Simultaneous optimization of isocenter locations and sector duration in radiosurgery

Treatment Planning Methods for Dose Painting by Numbers Treatment in Gamma Knife Radiosurgery

Purpose

Dose painting radiation therapy delivers a nonuniform dose to tumors to account for heterogeneous radiosensitivity. With recent and ongoing development of Gamma Knife machines making large-volume brain tumor treatments more practical, it is increasingly feasible to deliver dose painting treatments. The increased prescription complexity means automated treatment planning is greatly beneficial, and the impact of dose painting on stereotactic radiosurgery (SRS) plan quality has not yet been studied. This research investigates the plan quality achievable for Gamma Knife SRS dose painting treatments when using optimization techniques and automated isocenter placement in treatment planning.

Methods and Materials

Dose painting prescription functions with varying parameters were applied to convert voxel image intensities to prescriptions for 10 sample cases. To study achievable plan quality and optimization, clinically placed isocenters were used with each dose painting prescription and optimized using a semi-infinite linear programming formulation. To study automated isocenter placement, a grassfire sphere-packing algorithm and a clinically available Leksell gamma plan isocenter fill algorithm were used. Plan quality for each optimized treatment plan was measured with dose painting SRS metrics.

Results

Optimization can be used to find high quality dose painting plans, and plan quality is affected by the dose painting prescription method. Polynomial function prescriptions show more achievable plan quality than sigmoid function prescriptions even with high mean dose boost. Automated isocenter placement is shown as a feasible method for dose painting SRS treatment, and increasing the number of isocenters improves plan quality. The computational solve time for optimization is within 5 minutes in most cases, which is suitable for clinical planning.

Conclusions

The impact of dose painting prescription method on achievable plan quality is quantified in this study. Optimization and automated isocenter placement are shown as possible treatment planning methods to obtain high quality plans.

Tuning-Target-Guided Inverse Planning of Brain Tumors With Abutting Organs at Risk During Gamma Knife Stereotactic Radiosurgery

Purpose We proposed a planning strategy that utilized tuning targets to guide GammaKnife (GK) Inverse Planning (IP) to deliver higher dose to the tumor, while keeping acceptable dose to the abutting organ at risk (OAR). Methods Ten patients with a large portion of brain tumor abutting the OAR previously treated with GK stereotactic radiosurgery (SRS) were selected. For each patient, multiple tuning targets were created by cropping the target contour from three-dimensional (3D) expansions of the OAR. The number of the tuning targets depended on the complexity of the planning process. To demonstrate dose sparing effect, an IP plan was generated for each tuning target after one round of optimization without shot fine-tuning. In the dose enhancement study, a more aggressive target dose was prescribed to the tuning target with a larger margin and one to two shots were filled in the region with missing dose. The resulting plans were compared to the previously approved clinical plans. Results For all 10 patients, a dose sparing effect was observed, i.e. both target coverage and dose to the OARs decreased when the margins of 3D expansion increased. For one patient, a margin of 6 mm was needed to decrease the maximum dose to the optical chiasm and optical nerve by 44.3% and 28.4%, respectively. For the other nine patients, the mean dropping rate of V12Gyto brain stem were 28.2% and 59.5% for tuning targets of 1 and 2 mm margins, respectively. In the dose enhancement study, the tuning-target-guided plans were hotter than the approved treatment plans, while keeping similar dose to the OARs. The mean of the treatment and enhancement dose was 15.6 ± 2.2 Gy and 18.5 ± 3.2 Gy, respectively. The mean coverage of the target by prescription dose was slightly higher in the enhancement plans (96.9 ± 2.6% vs 96.3 ± 3.6%), whereas the mean coverage of the enhancement dose was 20.1% higher in the enhancement plans (89.6 ± 9.0% vs 74.6 ± 19.9%). Conclusions We demonstrated that an inverse planning strategy could facilitate target dose enhancement for challenging GK cases while keeping acceptable OAR dose.

Knowledge-based isocenter selection in radiosurgery planning

PURPOSE

We present a new method for knowledge-based isocenter selection for treatment planning in radiosurgery. Our objective is to develop a prediction model that can learn from past manually designed treatment plans. We leverage recent advances in deep learning to predict isocenter locations in treatment plans in order to provide a decision support tool.

METHODS

The proposed method adapts a geometric approach using orthogonal moment expansions as a feature vector for describing the shape of the tumor. Our approach accounts primarily for tumor shape and OAR proximity, the two factors that are known to greatly affect the isocenter placement. We solve the prediction problem by training a residual neural network with skip connections on the formed shape descriptors. Our network was trained on 533 patient cases and was validated on a set of out-of-sample cases.

RESULTS

Our method generates heatmap predictions for isocenter locations that are in most cases comparable to the experienced human planners, which shows that the method can be used in treatment planning to guide the users for determining the isocenters.

CONCLUSIONS

Our numerical experiments indicate a positive predictive value on an independent validation set when compared against a test dataset that was not seen by the model during training.