPE1 multi-center trial. The tested models were developed from lung cancer patients treated at MAASTRO Clinic (The Netherlands) from the period 2002 to 2011. The adverse event of interest was dyspnea ≥ Grade 2 (CTCAE v3) within 6 months after the end of radiotherapy. As some variables were missing randomly and cannot be imputed, 212 patients in the SCOPE1 were used for validation of model 1 and 255 patients were used for validation of model 2. The model parameter Forced Expiratory Volume in 1 s (FEV1), as a predictor to both validated models, was imputed using the WHO performance status. External validation was performed using an automated, decentralized approach, without exchange of individual patient data. Results: Out of 258 patients with esophageal cancer in SCOPE1 trial data, 38 patients (14.7%) developed radiation-induced dyspnea (≥ Grade 2) within 6 months after chemo-radiotherapy. The discrimination performance of the models in esophageal cancer patients treated with high-dose external beam radiotherapy was moderate, area under curve (AUC) of 0.68 (95% CI 0.55-0.76) and 0.70 (95% CI 0.58-0.77), respectively. The curves and AUCs derived by distributed learning were identical to the results from validation on a local host. Conclusion: We have externally validated previously published dyspnea models using an esophageal cancer dataset. FEV1 that is not routinely measured for esophageal cancer was imputed using WHO performance status. Prediction performance was not statistically different from previous training and validation sets. Risk estimates were dominated by WHO score in Model 1 and baseline dyspnea in Model 2. The distributed learning approach gave the same answer as local processing, and could be performed without accessing a validation site's individual patients-level data.

Project description:ObjectiveThere is limited evidence regarding the economic effects of nutrition support in cancer patients. This study aims at investigating the cost-effectiveness profile of systematic oral nutritional supplementation (ONS) in head and neck cancer (HNC) patients undergoing radiotherapy (RT) and receiving nutritional counseling.MethodsA cost-effectiveness analysis based on a RCT was performed to estimate direct medical costs, life years gained (LYG) and Quality-Adjusted Life Years (QALY) for nutritional counseling with or without ONS at 5-month and 6-year follow up time. Value of information analysis was performed to value the expected gain from reducing uncertainty through further data collection.ResultsONS with nutritional counseling produced higher QALY than nutritional counseling alone (0.291 ± 0.087 vs 0.288 ± 0.087), however the difference was not significant (0.0027, P = 0.84). Mean costs were €987.60 vs €996.09, respectively in the treatment and control group (-€8.96, P = 0.98). The Incremental Cost Effectiveness Ratio (ICER) was -€3,277/QALY, with 55.4% probabilities of being cost-effective at a cost-effectiveness threshold of €30,000/QALY. The Expected Incremental Benefit was €95.16 and the Population Expected Value of Perfect Information was €8.6 million, implying that additional research is likely to be worthwhile. At a median 6-year follow up, the treatment group had a significantly better survival rate when adjusting for late effect (P = 0.039).ConclusionOur findings provide the first evidence to inform decisions about funding and reimbursement of ONS in combination with nutritional counseling in HNC patients undergoing RT. ONS may improve quality of cancer care at no additional costs, however further research on the cost-effectiveness of nutritional supplementation is recommended.Trial registrationClinicalTrials.gov: NCT02055833. Registered 5th February 2014 https://clinicaltrials.gov/ct2/show/NCT02055833.

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Baseline nutritional status could be a predictor for radiation esophagitis in esophageal cancer patients undergoing radiotherapy.

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Baseline nutritional status could be a predictor for radiation esophagitis in esophageal cancer patients undergoing radiotherapy.

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