A novel nomogram and risk classification system predicting radiation pneumonitis in patients with esophageal cancer receiving radiotherapy

Date

29 Sep 2019

Session

Poster Display session 2

Presenters

Lu Wang

Citation

Annals of Oncology (2019) 30 (suppl_5): v253-v324. 10.1093/annonc/mdz247

Authors

L. Wang1, X. Meng2, J. Yu2

Author affiliations

  • 1 Radiation Oncology, Shandong cancer hosptial, 440 - Jinan/CN
  • 2 Radiation Oncology, Shandong cancer hosptial, 250117 - Jinan/CN
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Resources

Background

We initially ascertained the value of inflammatory indexes in predicting severe acute radiation pneumonitis (SARP). Furthermore, we firstly built a novel nomogram and risk classification system integrating clinicopathological, dosimetric and biological parameters to individually and precisely identify SARP in patients with esophageal cancer (EC) who received radiotherapy (RT).

Methods

All data were collected from 312 EC patients. Logistic regression was used to choose predictors of SARP and then build nomogram. The validation of nomogram was performed by area under the ROC curve (AUC), calibration curves and decision curve analyses (DCA). A risk classification system was generated by recursive partitioning analysis (RPA).

Results

The Subjective Global Assessment (SGA) score, pulmonary fibrosis score (PFS), planning target volume/total lungs volume (PTV/LV), mean lung dose (MLD) and systemic immuneinflammation index (SII) were independent predictors of SARP and finally incorporated into the nomogram. The AUC of nomogram for SARP prediction was 0.852, which was much higher than any other factor (range, 0.604-0.712). Calibration curves indicated favorable consistency between the nomogram prediction and the actual outcomes. DCA exhibited satisfactory clinical utility. A risk classification system was built to perfectly divide patients into three risk groups which were low-risk group (7.1%, score 0–158), intermediate-risk group (38%, score 159–280), and high-risk group (71.4%, score>280).

Conclusions

SGA score, PFS, PTV/LV, MLD and SII were potential valuable markers in predicting SARP. The constructed nomogram and corresponding risk classification system with superior prediction ability for SARP could assist in patients counseling and guide treatment decision making.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

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