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Title: Predicting Overall Survival After Stereotactic Ablative Radiation Therapy in Early-Stage Lung Cancer: Development and External Validation of the Amsterdam Prognostic Model

Purpose: 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 and Materials: A primary dataset of 703 ES-NSCLC SABR patients was randomly divided into a training (67%) and an internal validation (33%) dataset. In the former group, 21 unique parameters consisting of patient, treatment, and tumor factors were entered into an RPA model to predict OS. Univariate and multivariate models were constructed for RPA-selected factors to evaluate their relationship with OS. A nomogram for OS was constructed based on factors significant in multivariate modeling and validated with calibration plots. 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. 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. The nomogram predicting OS included smoking history in addition to RPA-identified factors. In contrast to RPA, validation of the nomogram performed well in internalmore » validation (r{sup 2}=0.97) and external SABR (r{sup 2}=0.79) and surgical cohorts (r{sup 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. The nomogram retained strong performance across surgical and SABR external validation datasets. RPA performance was poor in surgical patients, suggesting that 2 different distinct patient populations are being treated with these 2 effective modalities.« less
Authors:
 [1] ;  [2] ;  [3] ;  [1] ;  [4] ;  [5] ;  [6] ;  [1] ;  [5] ;  [6] ;  [5] ;  [4] ;  [6] ;  [4] ;  [6] ; ;  [1]
  1. Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands)
  2. (Canada)
  3. (United States)
  4. Department of Cardio-Thoracic Surgery, Erasmus University Medical Center, Rotterdam (Netherlands)
  5. Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada)
  6. Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States)
Publication Date:
OSTI Identifier:
22458751
Resource Type:
Journal Article
Resource Relation:
Journal Name: International Journal of Radiation Oncology, Biology and Physics; Journal Volume: 93; Journal Issue: 1; Other Information: Copyright (c) 2015 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; CALIBRATION; DATASETS; HAZARDS; LUNGS; MULTIVARIATE ANALYSIS; NEOPLASMS; NOMOGRAMS; PATIENTS; POPULATIONS; RADIOTHERAPY; RANDOM PHASE APPROXIMATION; SIMULATION; SMOKES; SURGERY; VALIDATION