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Predicting Overall Survival After Stereotactic Ablative Radiation Therapy in Early-Stage Lung Cancer: Development and External Validation of the Amsterdam Prognostic Model
- Source :
- Louie, A V, Haasbeek, C J A, Mokhles, S, Rodrigues, G B, Stephans, K L, Lagerwaard, F J, Palma, D A, Videtic, G M M, Warner, A, Takkenberg, J J M, Reddy, C A, Maat, A P W M, Woody, N M, Slotman, B J & Senan, S 2015, ' Predicting Overall Survival After Stereotactic Ablative Radiation Therapy in Early-Stage Lung Cancer: Development and External Validation of the Amsterdam Prognostic Model ', International journal of radiation oncology, biology, physics, vol. 93, no. 1, pp. 82-90 . https://doi.org/10.1016/j.ijrobp.2015.05.003, International Journal of Radiation Oncology Biology Physics, 93(1), 82-90. Elsevier Inc., International journal of radiation oncology, biology, physics, 93(1), 82-90. Elsevier Inc.
- Publication Year :
- 2015
-
Abstract
- 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 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. 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. (C) 2015 Elsevier Inc. All rights reserved.
- Subjects :
- Oncology
Cancer Research
medicine.medical_specialty
Multivariate analysis
Lung Neoplasms
Time Factors
medicine.medical_treatment
Recursive partitioning
SABR volatility model
Radiosurgery
SDG 3 - Good Health and Well-being
Internal medicine
Carcinoma, Non-Small-Cell Lung
medicine
Humans
Radiology, Nuclear Medicine and imaging
Stage (cooking)
Lung cancer
Aged
Radiation
Performance status
business.industry
Radiotherapy Dosage
Nomogram
medicine.disease
Prognosis
Radiation therapy
Nomograms
Logistic Models
Multivariate Analysis
business
Nuclear medicine
Subjects
Details
- ISSN :
- 03603016
- Volume :
- 93
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- International journal of radiation oncology, biology, physics
- Accession number :
- edsair.doi.dedup.....5f6b484ad2e7d79f09ed798bfd4046a1
- Full Text :
- https://doi.org/10.1016/j.ijrobp.2015.05.003