Prediction of chronic kidney disease in abdominal cancers radiation therapy using the functional assays of normal tissue complication probability models.

AIM

The purpose of this study is to predict chronic kidney disease (CKD) in the radiotherapy of abdominal cancers by evaluating clinical and functional assays of normal tissue complication probability (NTCP) models.

MATERIALS AND METHODS

Radiation renal damage was analyzed in 50 patients with abdominal cancers 12 months after radiotherapy through a clinical estimated glomerular filtration rate (eGFR). According to the common terminology criteria for the scoring system of adverse events, Grade 2 CKD (eGFR ≤30-59 ml/min/1.73 m) was considered as the radiation therapy endpoint. Modeling and parameter estimation of NTCP models were performed for the Lyman-equivalent uniform dose (EUD), the logit-EUD critical volume (CV), the relative seriality, and the mean dose model.

RESULTS

The confidence interval of the fitted parameters was 95%. The parameter value of D was obtained 22-38 Gy, and the n and s parameters were equivalent to 0.006 -3 and 1, respectively. According to the Akaike's information criterion, the mean dose model predicts radiation-induced CKD more accurately than the other models.

CONCLUSION

Although the renal medulla consists of many nephrons arranged in parallel, each nephron has a seriality architecture as renal functional subunits. Therefore, based on this principle and modeling results in this study, the whole kidney organs may have a serial-parallel combination or a secret architecture.