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.

Modeling gamma knife radiosurgical toxicity for multiple brain metastases

BACKGROUND AND PURPOSE

Radiation oncology protocols for single fraction radiosurgery recommend setting dosing criteria based on assumed risk of radionecrosis, which can be predicted by the 12 Gy normal brain volume (V12). In this study, we show that tumor surface area (SA) and a simple power-law model using only preplan variables can estimate and minimize radiosurgical toxicity.

MATERIALS AND METHODS

A 245-patient cohort with 1217 brain metastases treated with single or distributed Gamma Knife sessions was reviewed retrospectively. Univariate and multivariable linear regression models and power-law models determined which modeling parameters best predicted V12. The V12 power-law model, represented by a product of normalized Rx dose Rxn, and tumor longest axial dimension LAD (V12 ∼ Rxn1.5*LAD2), was independently validated using a secondary 63-patient cohort with 302 brain metastases.

RESULTS

Surface area was the best univariate linear predictor of V12 (adjR2 = 0.770), followed by longest axial dimension (adjR2 = 0.755) and volume (adjR2 = 0.745). The power-law model accounted for 90% variance in V12 for 1217 metastatic lesions (adjR2 = 0.906) and 245 patients (adjR2 = 0.896). The average difference ΔV12 between predicted and measured V12s was (0.28 ± 0.55) cm3 per lesion and (1.0 ± 1.2) cm3 per patient. The power-law predictive capability was validated using a secondary 63-patient dataset (adjR2 = 0.867) with 302 brain metastases (adjR2 = 0.825).

CONCLUSION

Surface area was the most accurate univariate predictor of V12 for metastatic lesions. We developed a preplan model for brain metastases that can help better estimate radionecrosis risk, determine prescription doses given a target V12, and provide safe dose escalation strategies without the use of any planning software.

Multi-Institutional Phase II Trial Using Dose Escalated Five Fraction Stereotactic Partial Breast Irradiation (S-PBI) with GammaPodTM for Early-Stage Breast Cancer

PURPOSE/OBJECTIVE(S)

We report on our early experience of a multi-institutional phase II study of dose escalated five fraction stereotactic partial breast irradiation (S-PBI) for early-stage breast cancer after partial mastectomy using the GammaPodTM stereotactic radiation system.

MATERIALS/METHODS

Patient eligibility included DCIS or invasive epithelial histologies, AJCC clinical stage 0, I, or II with tumor size < 3 cm, and negative margins. Prior safety of Phase I dose escalation has been reported. Dose was 40 Gy delivered in 5 fractions to the CTV, and minimum dose 30 Gy in 5 fractions to the PTV. CTV margin was 1 cm and PTV margin 3 mm. For PTV cavities larger than 100cc, dose was reduced to 35Gy in 5 fractions to the CTV and 30 Gy in 5 fractions to the PTV. Primary endpoint of the study is to determine the 3-year patient global cosmesis score (4-point scale excellent, good, fair, or poor) and adverse cosmesis using a dose escalated approach with smaller PTV margins than conventional methods. Both patients and physicians completed baseline and subsequent cosmesis outcome questionnaires. Treatment related toxicity was graded using the NCI version 4.0 and RTOG/EORTC late radiation scale.

RESULTS

From 3/2019-10/2021, 74 patients were treated respectively. Of these, 38 were treated to 40Gy and 36 were treated to 35 Gy. Median follow up (f/u) was 24 months (mo), range (r) 3-39mo. Median age was 63 years (r 43-77). Histology included 28 DCIS, and 46 invasive carcinomas. 45/46 invasive tumors were ER+. 60/74 (81%) patients received endocrine therapy, and 7/74 patient received chemotherapy. There were 221 acute grade 1 toxicities, and 28 Grade 2 toxicities. No grade 3 or higher acute toxicities were reported (< 90 days). The most common Grade 2 toxicities were radiation dermatitis (10), breast pain (8), blister (4), skin infection (2), nipple discharge (2), and fatigue (2). In the late period, there were 54 Grade 1 late toxicities, 4 Grade 2 late toxicities, and no Grade 3 or higher late toxicities. Grade 2 toxicities included fibrosis (2), and pain (2). Two patients developed grade 1 asymptomatic nonpalpable fat necrosis both diagnosed at 12 months after radiation treatments. The most common grade 1 late toxicities were breast pain (14), hyperpigmentation (8), fibrosis (10), and fatigue (5). Physicians scored cosmesis excellent or good 70/73 (95.8%), 58/60 (96.7%), 36/36 (100%),17/17(100%) respectively at baseline, 12 months, 24 months, and 36months post SBRT, while patients scored the same periods 62/71 (83.7%), 53/59 (89.8%), 33/36 (91.6%), 17/18 (94.4%). There have been no reports of disease recurrences.

