Predictive Factors for Acute Esophageal Toxicity in Thoracic Radiotherapy

Takeda, Ken; Nemoto, Kenji; Saito, Haruo; Ogawa, Yoshihiro; Takai, Yoshihiro; Yamada, Satoshi

doi:10.1620/tjem.208.299

Cited by 20 publications

(12 citation statements)

References 15 publications

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“…The development of skin reactions increased with dose and was first documented at 35.5 Gy of radiation (Twardella et al, 2003). Similar findings were reported in a study that used a nonhomogenous sample of 61 patients with intrathoracic malignancies and included 44 male and 17 female patients, with the development of toxicities associated with radiotherapy doses of greater than 35 Gy (Takeda et al, 2006). Therefore, findings in the literature strongly indicate that increasing doses of radiation result in the development of toxicities; however, these occur at varying doses, depending on the type of cancer and the treatment site.…”

Section: Wolff Et Al 2011 Germanysupporting

confidence: 80%

Predicting Radiotherapy-Related Clinical Toxicities in Cancer: A Literature Review

O’Gorman¹,

Sąsiadek²,

Denieffe³

et al. 2014

Clinical Journal of Oncology Nursing

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Assessment of patients receiving radiotherapy for cancer is essential, with the ability to identify those who may be more likely to experience radiotherapy-related side effects noted as an important issue for nurses. Body mass, age, and radiation dose may be predictive factors for the development of such side effects. This review considers these factors and how nurses can use this evidence to inform their care, with results indicating that the dose of radiation, the site treated, and body mass index are predictive of toxicities that may develop. Increased awareness of these predictive factors will aid nurses in identifying patients at greater risk of developing radiation-related side effects. This will assist in guiding nursing interventions, as well as enabling the individualization of patient education, by placing greater emphasis on preventive measures for patients who are more vulnerable to the development of radiation-related toxicities.

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Section: Wolff Et Al 2011 Germanysupporting

confidence: 80%

Predicting Radiotherapy-Related Clinical Toxicities in Cancer: A Literature Review

O’Gorman¹,

Sąsiadek²,

Denieffe³

et al. 2014

Clinical Journal of Oncology Nursing

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“…Two recent reviews9, 10 summarized the dosimetric predictors. Rose et al 9 systematically reviewed 18 published studies of patients with non–small cell lung cancer who had radiation-induced esophagitis 3, 4, 5, 6, 7, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23. Eleven studies specifically assessed AE, and the other studies assessed acute and chronic radiation-induced esophagitis together.…”

Section: Introductionmentioning

confidence: 99%

Independent test of a model to predict severe acute esophagitis

Huang

Robinson

Molotievschi

et al. 2017

Advances in Radiation Oncology

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PurposeTreatment 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 materialsThe 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.ResultsNinety-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.ConclusionsThe 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.

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“…Clinical and dosimetric factors that may be related to the incidence and severity of ARIE include: age [4], tumor [5] and nodal stage [1,6], concurrent chemoirradiation [5], mean esophageal dose and maximal dose point [7], and esophageal volume receiving >35 Gy (V35), V45, V50, V60 [1,4,8], percent length of esophagus receiving >40 Gy, and >66 Gy full circumference [1]. Most of these factors are closely associated with the total radiation dose received by a certain volume of esophagus.…”

Section: Introductionmentioning

confidence: 99%