Purpose. Validation of a Canadian three-tiered prognostic model (survival prediction score, SPS) in Norwegian cancer patients referred for palliative radiotherapy (PRT), and evaluation of age-dependent performance of the model. Patients and Methods. We analyzed all 579 PRT courses administered at a dedicated PRT facility between 20.06.07 and 31.12.2009. SPS was assigned as originally described, That is, by taking into consideration three variables: primary cancer type, site of metastases, and performance status. Results. Patients with poor prognosis (non-breast cancer, metastases other than bone, and Karnofsky performance status (KPS) ≤ 60) had median survival of 13 weeks. Those with intermediate prognosis (two of these parameters) survived for a median of 29 weeks, and patients with good prognosis for a median of 114 weeks, . While this model performed well in patients who were 60？years or older, it was less satisfactory in younger patients (no significant difference between the good and intermediate prognosis groups). Conclusion. SPS should mainly be used to predict survival of elderly cancer patients. However, even in this group accuracy is limited because the good prognosis group contained patients with short survival, while the poor prognosis group contained long-term survivors. Thus, improved models should be developed. 1. Introduction Gradual refinement of palliative oncological treatment approaches has contributed to better, prognosis-adapted cancer care. In part, disease trajectories extend over many years, even in patients without curative treatment option. In contrast, other patients with poorly responding tumors often face rapid disease progression and, as a direct consequence, limited survival. Clinicians are trying to tailor their treatment approaches by estimating patients’ prognosis. While physicians’ clinical experience and previous course of disease might provide hints [1, 2], a large number of more objective assessments tools have been developed [3–8], both for research purposes, patient stratification in clinical trials, and decision making. The choice between different tools might not always be easy. Ideally, prognostic scores are easy to administer, without need for expensive imaging or biomarker assessment, and valid across different institutions and countries [9]. The survival prediction score (SPS) developed by Chow et al. is among the tools that might be widely applicable, because it is based on three readily available parameters: primary cancer type, site of metastases, and performance status [10]. The present study reexamines
References
[1]
P. Glare, K. Virik, M. Jones et al., “A systematic review of physicians' survival predictions in terminally ill cancer patients,” British Medical Journal, vol. 327, no. 7408, pp. 195–198, 2003.
[2]
F. Toscani, C. Brunelli, G. Miccinesi et al., “Predicting survival in terminal cancer patients: clinical observation or quality-of-life evaluation?” Palliative Medicine, vol. 19, no. 3, pp. 220–227, 2005.
[3]
L. Gaspar, C. Scott, M. Rotman et al., “Recursive Partitioning Analysis (RPA) of prognostic factors in three Radiation Therapy Oncology Group (RTOG) brain metastases trials,” International Journal of Radiation Oncology Biology Physics, vol. 37, no. 4, pp. 745–751, 1997.
[4]
M. Pirovano, M. Maltoni, O. Nanni et al., “A new palliative prognostic score: a first step for the staging of terminally ill cancer patients,” Journal of Pain and Symptom Management, vol. 17, no. 4, pp. 231–239, 1999.
[5]
E. Chow, K. Fung, T. Panzarella, A. Bezjak, C. Danjoux, and I. Tannock, “A predictive model for survival in metastatic cancer patients attending an outpatient palliative radiotherapy clinic,” International Journal of Radiation Oncology Biology Physics, vol. 53, no. 5, pp. 1291–1302, 2002.
[6]
S. Gripp, S. Moeller, E. B？lke et al., “Survival prediction in terminally ill cancer patients by clinical estimates, laboratory tests, and self-rated anxiety and depression,” Journal of Clinical Oncology, vol. 25, no. 22, pp. 3313–3320, 2007.
[7]
D. Rades, J. Dunst, and S. E. Schild, “The first score predicting overall survival in patients with metastatic spinal cord compression,” Cancer, vol. 112, no. 1, pp. 157–161, 2008.
[8]
D. Rades, J. Dunst, and S. E. Schild, “A new scoring system to predicting the survival of patients treated with whole-brain radiotherapy for brain metastases,” Strahlentherapie und Onkologie, vol. 184, no. 5, pp. 251–255, 2008.
[9]
C. Nieder and M. P. Mehta, “Prognostic indices for brain metastases—usefulness and challenges,” Radiation Oncology, vol. 4, article 10, 2009.