CONCLUSION

Results at 24-month median follow-up, of our dose escalated stereotactic partial breast 5 fraction regimen, has low acute and late toxicity, while maintaining high proportion of excellent/good cosmetic outcomes. Continued analysis of all cohorts is in progress.

CLINICAL TRIALS

gov identifier is NCT03581136.

Assessment of Cardiac Radiation Dose in the Co-60 Prone Based Stereotactic Partial Breast Irradiation (CP-sPBI) Using the Distance from the Heart to the Planning Treatment Volume as a Surrogate Marker

PURPOSE/OBJECTIVE(S)

Irradiation of the breast has shown to provide sharp dose gradients using Co-60 prone based stereotactic partial breast irradiation (CP-sPBI), a contemporary device for stereotactic radiotherapy for breast cancer (BC) for accelerated partial breast irradiation (APBI). In addition, the precise setup of CP-sPBI permits a small planning treatment volume (PTV) margin of 3 mm creating a greater distance from PTV to organs at risk. However, to date the factors that influence dose gradients and subsequent cardiac doses of ionizing radiation using CP-sPBI have not been well-studied. Here we evaluate distance of the heart to the lumpectomy PTV cavity and how this effects cardiac dose.

MATERIALS/METHODS

A retrospective database of 113 consecutive patients treated by CP-sPBI for APBI from March 2019 to February 2023 who were treated with 30 Gy in 5 fractions were queried for analysis. The minimum distance from the heart to the PTV (hP) was measured in either the axial or sagittal view. A group of 28 patient cases were randomly selected to achieve an even distribution of 28 cases with hP < 2.75 cm and hP ≥ 2.75 cm to compare cardiac toxicities based on hP. Descriptive analyses were performed to evaluate various cardiac dosimetric parameters based on laterality of BC and hP, using the student's t test.

RESULTS

The mean (range) hP was 4.58 cm (0.80-12.23) for all cases. The subgroup analyses of 28 patient cases with cardiac parameters showed the heart mean (range) dose of 1.20 Gy (0.01-2.11). The mean and max heart dose to the left-sided BC were similar to those to the right-sided BC (mean dose: 1.20 vs. 1.19 Gy; P = 0.97 and max dose: 10.47 vs. 5.66 Gy; P = 0.06). An inverse correlation between hP and mean heart dose was shown with the correlation coefficient of -0.81. Using a cutoff of 2.75 cm hP, the differences between hP < 2.75 and hP ≥ 2.75 cm for all cardiac dosimetric evaluations were all statistically significant, including mean (1.67 vs. 0.79 Gy; p<0.01) and maximal heart dose (14.48 vs. 4.11 Gy; p<0.01) CONCLUSION: CP-sPBI treatment delivery system was able to achieve acceptable clinically relevant heart dosimetric parameters when delivering 5 fraction APBI with a mean heart dose of 1.20 Gy for all locations of PTV cavity volume in the breast. Due to CP-sPBIs excellent dose fall-off characteristics, APBI using CP-SPBI showed clinically acceptable cardiac dosimetric parameters, particularly for PTVs located > 2.75 cm from the heart.

Modeling Gamma Knife Radiosurgical Toxicity for Multiple Brain Metastases

PURPOSE/OBJECTIVE(S)

Dosing for single fraction radiosurgery has traditionally relied on tumor measurements from a single maximum diameter. Most protocols recommend setting dosing criteria based on assumed risk of radionecrosis roughly correlating with tumor size. However, the risk of radionecrosis after radiosurgery is best modeled by a function of dose and volume treated, with the largest body of evidence supporting the use of brain tissue receiving ≥12 Gy in one fraction (V12, i.e., > 10.9 cm3). Here we show that tumor surface area (SA) and second order dimensions are superior predictors for Gamma Knife radiosurgical toxicity and can be used to estimate V12.

MATERIALS/METHODS

A total of 1217 brain metastases from 245 patients treated with a prescribed dose from 13 to 27 Gy in one fraction were retrospectively reviewed. Eight independent modeling parameters were considered; 3 geometric tumor characteristics: SA, volume (V), and largest axial dimension (LAD) and 5 treatment planning variables: prescription dose (Rx), coverage, selectivity, gradient index, and number of shots. Linear regression and power-law formulations were performed to determine which parameters were the most accurate predictors of V12. The power model is dependent on a conceptualized "pseudo surface area" (PSA), defined as the surface area of a sphere with a diameter of LAD of a lesion (PSA = π*LAD2). At the aggregate patient level, the model predicts total brain V12 by summing the V12 values for each singular lesion only by using LAD and Rx as input variables.

