Dataset Information

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.

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Project description:This study aimed to compare the predictive performance of different modeling methods in developing normal tissue complication probability (NTCP) models for predicting radiation-induced esophagitis (RE) in non-small cell lung cancer (NSCLC) patients receiving proton radiotherapy. The dataset was composed of 328 NSCLC patients receiving passive-scattering proton therapy and 41.6% of the patients experienced ≥ grade 2 RE. Five modeling methods were used to build NTCP models: standard Lyman-Kutcher-Burman (sLKB), generalized LKB (gLKB), multivariable logistic regression using two variable selection procedures-stepwise forward selection (Stepwise-MLR), and least absolute shrinkage and selection operator (LASSO-MLR), and support vector machines (SVM). Predictive performance was internally validated by a bootstrap approach for each modeling method. The overall performance, discriminative ability, and calibration were assessed using the Negelkerke R2, area under the receiver operator curve (AUC), and Hosmer-Lemeshow test, respectively. The LASSO-MLR model showed the best discriminative ability with an AUC value of 0.799 (95% confidence interval (CI): 0.763-0.854), and the best overall performance with a Negelkerke R2 value of 0.332 (95% CI: 0.266-0.486). Both of the optimism-corrected Negelkerke R2 values of the SVM and sLKB models were 0.301. The optimism-corrected AUC of the gLKB model (0.796) was higher than that of the SVM model (0.784). The sLKB model had the smallest optimism in the model variation and discriminative ability. In the context of classification and probability estimation for predicting the NTCP for radiation-induced esophagitis, the MLR model developed with LASSO provided the best predictive results. The simplest LKB modeling had similar or even better predictive performance than the most complex SVM modeling, and it was least likely to overfit the training data. The advanced machine learning approach might have limited applicability in clinical settings with a relatively small amount of data.

Dataset Information

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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