A Deep-Learning Method of Automatic VMAT Planning via MLC Dynamic Sequence Prediction (AVP-DSP) Using 3D Dose Prediction: A Feasibility Study of Prostate Radiotherapy Application
| dc.contributor.advisor | Wu, Qingrong (Jackie) | |
| dc.contributor.author | Ni, Yimin | |
| dc.date.accessioned | 2020-06-09T17:45:30Z | |
| dc.date.available | 2021-06-01T08:17:12Z | |
| dc.date.issued | 2020 | |
| dc.department | DKU - Medical Physics Master of Science Program | |
| dc.description.abstract | Introduction: VMAT treatment planning requires time-consuming DVH-based inverse optimization process, which impedes its application in time-sensitive situations. This work aims to develop a deep-learning based algorithm, Automatic VMAT Planning via MLC Dynamic Sequence Prediction (AVP-DSP), for rapid prostate VMAT treatment planning. Methods: AVP-DSP utilizes a series of 2D projections of a patient’s dose prediction and contour structures to generate a single 360º dynamic MLC sequence in a VMAT plan. The backbone of AVP-DSP is a novel U-net implementation which has a 4-resolution-step analysis path and a 4-resolution-step synthesis path. AVP-DSP was developed based on 131 previous prostate patients who received simultaneously-integrated-boost (SIB) treatment (58.8Gy/70Gy to PTV58.8/PTV70 in 28fx). All patients were planned by a 360º single-arc VMAT technique using an in-house intelligent planning tool in a commercial treatment planning system (TPS). 120 plans were used in AVP-DSP training/validation, and 11 plans were used as independent tests. Key dosimetric metrics achieved by AVP-DSP were compared against the ones planned by the commercial TPS. Results: After dose normalization (PTV70 V70Gy=95%), all 11 AVP-DSP test plans met institutional clinic guidelines of dose distribution outside PTV. Bladder (V70Gy=6.8±3.6cc, V40Gy=19.4±9.2%) and rectum (V70Gy=2.8±1.8cc, V40Gy=26.3±5.9%) results in AVP-DSP plans were comparable with the commercial TPS plan results (bladder V70Gy=4.1±2.0cc, V40Gy=17.7±8.9%; rectum V70Gy=1.4±0.7cc, V40Gy=24.0±5.0%). 3D max dose results in AVP-DSP plans(D1cc=118.9±4.1%) were higher than the commercial TPS plans results(D1cc=106.7±0.8%). On average, AVP-DSP used 30 seconds for a plan generation in contrast to the current clinical practice (>20 minutes). Conclusion: Results suggest that AVP-DSP can generate a prostate VMAT plan with clinically-acceptable dosimetric quality. With its high efficiency, AVP-DSP may hold great potentials of real-time planning application after further validation. | |
| dc.identifier.uri | ||
| dc.subject | Medicine | |
| dc.subject | Applied physics | |
| dc.subject | Deep learning | |
| dc.subject | real-time planning | |
| dc.subject | Treatment planning | |
| dc.title | A Deep-Learning Method of Automatic VMAT Planning via MLC Dynamic Sequence Prediction (AVP-DSP) Using 3D Dose Prediction: A Feasibility Study of Prostate Radiotherapy Application | |
| dc.type | Master's thesis | |
| duke.embargo.months | 11.736986301369862 |