The dosimetric parameters from the DVH cannot predict the amount of tumor kill and normal tissue complications directly but it can assess the conformity and homogeneity of the physical dose distributions.
For example, the D-V parameter V20 [Percentage of lung volume receiving 20Gy] is used to gauge the incidence of grade >/=2 or grade >/=3 radiation pneumonitis with the plan. But the complication can be correlated to more than one point in the DVH [eg. V5, V40, D50] and it is treatment technique dependent. The aim of this study is to quantify the uncertainty of physical dose metrics to predict the clinical outcomes of the radiotherapy treatments. The radiobiological estimates such as TCP and NTCP were made for a cohort of 50 patients [15-Brain; 20-H and N; 15-Pelvis] using the D-V parameters. A statistical analysis based on Spearman ranking coefficient correlation was performed to determine the correlation of the physical plan quality indicators with that of radiobiological estimates. The correlation between the Conformity Index and the Tumor Control probability was found to be good and the dosimetric parameters for optic nerves, optic chiasm, brain stem, normal brain and parotids correlated well with the Normal Tissue Complication Probability estimates compared to other normal structures. A follow up study [median duration: 28 Months] was also performed. There was no grade 3 or grade 4 normal tissue complications observed. Local tumor control was found to be higher in brain [90%] and pelvic cases [95%] whereas a decline of 75% was noted with Head and Neck cases. The EUD concept of radiobiological model used in the software determines the TCP and NTCP values which can predict precise outcomes with the use of dose volume data in the voxel level. The uncertainty of using physical dose metrics for plan evaluation is quantified with the statistical analysis. It is also helpful in ranking rival treatment plans
A. Surega ,J. Punitha ,S. Sajitha ,B. S. Ramesh ,A. Pichandi ,P. Sasikala ,
A statistical quantification of radiobiological metrics in intensity modulated radiation therapy evaluation,
Gulf J. Oncol. 2015;
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