Where Does Auto-Segmentation for Brain Metastases Radiosurgery Stand Today?

Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician's manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care. Recent strides in machine learning, particularly in deep learning (DL), have revolutionized medical image segmentation, achieving state-of-the-art results. This review aims to analyze auto-segmentation strategies, characterize the utilized data, and assess the performance of cutting-edge BM segmentation methodologies. Additionally, we delve into the challenges confronting BM segmentation and share insights gleaned from our algorithmic and clinical implementation experiences.

Stereotactic radiosurgery for sarcoma metastases to the brain: a single-institution experience.

OBJECTIVE: Brain metastases (BMs) secondary to sarcoma are rare, and their incidence ranges from 1% to 8% of all bone and soft tissue sarcomas. Although stereotactic radiosurgery (SRS) is widely used for BMs, only a few papers have reported on SRS for sarcoma metastasizing to the brain. The purpose of this study was to evaluate the safety and effectiveness of SRS for sarcoma BM. METHODS: The authors retrospectively reviewed the clinical and radiological outcomes of patients with BM secondary to histopathologically confirmed sarcoma treated with SRS, either as primary treatment or as adjuvant therapy after surgery, at their institution between January 2005 and September 2022. They also compared the outcomes of patients with hemorrhagic lesions and of those without. RESULTS: Twenty-three patients (9 females) with 150 BMs secondary to sarcoma were treated with CyberKnife SRS. Median age at the time of treatment was 48.22 years (range 4-76 years). The most common primary tumor sites were the heart, lungs, uterus, upper extremities, chest wall, and head and neck. The median Karnofsky Performance Status on presentation was 73.28 (range 40-100). Eight patients underwent SRS as a primary treatment and 15 as adjuvant therapy to the resection cavity. The median tumor volume was 24.1 cm3 (range 0.1-150.3 cm3), the median marginal dose was 24 Gy (range 18-30 Gy) delivered in a median of 1 fraction (range 1-5) to a median isodose line of 76%. The median follow-up was 8 months (range 2-40 months). Median progression-free survival and overall survival were 5.3 months (range 0.4-32 months) and 8.2 months (range 0.1-40), respectively. The 3-, 6-, and 12-month local tumor control (LTC) rates for all lesions were respectively 78%, 52%, and 30%. There were no radiation-induced adverse effects. LTC at the 3-, 6-, and 12-month follow-ups was better in patients without hemorrhagic lesions (100%, 70%, and 40%, respectively) than in those with hemorrhagic lesions (68%, 38%, and 23%, respectively). CONCLUSIONS: SRS, both as a primary treatment and as adjuvant therapy to the resection cavity after surgery, is a safe and relatively effective treatment modality for sarcoma BMs. Nonhemorrhagic lesions show better LTC than hemorrhagic lesions. Larger studies aiming to validate these results are encouraged.

Stereotactic body radiotherapy optimization to reduce the risk of carotid blowout syndrome using normal tissue complication probability objectives.

PURPOSE: To determine the possibility of further improving clinical stereotactic body radiotherapy (SBRT) plans using normal tissue complication probability (NTCP) objectives in order to minimize the risk for carotid blowout syndrome (CBOS). METHODS: 10 patients with inoperable locally recurrent head and neck cancer, who underwent SBRT using CyberKnife were analyzed. For each patient, three treatment plans were examined: (1) cone-based without delineation of the ipsilateral internal carotid (clinical plan used to treat the patients); (2) cone-based with the carotid retrospectively delineated and spared; and (3) Iris-based with carotid sparing. The dose-volume histograms of the target and primary organs at risk were calculated. The three sets of plans were compared based on dosimetric and TCP/NTCP (tumor control and normal tissue complication probabilities) metrics. For the NTCP values of carotid, the relative seriality model was used with the following parameters: D50 = 40 Gy, γ = 0.75, and s = 1.0. RESULTS: Across the 10 patient plans, the average TCP did not significantly change when the plans were re-optimized to spare the carotid. The estimated risk of CBOS was significantly decreased in the re-optimized plans, by 14.9% ± 7.4% for the cone-based plans and 17.7% ± 7.1% for the iris-based plans (p = 0.002 for both). The iris-based plans had significant (p = 0.02) reduced CBOS risk and delivery time (20.1% ± 7.4% time reduction, p = 0.002) compared to the cone-based plans. CONCLUSION: A significant improvement in the quality of the clinical plans could be achieved through the delineation of the internal carotids and the use of more modern treatment delivery modalities. In this way, for the same target coverage, a significant reduction in the risk of CBOS could be achieved. The range of risk reduction varied depending on the proximity of carotid artery to the target.

