External validation of an NTCP model for acute esophageal toxicity in locally advanced NSCLC patients treated with intensity-modulated (chemo-)radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology(2018)

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摘要
BACKGROUND AND PURPOSE:We externally validated a previously established multivariable normal-tissue complication probability (NTCP) model for Grade ≥2 acute esophageal toxicity (AET) after intensity-modulated (chemo-)radiotherapy or volumetric-modulated arc therapy for locally advanced non-small cell lung cancer. MATERIALS AND METHODS:A total of 603 patients from five cohorts (A-E) within four different Dutch institutes were included. Using the NTCP model, containing predictors concurrent chemoradiotherapy, mean esophageal dose, gender and clinical tumor stage, the risk of Grade ≥2 AET was estimated per patient and model discrimination and (re)calibration performance were evaluated. RESULTS:Four validation cohorts (A, B, D, E) experienced higher incidence of Grade ≥2 AET compared to the training cohort (49.3-70.2% vs 35.6%; borderline significant for one cohort, highly significant for three cohorts). Cohort C experienced lower Grade ≥2 AET incidence (21.7%, p < 0.001). For three cohorts (A-C), discriminative performance was similar to the training cohort (area under the curve (AUC) 0.81-0.89 vs 0.84). In the two remaining cohorts (D-E) the model showed poor discriminative power (AUC 0.64 and 0.63). Reasonable calibration performance was observed in two cohorts (A-B), and recalibration further improved performance in all three cohorts with good discrimination (A-C). Recalibration for the two poorly discriminating cohorts (D-E) did not improve performance. CONCLUSIONS:The NTCP model for AET prediction was successfully validated in three out of five patient cohorts (AUC ≥0.80). The model did not perform well in two cohorts, which included patients receiving substantially different treatment. Before applying the model in clinical practice, validation of discrimination and (re)calibration performance in a local cohort is recommended.
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