Comparison of predicted and clinical response to radiotherapy: a radiobiology modelling study.
BACKGROUND
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.


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).


CONCLUSIONS
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.
Hedman
Mattias
M
Department of Oncology, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden. mattias.hedman@ki.se
Bjork-Eriksson
Thomas
T
Mercke
Claes
C
West
Catharine
C
Hesselius
Patrick
P
Brodin
Ola
O
D001918:Brachytherapy; D004307:Dose-Response Relationship, Radiation; D006258:Head and Neck Neoplasms / Q000517:prevention & control / Q000532:radiotherapy*; D006801:Humans; D008432:Mathematical Computing; D015233:Models, Statistical; D009364:Neoplasm Recurrence, Local / Q000517:prevention & control* / Q000523:psychology; D014411:Neoplastic Stem Cells / Q000528:radiation effects*; D011237:Predictive Value of Tests; D011832:Radiation Injuries / Q000209:etiology / Q000517:prevention & control; D011835:Radiation Protection; D011836:Radiation Tolerance; D011853:Radiobiology; D011878:Radiotherapy / Q000379:methods; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted; D012062:Relative Biological Effectiveness; D012680:Sensitivity and Specificity