RESULTS

Tumor SA was the best univariate linear predictor of V12 (adjR2 = 0.770), followed by LAD (adjR2 = 0.755) and V (adjR2 = 0.745). The SA predictive model improves for lesions that have high sphericity > 0.85 (adjR2 = 0.837), with a measure of 1 indicating a perfect sphere. Using bivariable regression analysis, we formulated a single term power model that even more accurately predicts for V12 (V12 = 0.0137 * Rx1.5 * LAD2, adjR2 = 0.906) and is proportional to PSA. At the patient level, this model also accurately predicts for total brain V12 (adjR2 = 0.896) and V12 > 10.9 cm3 (Sensitivity = 99.1%, Specificity = 90.5%).

CONCLUSION

Conceptually, SA univariately predicts for V12 more accurately than other tumor physical dimensions or treatment planning parameters, while the best bivariable power model involves PSA. We provide a preplan model for brain metastases that can help better estimate radionecrosis risk, determine prescription doses given a target V12, and provide safe dose escalation strategies without the use of any planning software.

Predicting Neurocognitive Decline in Multiple Brain Metastases Patients Undergoing Distributed Stereotactic Radiosurgery

PURPOSE/OBJECTIVE(S)

Stereotactic radiosurgery (SRS) is the standard of care for treating a limited number (<3) of brain metastasis (BMs), which offers reduced neurotoxicity compared to whole brain radiotherapy (WBRT). Contemporary advancements in SRS made it possible to also commonly treat multiple (>4) BMs (mBMs). Emphasizing the value of preserving quality of life (QoL) after SRS, there is an urgent need for a systematic study of potential neurocognitive decline in patients receiving SRS treatment for mBMs. The purpose of this study is to use routine MRIs to predict neurocognitive decline for patients treated with distributed SRS, allowing for timely and effective treatment strategy design.

MATERIALS/METHODS

This study uses data from an institutional phase I/II clinical trial to determine the neurocognitive decline in patients with (>6) mBMs treated with distributed SRS. In the first 12 months post-SRS, participants are followed and evaluated with routine MRIs and the Hopkins Verbal Learning Test-Revised (HVLT-R) at 2 to 3-month intervals. Changes in HVLT-Delayed Recall scores between two visits are used to define neurocognitive decline. For each visit, an in-house deep learning model segments 66 cortical and 55 subcortical brain regions of interest (ROIs) from the T1 structural MRI and extracts 253 ROI features, including the surface area and thickness of cortical ROIs, and the volume of all ROIS. The difference in ROI features between two visits, together with other clinical factors (e.g., prescription, number of BMs, etc.), is considered as one sample. The study included 22 subjects with 91 visits, resulting in 171 samples with neurocognitive decline labels. The entire sample set is split into 10 folds on patient level for cross validation. In each fold, feature engineering is conducted to remove redundancy and to select the most-important features. The top 20% most frequently selected features are applied with Support Vector Machine to predict the neurocognitive decline label of each sample.

RESULTS

As a preliminary result, the proposed method achieves an accuracy of 76%, with an area under the curve (AUC) of 0.75, sensitivity of 0.65 and specificity of 0.83 for predicting neurocognitive decline in mBMs SRS patients using only routine T1 MRIs. The volume of lateral occipital complex, the thickness of inferior parietal lobe and postcentral gyrus, and the surface area of lateral orbitofrontal cortex and pars triangularis are identified as the 5 most important features for this task.

CONCLUSION

Our method shows promising findings for post-SRS neurocognitive decline prediction solely based on routine baseline and follow-up MRIs. In addition, it can identify critical brain ROIs associated with the post-SRS cognitive function. This method has the potential to assist treatment planning strategy to help preserve patients' QoL.

Machine characterization and central axis depth dose data of a superficial x-ray radiotherapy unit

