Comparison of predicted and clinical response to radiotherapy: a radiobiology modelling study.
West, Catharine M L
AffiliationDepartment of Oncology, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden. firstname.lastname@example.org
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AbstractINTRODUCTION: A model to predict clinical outcome after radiation therapy would be a valuable aid in the effort of developing more tailored treatment regimes for different patients. In this work we evaluate the clinical utility of a model that incorporates the following individually measured radiobiology parameters: intrinsic radiosensitivity, proliferation and number of clonogenic cells. The hypothesis underlying the study was that the incorporation of individually measured tumour parameters in a model would increase its reliability in predicting treatment outcome compared with the use of average population derived data. MATERIAL AND METHODS: Forty-six patients with head and neck tumours were analyzed, the majority of whom received both external beam radiotherapy and brachytherapy. Eighteen patients received external beam treatment alone and statistical analyses were carried out on this subgroup. RESULTS: Four of the 18 patients had a >95% calculated probability of cure and none developed a local recurrence resulting in a negative predictive value of 100% (compared with 67% for population-derived data). The sensitivity of the model in predicting local recurrence was 75% (compared with 38% for population-derived data). Using a model that incorporated individually measured radiobiology data, there was a statistically significant difference in local control levels for patients with >95% and <5% predicted probability of local control (chi(2), p = 0.04). DISCUSSION: This study suggests, therefore, that incorporation of measured biological data within a radiobiological model improves its ability to predict radiation therapy outcome compared with the use of population-derived data.
CitationComparison of predicted and clinical response to radiotherapy: a radiobiology modelling study. 2009, 48 (4):584-90 Acta Oncol
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