Dosimetric feasibility of stereotactic irradiation of primary prostate cancer at 5x9 Gy with a method of urethral sparing

Impact of technological advances in treatment planning, image guidance, and treatment delivery on target margin design for prostate cancer radiotherapy: an updated review

Recent innovations in image guidance, treatment delivery, and adaptive radiotherapy (RT) have created a new paradigm for planning target volume (PTV) margin design for patients with prostate cancer. We performed a review of the recent literature on PTV margin selection and design for intact prostate RT, excluding post-operative RT, brachytherapy, and proton therapy. Our review describes the increased focus on prostate and seminal vesicles as heterogenous deforming structures with further emergence of intra-prostatic GTV boost and concurrent pelvic lymph node treatment. To capture recent innovations, we highlight the evolution in cone beam CT guidance, and increasing use of MRI for improved target delineation and image registration and supporting online adaptive RT. Moreover, we summarize new and evolving image-guidance treatment platforms as well as recent reports of novel immobilization strategies and motion tracking. Our report also captures recent implementations of artificial intelligence to support image guidance and adaptive RT. To characterize the clinical impact of PTV margin changes via model-based risk estimates and clinical trials, we highlight recent high impact reports. Our report focusses on topics in the context of PTV margins but also showcase studies attempting to move beyond the PTV margin recipes with robust optimization and probabilistic planning approaches. Although guidelines exist for target margins conventional using CT-based image guidance, further validation is required to understand the optimal margins for online adaptation either alone or combined with real-time motion compensation to minimize systematic and random uncertainties in the treatment of patients with prostate cancer.

Fully automated segmentation of prostatic urethra for MR-guided radiation therapy

PURPOSE

Accurate delineation of the urethra is a prerequisite for urethral dose reduction in prostate radiotherapy. However, even in magnetic resonance-guided radiation therapy (MRgRT), consistent delineation of the urethra is challenging, particularly in online adaptive radiotherapy. This paper presented a fully automatic MRgRT-based prostatic urethra segmentation framework.

METHODS

Twenty-eight prostate cancer patients were included in this study. In-house 3D half fourier single-shot turbo spin-echo (HASTE) and turbo spin echo (TSE) sequences were used to image the Foley-free urethra on a 0.35 T MRgRT system. The segmentation pipeline uses 3D nnU-Net as the base and innovatively combines ground truth and its corresponding radial distance (RD) map during training supervision. Additionally, we evaluate the benefit of incorporating a convolutional long short term memory (LSTM-Conv) layer and spatial recurrent convolution layer (RCL) into nnU-Net. A novel slice-by-slice simple exponential smoothing (SEPS) method specifically for tubular structures was used to post-process the segmentation results.

RESULTS

The experimental results show that nnU-Net trained using a combination of Dice, cross-entropy and RD achieved a Dice score of 77.1 ± 2.3% in the testing dataset. With SEPS, Hausdorff distance (HD) and 95% HD were reduced to 2.95 ± 0.17 mm and 1.84 ± 0.11 mm, respectively. LSTM-Conv and RCL layers only minimally improved the segmentation precision.

CONCLUSION

We present the first Foley-free MRgRT-based automated urethra segmentation study. Our method is built on a data-driven neural network with novel cost functions and a post-processing step designed for tubular structures. The performance is consistent with the need for online and offline urethra dose reduction in prostate radiotherapy.