26138912
OBJECTIVE	A prognostic model for 5-year overall survival ( OS ) , consisting of recursive partitioning analysis ( RPA ) and a nomogram , was developed for patients with early-stage non-small cell lung cancer ( ES-NSCLC ) treated with stereotactic ablative radiation therapy ( SABR ) .
METHODS	A primary dataset of 703 ES-NSCLC SABR patients was randomly divided into a training ( 67 % ) and an internal validation ( 33 % ) dataset .
METHODS	In the former group , 21 unique parameters consisting of patient , treatment , and tumor factors were entered into an RPA model to predict OS .
METHODS	Univariate and multivariate models were constructed for RPA-selected factors to evaluate their relationship with OS .
METHODS	A nomogram for OS was constructed based on factors significant in multivariate modeling and validated with calibration plots .
METHODS	Both the RPA and the nomogram were externally validated in independent surgical ( n = 193 ) and SABR ( n = 543 ) datasets .
RESULTS	RPA identified 2 distinct risk classes based on tumor diameter , age , World Health Organization performance status ( PS ) and Charlson comorbidity index .
RESULTS	This RPA had moderate discrimination in SABR datasets ( c-index range : 0.52-0 .60 ) but was of limited value in the surgical validation cohort .
RESULTS	The nomogram predicting OS included smoking history in addition to RPA-identified factors .
RESULTS	In contrast to RPA , validation of the nomogram performed well in internal validation ( r ( 2 ) = 0.97 ) and external SABR ( r ( 2 ) = 0.79 ) and surgical cohorts ( r ( 2 ) = 0.91 ) .
CONCLUSIONS	The Amsterdam prognostic model is the first externally validated prognostication tool for OS in ES-NSCLC treated with SABR available to individualize patient decision making .
CONCLUSIONS	The nomogram retained strong performance across surgical and SABR external validation datasets .
CONCLUSIONS	RPA performance was poor in surgical patients , suggesting that 2 different distinct patient populations are being treated with these 2 effective modalities .