[10]
E. Chow, M. Abdolell, T. Panzarella et al., “Predictive model for survival in patients with advanced cancer,” Journal of Clinical Oncology, vol. 26, no. 36, pp. 5863–5869, 2008.
[11]
E. R. Laws, I. F. Parney, W. Huang et al., “Survival following surgery and prognostic factors for recently diagnosed malignant glioma: data from the glioma outcomes project,” Journal of Neurosurgery, vol. 99, no. 3, pp. 467–473, 2003.
[12]
P. W. Sperduto, B. Berkey, L. E. Gaspar, M. Mehta, and W. Curran, “A new prognostic index and comparison to three other indices for patients with brain metastases: an analysis of 1,960 patients in the RTOG database,” International Journal of Radiation Oncology Biology Physics, vol. 70, no. 2, pp. 510–514, 2008.
[13]
M. Federico, M. Bellei, L. Marcheselli et al., “Follicular lymphoma international prognostic index 2: a new prognostic index for follicular lymphoma developed by the international follicular lymphoma prognostic factor project,” Journal of Clinical Oncology, vol. 27, no. 27, pp. 4555–4562, 2009.
[14]
C. R？llig, C. Thiede, M. Gramatzki et al., “A novel prognostic model in elderly patients with acute myeloid leukemia: results of 909 patients entered into the prospective AML96 trial,” Blood, vol. 116, no. 6, pp. 971–978, 2010.
[15]
J. N. Winter, S. Li, V. Aurora et al., “Expression of p21 protein predicts clinical outcome in DLBCL patients older than 60 years treated with R-CHOP but not CHOP: a prospective ECOG and Southwest Oncology Group correlative study on E4494,” Clinical Cancer Research, vol. 16, no. 8, pp. 2435–2442, 2010.
[16]
D. Rades, N. D. Seibold, M. P. Gebhard, F. Noack, S. E. Schild, and C. Thorns, “Prognostic factors (including HPV status) for irradiation of locally advanced squamous cell carcinoma of the head and neck (SCCHN),” Strahlentherapie und Onkologie, vol. 187, no. 10, pp. 626–632, 2011.
[17]
E. Chow, M. Abdolell, T. Panzarella et al., “Validation of a predictive model for survival in metastatic cancer patients attending an outpatient palliative radiotherapy clinic,” International Journal of Radiation Oncology Biology Physics, vol. 73, no. 1, pp. 280–287, 2009.
[18]
S. Gripp, S. Mjartan, E. Boelke, and R. Willers, “Palliative radiotherapy tailored to life expectancy in end-stage cancer patients: reality or myth?” Cancer, vol. 116, no. 13, pp. 3251–3256, 2010.
[19]
B. A. Guadagnolo, K. P. Liao, L. Elting, et al., “Use of radiation therapy in the last 30 days of life among a large population-based cohort of elderly patients in the United States,” Journal of Clinical Oncology, vol. 31, pp. 80–87, 2013.
[20]
D. Rades, T. Veninga, A. Bajrovic, et al., “A validated scoring system to identify long-term survivors after radiotherapy for metastatic spinal cord compression,” Strahlentherapie und Onkologie, vol. 189, no. 6, pp. 462–466, 2013.
[21]
D. Rades, M. Hueppe, and S. E. Schild, “A score to identify patients with metastatic spinal cord compression who may be candidates for best supportive care,” Cancer, vol. 119, no. 4, pp. 897–903, 2013.
[22]
P. W. Sperduto, S. T. Chao, P. K. Sneed, et al., “Diagnosisspecific prognostic factors, indexes, and treatment outcomes for patients with newly diagnosed brain metastases: a multiinstitutional analysis of 4,259 patients,” International Journal of Radiation Oncology, Biology Physics, vol. 77, no. 3, pp. 655–661, 2010.
[23]
C. Nieder, J. Norum, A. Dalhaug, et al., “Best supportive care in patients with brain metastases and adverse prognostic factors: development of improved decision aids,” Support Care Cancer, vol. 21, no. 10, pp. 2671–2678, 2013.
[24]
A. T. Stopeck, U. Brown-Glaberman, H. Y. Wong, et al., “The role of targeted therapy and biomarkers in breast cancer treatment,” Clinical & Experimental Metastasis, vol. 29, no. 7, pp. 807–819, 2012.
