889 resultados para CELL LUNG-CANCER
Resumo:
Purpose: Respiratory motion causes substantial uncertainty in radiotherapy treatment planning. Four-dimensional computed tomography (4D-CT) is a useful tool to image tumor motion during normal respiration. Treatment margins can be reduced by targeting the motion path of the tumor. The expense and complexity of 4D-CT, however, may be cost-prohibitive at some facilities. We developed an image processing technique to produce images from cine CT that contain significant motion information without 4D-CT. The purpose of this work was to compare cine CT and 4D-CT for the purposes of target delineation and dose calculation, and to explore the role of PET in target delineation of lung cancer. Methods: To determine whether cine CT could substitute 4D-CT for small mobile lung tumors, we compared target volumes delineated by a physician on cine CT and 4D-CT for 27 tumors with intrafractional motion greater than 1 cm. We assessed dose calculation by comparing dose distributions calculated on respiratory-averaged cine CT and respiratory-averaged 4D-CT using the gamma index. A threshold-based PET segmentation model of size, motion, and source-to-background was developed from phantom scans and validated with 24 lung tumors. Finally, feasibility of integrating cine CT and PET for contouring was assessed on a small group of larger tumors. Results: Cine CT to 4D-CT target volume ratios were (1.05±0.14) and (0.97±0.13) for high-contrast and low-contrast tumors respectively which was within intraobserver variation. Dose distributions on cine CT produced good agreement (< 2%/1 mm) with 4D-CT for 71 of 73 patients. The segmentation model fit the phantom data with R2 = 0.96 and produced PET target volumes that matched CT better than 6 published methods (-5.15%). Application of the model to more complex tumors produced mixed results and further research is necessary to adequately integrate PET and cine CT for delineation. Conclusions: Cine CT can be used for target delineation of small mobile lesions with minimal differences to 4D-CT. PET, utilizing the segmentation model, can provide additional contrast. Additional research is required to assess the efficacy of complex tumor delineation with cine CT and PET. Respiratory-averaged cine CT can substitute respiratory-averaged 4D-CT for dose calculation with negligible differences.
Resumo:
The Radiological Physics Center (RPC) provides heterogeneous phantoms that are used to evaluate radiation treatment procedures as part of a comprehensive quality assurance program for institutions participating in clinical trials. It was hypothesized that the existing RPC heterogeneous thorax phantom can be modified to assess lung tumor proton beam therapy procedures involving patient simulation, treatment planning, and treatment delivery, and could confirm agreement between the measured dose and calculated dose within 5%/3mm with a reproducibility of 5%. The Hounsfield Units (HU) for lung equivalent materials (balsa wood and cork) was measured using a CT scanner. The relative linear stopping power (RLSP) of these materials was measured. The linear energy transfer (LET) of Gafchromic EBT2 film was analyzed utilizing parallel and perpendicular orientations in a water tank and compared to ion chamber readings. Both parallel and perpendicular orientations displayed a quenching effect underperforming the ion chamber, with the parallel orientation showing an average 31 % difference and the perpendicular showing an average of 15% difference. Two treatment plans were created that delivered the prescribed dose to the target volume, while achieving low entrance doses. Both treatment plans were designed using smeared compensators and expanded apertures, as would be utilized for a patient in the clinic. Plan 1a contained two beams that were set to orthogonal angles and a zero degree couch kick. Plan 1b utilized two beams set to 10 and 80 degrees with a 15 degree couch kick. EBT2 film and TLD were inserted and the phantom was irradiated 3 times for each plan. Both plans passed the criteria for the TLD measurements where the TLD values were within 7% of the dose calculated by Eclipse. Utilizing the 5%/3mm criteria, the 3 trial average of overall pass rate was 71% for Plan 1a. The 3 trial average for the overall pass rate was 76% for Plan 1b. The trials were then analyzed using RPC conventional lung treatment guidelines set forth by the RTOG: 5%/5mm, and an overall pass rate of 85%. Utilizing these criteria, only Plan 1b passed for all 3 trials, with an average overall pass rate of 89%.
