iCONE-SRS: Development of inverse treatment planning for cone-based stereotactic radiosurgery.

PURPOSE: At present, commercially available treatment planning systems (TPS) only offer manual planning functionality for cone-based stereotactic radiosurgery (SRS) leading to labor intensive treatment planning. Our objective was to reduce treatment planning time through development of a simple inverse TPS for cone-based SRS. METHODS: The iCONE TPS was developed using MATLAB (R2015a, The MathWorks Inc.) and serves as an inverse planning adjunct to a commercially available TPS. Simulated annealing is used to determine optimal table angle, gantry start and stop angles, and cone sizes for a user-defined number of non-coplanar arcs relative to user-defined dose objectives. iCONE and clinically generated plans were compared through a retrospective planning study of 60 patients treated for 1-3 brain metastases (total of 100 lesions). RESULTS: Planning target volume (PTV) coverage was enforced for all plans through normalization. PTV maximum dose was constrained to be within 120%-135% of the prescription dose. The median conformity index for iCONE plans was 1.35, 1.33, and 1.32 for 1, 2, and 3-target cases respectively corresponding to a median increase of 0.05 (range = -0.1 to 0.5, P < 0.05), 0.06 (range = -0.83 to 0.53, P < 0.05), and 0.03 (range = -1.21 to 0.74, P > 0.05) relative to the clinical plans. No clinically significant differences were found with respect to the dose to organs-at-risk. Median iCONE planning times were approximately a factor of five lower than consensus estimates for manual planning provided by local experienced SRS planners. CONCLUSIONS: A simple inverse TPS for cone-based SRS was developed. Plan quality was found to be similar to manually generated plans; however, degradation was observed in some cases highlighting the need for continued oversight and manual adjustment by experienced planners if implemented in the clinic. A factor of five reduction in treatment planning time was estimated.

Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study.

For multicenter clinical studies, characterizing the robustness of image-derived radiomics features is essential. Features calculated on PET images have been shown to be very sensitive to image noise. The purpose of this work was to investigate the efficacy of a relatively simple harmonization strategy on feature robustness and agreement. A purpose-built texture pattern phantom was scanned on 10 different PET scanners in 7 institutions with various different image acquisition and reconstruction protocols. An image harmonization technique based on equalizing a contrast-to-noise ratio was employed to generate a "harmonized" alongside a "standard" dataset for a reproducibility study. In addition, a repeatability study was performed with images from a single PET scanner of variable image noise, varying the binning time of the reconstruction. Feature agreement was measured using the intraclass correlation coefficient (ICC). In the repeatability study, 81/93 features had a lower ICC on the images with the highest image noise as compared to the images with the lowest image noise. Using the harmonized dataset significantly improved the feature agreement for five of the six investigated feature classes over the standard dataset. For three feature classes, high feature agreement corresponded with higher sensitivity to the different patterns, suggesting a way to select suitable features for predictive models.