Early prediction of acute esophagitis for adaptive radiation therapy Journal Article


Authors: Alam, S. R.; Zhang, P.; Zhang, S. Y.; Chen, I.; Rimner, A.; Tyagi, N.; Hu, Y. C.; Lu, W.; Yorke, E. D.; Deasy, J. O.; Thor, M.
Article Title: Early prediction of acute esophagitis for adaptive radiation therapy
Abstract: Purpose: Acute esophagitis (AE) is a common dose-limiting toxicity in radiation therapy of locally advanced non-small cell lung cancer (LA-NSCLC). We developed an early AE prediction model from weekly accumulated esophagus dose and its associated local volumetric change. Methods and Materials: Fifty-one patients with LA-NSCLC underwent treatment with intensity modulated radiation therapy to 60 Gy in 2-Gy fractions with concurrent chemotherapy and weekly cone beam computed tomography (CBCT). Twenty-eight patients (55%) developed grade ≥2 AE (≥AE2) at a median of 4 weeks after the start of radiation therapy. For early ≥AE2 prediction, the esophagus on CBCT of the first 2 weeks was deformably registered to the planning computed tomography images, and weekly esophagus dose was accumulated. Week 1–to–week 2 (w1→w2) esophagus volume changes including maximum esophagus expansion (MEex%) and volumes with ≥x% local expansions (VEx%; x = 5, 10, 15) were calculated from the Jacobian map of deformation vector field gradients. Logistic regression model with 5-fold cross-validation was built using combinations of the accumulated mean esophagus doses (MED) and the esophagus change parameters with the lowest P value in univariate analysis. The model was validated on an additional 18 and 11 patients with weekly CBCT and magnetic resonance imaging (MRI), respectively, and compared with models using only planned mean dose (MEDPlan). Performance was assessed using area under the curve (AUC) and Hosmer-Lemeshow test (PHL). Results: Univariately, w1→w2 VE10% (P =.004), VE5% (P =.01) and MEex% (P =.02) significantly predicted ≥AE2. A model combining MEDW2 and w1→w2 VE10% had the best performance (AUC = 0.80; PHL = 0.43), whereas the MEDPlan model had a lower accuracy (AUC = 0.67; PHL = 0.26). The combined model also showed high accuracy in the CBCT (AUC = 0.78) and MRI validations (AUC = 0.75). Conclusions: A CBCT-based, cross-validated, and internally validated model on MRI with a combination of accumulated esophagus dose and local volume change from the first 2 weeks of chemotherapy significantly improved AE prediction compared with conventional models using only the planned dose. This model could inform plan adaptation early to lower the risk of esophagitis. © 2021
Keywords: intensity modulated radiation therapy; chemotherapy; magnetic resonance imaging; radiotherapy; computerized tomography; computed tomography images; forecasting; cone-beam computed tomography; logistic regression; dose limiting toxicity; concurrent chemotherapy; adaptive radiation therapies; logistic regression modeling; predictive analytics; locally advanced non-small-cell lung cancers
Journal Title: International Journal of Radiation Oncology, Biology, Physics
Volume: 110
Issue: 3
ISSN: 0360-3016
Publisher: Elsevier Inc.  
Date Published: 2021-07-01
Start Page: 883
End Page: 892
Language: English
DOI: 10.1016/j.ijrobp.2021.01.007
PUBMED: 33453309
PROVIDER: scopus
PMCID: PMC8180486
DOI/URL:
Notes: Article -- Export Date: 1 July 2021 -- Source: Scopus
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MSK Authors
  1. Andreas Rimner
    392 Rimner
  2. Pengpeng Zhang
    125 Zhang
  3. Ellen D Yorke
    390 Yorke
  4. Joseph Owen Deasy
    410 Deasy
  5. Yu-Chi Hu
    89 Hu
  6. Neelam Tyagi
    106 Tyagi
  7. Maria Elisabeth Thor
    104 Thor
  8. Wei   Lu
    48 Lu
  9. Ishita Chen
    10 Chen