Synthetic digital reconstructed radiographs for MR-only robotic stereotactic radiation therapy: A proof of concept.

PURPOSE: To create synthetic CTs and digital reconstructed radiographs (DRRs) from MR images that allow for fiducial visualization and accurate dose calculation for MR-only radiosurgery. METHODS: We developed a machine learning model to create synthetic CTs from pelvic MRs for prostate treatments. This model has been previously proven to generate synthetic CTs with accuracy on par or better than alternate methods, such as atlas-based registration. Our dataset consisted of 11 paired CT and conventional MR (T2) images used for previous CyberKnife (Accuray, Inc) radiotherapy treatments. The MR images were pre-processed to mimic the appearance of fiducial-enhancing images. Two models were trained for each parameter case, using a sub-set of the available image pairs, with the remaining images set aside for testing and validation of the model to identify the optimal patch size and number of image pairs used for training. Four models were then trained using the identified parameters and used to generate synthetic CTs, which in turn were used to generate DRRs at angles 45° and 315°, as would be used for a CyberKnife treatment. The synthetic CTs and DRRs were compared visually and using the mean squared error and peak signal-to-noise ratio against the ground-truth images to evaluate their similarity. RESULTS: The synthetic CTs, as well as the DRRs generated from them, gave similar visualization of the fiducial markers in the prostate as the true counterparts. There was no significant difference found for the fiducial localization for the CTs and DRRs. Across the 8 DRRs analyzed, the mean MSE between the normalized true and synthetic DRRs was 0.66 ± 0.42% and the mean PSNR for this region was 22.9 ± 3.7 dB. For the full CTs, the mean MAE was 72.9 ± 88.1 HU and the mean PSNR was 31.2 ± 2.2 dB. CONCLUSIONS: Our machine learning-based method provides a proof of concept of a way to generate synthetic CTs and DRRs for accurate dose calculation and fiducial localization for use in radiation treatment of the prostate.

Optimization of hexagonal-pattern minibeams for spatially fractionated radiotherapy using proton beam scanning.

PURPOSE: In this study, we investigated computationally and experimentally a hexagonal-pattern array of spatially fractionated proton minibeams produced by proton pencil beam scanning (PBS) technique. Spatial fractionation of dose delivery with millimeter or submillimeter beam size has proven to be a promising approach to significantly increase the normal tissue tolerance. Our goals are to obtain an optimized minibeam design and to show that it is feasible to implement the optimized minibeams at the existing proton clinics. METHODS: An optimized minibeam arrangement is one that would produce high peak-to-valley dose ratios (PVDRs) in normal tissues and a PVDR approaching unity at the Bragg peak. Using Monte Carlo (MC) code TOPAS we simulated proton pencil beams that mimic those available at the existing proton therapy facilities and obtained a hexagonal-pattern array of minibeams by collimating the proton pencil beams through the 1-3 mm diameter pinholes of a collimator. We optimized the minibeam design by considering different combinations of parameters including collimator material and thickness (t), center-to-center (c-t-c) distance, and beam size. The optimized minibeam design was then evaluated for normal tissue sparing against the uniform pencil beam scanning (PBS) by calculating the therapeutic advantage (TA) in terms of cell survival fraction. Verification measurements using radiochromic films were performed at the Emory proton therapy center (EPTC). RESULTS: Optimized hexagonal-pattern minibeams having PVDRs of >10 at phantom surface and of >3 at depths up to 6 cm were achieved with 2 mm diameter modulated proton minibeams (with proton energies between 120 and 140 MeV) corresponding to a spread-out-Bragg-peak (SOBP) over the depth of 10-14 cm. The results of the film measurements agree with the MC results within 10%. The TA of the 2 mm minibeams against the uniform PBS is >3 from phantom surface to the depth of 5 cm and then smoothly drops to ~1.5 as it approaches the proximal edge of the SOBP. For 2 mm minibeams and 6 mm c-t-c distance, we delivered 1.72 Gy at SOBP for 7.2 × 7.2 × 4 cm3 volume in 48 s. CONCLUSIONS: We conclude that it is feasible to implement the optimized hexagonal-pattern 2 mm proton minibeam radiotherapy at the existing proton clinics, because desirable PVDRs and TAs are achievable and the treatment time is reasonable.