RESUMEN
PURPOSE: Treatment planning factors are known to affect the risk of severe acute esophagitis during thoracic radiation therapy. We tested a previously published model to predict the risk of severe acute esophagitis on an independent data set. METHODS AND MATERIALS: The data set consists of data from patients who had recoverable treatment plans and received definitive radiation therapy for non-small cell carcinoma of the lung at a single institution between November 2004 and January 2010. Complete esophagus dose-volume and available clinical information was extracted using our in-house software. The previously published model was a logistic function with a combination of mean esophageal dose and use of concurrent chemotherapy. In addition to testing the previous model, we used a novel, machine learning-based method to build a maximally predictive model. RESULTS: Ninety-four patients (81.7%) developed Common Terminology Criteria for Adverse Events, Version 4, Grade 2 or more severe esophagitis (Grade 2: n = 79 and Grade 3: n = 15). Univariate analysis revealed that the most statistically significant dose-volume parameters included percentage of esophagus volume receiving ≥40 to 60 Gy, minimum dose to the highest 20% of esophagus volume (D20) to D35, and mean dose. Other significant predictors included concurrent chemotherapy and patient age. The previously published model predicted risk effectively with a Spearman's rank correlation coefficient (rs) of 0.43 (P < .001) with good calibration (Hosmer-Lemeshow goodness of fit: P = .537). A new model that was built from the current data set found the same variables, yielding an rs of 0.43 (P < .001) with a logistic function of 0.0853 × mean esophageal dose [Gy] + 1.49 × concurrent chemotherapy [1/0] - 1.75 and Hosmer-Lemeshow P = .659. A novel preconditioned least absolute shrinkage and selection operator method yielded an average rs of 0.38 on 100 bootstrapped data sets. CONCLUSIONS: The previously published model was validated on an independent data set and determined to be nearly as predictive as the best possible two-parameter logistic model even though it overpredicted risk systematically. A novel, machine learning-based model using a bootstrapping approach showed reasonable predictive power.
RESUMEN
PURPOSE: To construct a maximally predictive model of the risk of severe acute esophagitis (AE) for patients who receive definitive radiation therapy (RT) for non-small-cell lung cancer. METHODS AND MATERIALS: The dataset includes Washington University and RTOG 93-11 clinical trial data (events/patients: 120/374, WUSTL = 101/237, RTOG9311 = 19/137). Statistical model building was performed based on dosimetric and clinical parameters (patient age, sex, weight loss, pretreatment chemotherapy, concurrent chemotherapy, fraction size). A wide range of dose-volume parameters were extracted from dearchived treatment plans, including Dx, Vx, MOHx (mean of hottest x% volume), MOCx (mean of coldest x% volume), and gEUD (generalized equivalent uniform dose) values. RESULTS: The most significant single parameters for predicting acute esophagitis (RTOG Grade 2 or greater) were MOH85, mean esophagus dose (MED), and V30. A superior-inferior weighted dose-center position was derived but not found to be significant. Fraction size was found to be significant on univariate logistic analysis (Spearman R = 0.421, p < 0.00001) but not multivariate logistic modeling. Cross-validation model building was used to determine that an optimal model size needed only two parameters (MOH85 and concurrent chemotherapy, robustly selected on bootstrap model-rebuilding). Mean esophagus dose (MED) is preferred instead of MOH85, as it gives nearly the same statistical performance and is easier to compute. AE risk is given as a logistic function of (0.0688 MED+1.50 ConChemo-3.13), where MED is in Gy and ConChemo is either 1 (yes) if concurrent chemotherapy was given, or 0 (no). This model correlates to the observed risk of AE with a Spearman coefficient of 0.629 (p < 0.000001). CONCLUSIONS: Multivariate statistical model building with cross-validation suggests that a two-variable logistic model based on mean dose and the use of concurrent chemotherapy robustly predicts acute esophagitis risk in combined-data WUSTL and RTOG 93-11 trial datasets.
Asunto(s)
Carcinoma de Pulmón de Células no Pequeñas/radioterapia , Esofagitis/etiología , Esófago/efectos de la radiación , Neoplasias Pulmonares/radioterapia , Modelos Biológicos , Traumatismos por Radiación/complicaciones , Adulto , Factores de Edad , Anciano , Anciano de 80 o más Años , Análisis de Varianza , Carcinoma de Pulmón de Células no Pequeñas/tratamiento farmacológico , Estudios de Cohortes , Femenino , Humanos , Neoplasias Pulmonares/tratamiento farmacológico , Masculino , Persona de Mediana Edad , Dosificación Radioterapéutica , Riesgo , Factores Sexuales , Carga Tumoral , Pérdida de PesoRESUMEN
PURPOSE: to investigate the potential role of incidental heart irradiation on the risk of radiation pneumonitis (RP) for patients receiving definitive radiation therapy for non-small-cell lung cancer (NSCLC). MATERIAL AND METHODS: two hundred and nine patient datasets were available for this study. Heart and lung dose-volume parameters were extracted for modeling, based on Monte Carlo-based heterogeneity corrected dose distributions. Clinical variables tested included age, gender, chemotherapy, pre-treatment weight-loss, performance status, and smoking history. The risk of RP was modeled using logistic regression. RESULTS: the most significant univariate variables were heart related, such as heart heart V65 (percent volume receiving at least 65 Gy) (Spearman Rs = 0.245, p < 0.001). The best-performing logistic regression model included heart D10 (minimum dose to the hottest 10% of the heart), lung D35, and maximum lung dose (Spearman Rs = 0.268, p < 0.0001). When classified by predicted risk, the RP incidence ratio between the most and least risky 1/3 of treatments was 4.8. The improvement in risk modeling using lung and heart variables was better than using lung variables alone. CONCLUSIONS: these results suggest a previously unsuspected role of heart irradiation in many cases of RP.