Comput Math Methods Med - Modeling of non-small cell lung cancer volume changes during CT-based image guided radiotherapy: patterns observed and clinical implications.

Tópicos

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Resumo

Background. To characterize the lung tumor volume response during conventional and hypofractionated radiotherapy (RT) based on diagnostic quality CT images prior to each treatment fraction. Methods. Out of 26 consecutive patients who had received CT-on-rails IGRT to the lung from 2004 to 2008, 18 were selected because they had lung lesions that could be easily distinguished. The time course of the tumor volume for each patient was individually analyzed using a computer program. Results. The model fits of group L (conventional fractionation) patients were very close to experimental data, with a median % (average percent difference between data and fit) of 5.1% (range 3.5-10.2%). The fits obtained in group S (hypofractionation) patients were generally good, with a median % of 7.2% (range 3.7-23.9%) for the best fitting model. Four types of tumor responses were observed-Type A: "high" kill and "slow" dying rate; Type B: "high" kill and "fast" dying rate; Type C: "low" kill and "slow" dying rate; and Type D: "low" kill and "fast" dying rate. Conclusions. The models used in this study performed well in fitting the available dataset. The models provided useful insights into the possible underlying mechanisms responsible for the RT tumor volume response.

Resumo Limpo

background character lung tumor volum respons convent hypofraction radiotherapi rt base diagnost qualiti ct imag prior treatment fraction method consecut patient receiv ctonrail igrt lung select lung lesion easili distinguish time cours tumor volum patient individu analyz use comput program result model fit group l convent fraction patient close experiment data median averag percent differ data fit rang fit obtain group s hypofraction patient general good median rang best fit model four type tumor respons observedtyp high kill slow die rate type b high kill fast die rate type c low kill slow die rate type d low kill fast die rate conclus model use studi perform well fit avail dataset model provid use insight possibl under mechan respons rt tumor volum respons

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