Knowledge-Based IMRT Treatment Planning for Prostate Cancer: Experience with 101 cases from Duke Clinic
| dc.contributor.advisor | Lo, Joseph Yuan-Chieh | |
| dc.contributor.advisor | Das, Shiva K | |
| dc.contributor.author | Dick, Deon | |
| dc.date.accessioned | 2013-01-16T20:47:43Z | |
| dc.date.available | 2013-07-15T04:30:05Z | |
| dc.date.issued | 2012 | |
| dc.department | Medical Physics | |
| dc.description.abstract | Intensity-modulated radiotherapy (IMRT) has become an effective tool for cancer treatment with radiation. However, even expert radiation planners still need to spend a substantial amount of time, approximately 4 hours, manually adjusting IMRT optimization parameters such as dose limits and costlet weights in order to obtain a clinically acceptable plan. Also, the quality of the treatment plan generated is solely based on the experience and training of the planning. In comparing the geometries of the planning target volume (PTV), bladder, rectum, right and left femoral heads, a knowledge-based approach to IMRT treatment planning may reduce the time needed to generate a clinically acceptable prostate plan. The knowledge-based approach uses the clinically acceptable plans of previously irradiated patients which are adapted to the new patient. Patient selection is done by using mutual information (MI). Having selected the best matched patient, Elastix (a toolkit for rigid and deformable registration) is used to deform the treatment plan of the previously irradiated patient to the new patient's geometry. The Eclipse treatment planning system is used to generate both pre-optimized and post optimized plans for the new patients. The knowledge-based treatment plans require no manual intervention. For the 101 patient data, it was shown that the newly generated plans were of similar or slightly worse dosimetric quality and were only generated in less than 30 minutes. Given the large size of this data set, the results are likely to be robust in representing treatment planning efficacy over a diverse range of patient anatomy. The results also show that this work has the potential to automatically provide high quality treatment plans while dramatically reducing the dependence of the expertise of the planner and the treatment planning time. | |
| dc.identifier.uri | ||
| dc.subject | Medical imaging and radiology | |
| dc.subject | Elastix | |
| dc.subject | IMRT | |
| dc.subject | Knowledge-Based | |
| dc.subject | Mutual information | |
| dc.subject | NTCP | |
| dc.title | Knowledge-Based IMRT Treatment Planning for Prostate Cancer: Experience with 101 cases from Duke Clinic | |
| dc.type | Master's thesis | |
| duke.embargo.months | 6 |