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).
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).
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).
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.
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All authors have declared no conflicts of interest.