Resumo:
A patient diagnosed with a glioma, generally, has an average of 14 months year to live after implementation of conventional therapies such as surgery, chemotherapy, and radiation. Glioblastomas are highly lethal because of their aggressive nature and resistance to conventional therapies and apoptosis. Thus other avenues of cell death urgently need to be explored. Autophagy, which is also known as programmed cell death type II, has recently been identified as an alternative mechanism to kill apoptosis- resistant cancer cells. Traditionally, researchers have studied how cells undergo autophagy during viral infection as an immune response mechanism, but recently researchers have discovered how viruses have evolved to manipulate autophagy for their benefit. Extensive studies of viral-induced autophagy provide a rationale to investigate other viruses, such as the adenovirus, which may be developed as part of a therapy against cancers resistant to apoptosis. Despite the present and relatively poor understanding of the mechanisms behind adenoviral-induced autophagy, adenovirus is a promising candidate, because of its ability to efficiently eradicate tumors. A better understanding of how the adenovirus induces autophagy will allow for the development of viruses with increased oncolytic potency. We hypothesized that adenovirus induces autophagy in order to aid in lysis. We found that replication, not infection, was required for adenovirus-mediated autophagy. Loss of function analysis of early genes revealed that, of the early genes tested, no single gene was sufficient to induce autophagy alone. Examination of cellular pathways for their role in autophagy during adenovirus infection revealed a function for the eIF2α pathway and more specifically the GCN2 kinase. Cells lacking GCN2 are more resistant to adenovirus-mediated autophagy in vitro; in vivo we also found these cells fail to undergo autophagy, but display more cell death. We believe that autophagy is a protective mechanism the cell employs during adenoviral infection, and in the in vivo environment, cells cannot recover from virus infection and are more susceptible to death. Congruently, infected cells deficient for autophagy through deletion of ATG5 are not able undergo productive cell lysis, providing evidence that the destruction of the cytoplasm and cell membrane through autophagy is crucial to the viral life cycle. This project is the first to describe a gene, other than a named autophagy gene, to be required for adenovirus- mediated autophagy. It is also the first to examine autophagic cell death as a means to aid in viral-induced cell lysis.
Resumo:
The prognosis for lung cancer patients remains poor. Five year survival rates have been reported to be 15%. Studies have shown that dose escalation to the tumor can lead to better local control and subsequently better overall survival. However, dose to lung tumor is limited by normal tissue toxicity. The most prevalent thoracic toxicity is radiation pneumonitis. In order to determine a safe dose that can be delivered to the healthy lung, researchers have turned to mathematical models predicting the rate of radiation pneumonitis. However, these models rely on simple metrics based on the dose-volume histogram and are not yet accurate enough to be used for dose escalation trials. The purpose of this work was to improve the fit of predictive risk models for radiation pneumonitis and to show the dosimetric benefit of using the models to guide patient treatment planning. The study was divided into 3 specific aims. The first two specifics aims were focused on improving the fit of the predictive model. In Specific Aim 1 we incorporated information about the spatial location of the lung dose distribution into a predictive model. In Specific Aim 2 we incorporated ventilation-based functional information into a predictive pneumonitis model. In the third specific aim a proof of principle virtual simulation was performed where a model-determined limit was used to scale the prescription dose. The data showed that for our patient cohort, the fit of the model to the data was not improved by incorporating spatial information. Although we were not able to achieve a significant improvement in model fit using pre-treatment ventilation, we show some promising results indicating that ventilation imaging can provide useful information about lung function in lung cancer patients. The virtual simulation trial demonstrated that using a personalized lung dose limit derived from a predictive model will result in a different prescription than what was achieved with the clinically used plan; thus demonstrating the utility of a normal tissue toxicity model in personalizing the prescription dose.