Objectives. The purpose of this study is to present data from the clinical commissioning of an Xstrahl 150 x-ray unit used for superficial radiotherapy,Methods. Commissioning tasks included vendor acceptance tests, timer reproducibility, linearity and end-effect measurements, half-value layer (HVL) measurements, inverse square law verification, head-leakage measurements, and beam output calibration. In addition, percent depth dose (PDD) curves were determined for different combinations of filter/kV settings and applicators. Automated PDD water phantom scans were performed utilizing four contemporary detectors: a microDiamond detector, a microSilicon detector, an EDGE detector, and a PinPoint ionization chamber. The measured PDD data were compared to the published values in BJR Supplement 25,Results. The x-ray unit's mechanical, safety, and radiation characteristics were within vendor-stated specifications. Across sixty commissioned x-ray beams, the PDDs determined in water using solid state detectors were in excellent agreement with the BJR 25 data. For the lower (<100 kVp) and medium-energy (≥100 kVp) superficial beams the average agreement was within [-3.6,+0.4]% and [-3.7,+1.4]% range, respectively. For the high-energy superficial (low-energy orthovoltage) x-rays at 150 kVp, the average difference for the largest 20 × 20 cm²collimator was (-0.7 ± 1.0)%,Conclusions. This study presents machine characterization data collected for clinical use of a superficial x-ray unit. Special focus was placed on utilizing contemporary detectors and techniques for the relative PDD measurements using a motorized water phantom. The results in this study confirm that the aggregate values published in the BJR 25 report still serve as a valid benchmark when comparing data from site-specific measurements, or the reference data for clinical utilization without such measurements,Advances in knowledge. This paper presents comprehensive data from the acceptance and commissioning of a modern kilovoltage superficial x-ray radiotherapy machine. Comparisons between the PDD data measured in this study using different detectors and BJR 25 data are highlighted.

Ensemble learning for glioma patients overall survival prediction using pre-operative MRIs

Objective: Gliomas are the most common primary brain tumors. Approximately 70% of the glioma patients diagnosed with glioblastoma have an averaged overall survival (OS) of only ∼16 months. Early survival prediction is essential for treatment decision-making in glioma patients. Here we proposed an ensemble learning approach to predict the post-operative OS of glioma patients using only pre-operative MRIs.Approach: Our dataset was from the Medical Image Computing and Computer Assisted Intervention Brain Tumor Segmentation challenge 2020, which consists of multimodal pre-operative MRI scans of 235 glioma patients with survival days recorded. The backbone of our approach was a Siamese network consisting of twinned ResNet-based feature extractors followed by a 3-layer classifier. During training, the feature extractors explored traits of intra and inter-class by minimizing contrastive loss of randomly paired 2D pre-operative MRIs, and the classifier utilized the extracted features to generate labels with cost defined by cross-entropy loss. During testing, the extracted features were also utilized to define distance between the test sample and the reference composed of training data, to generate an additional predictor via K-NN classification. The final label was the ensemble classification from both the Siamese model and the K-NN model.Main results: Our approach classifies the glioma patients into 3 OS classes: long-survivors (>15 months), mid-survivors (between 10 and 15 months) and short-survivors (<10 months). The performance is assessed by the accuracy (ACC) and the area under the curve (AUC) of 3-class classification. The final result achieved an ACC of 65.22% and AUC of 0.81.Significance: Our Siamese network based ensemble learning approach demonstrated promising ability in mining discriminative features with minimal manual processing and generalization requirement. This prediction strategy can be potentially applied to assist timely clinical decision-making.

Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation

Stereotactic radiosurgery (SRS) is now the standard of care for brain metastases (BMs) patients. The SRS treatment planning process requires precise target delineation, which in clinical workflow for patients with multiple (>4) BMs (mBMs) could become a pronounced time bottleneck. Our group has developed an automated BMs segmentation platform to assist in this process. The accuracy of the auto-segmentation, however, is influenced by the presence of false-positive segmentations, mainly caused by the injected contrast during MRI acquisition. To address this problem and further improve the segmentation performance, a deep-learning and radiomics ensemble classifier was developed to reduce the false-positive rate in segmentations. The proposed model consists of a Siamese network and a radiomic-based support vector machine (SVM) classifier. The 2D-based Siamese network contains a pair of parallel feature extractors with shared weights followed by a single classifier. This architecture is designed to identify the inter-class difference. On the other hand, the SVM model takes the radiomic features extracted from 3D segmentation volumes as the input for twofold classification, either a false-positive segmentation or a true BM. Lastly, the outputs from both models create an ensemble to generate the final label. The performance of the proposed model in the segmented mBMs testing dataset reached the accuracy (ACC), sensitivity (SEN), specificity (SPE) and area under the curve of 0.91, 0.96, 0.90 and 0.93, respectively. After integrating the proposed model into the original segmentation platform, the average segmentation false negative rate (FNR) and the false positive over the union (FPoU) were 0.13 and 0.09, respectively, which preserved the initial FNR (0.07) and significantly improved the FPoU (0.55). The proposed method effectively reduced the false-positive rate in the BMs raw segmentations indicating that the integration of the proposed ensemble classifier into the BMs segmentation platform provides a beneficial tool for mBMs SRS management.

Dose kernel decomposition for spot-based radiotherapy treatment planning

PURPOSE

Pre-calculation of accurate dose deposition kernels for treatment planning of spot-based radiotherapies, such as Gamma Knife (GK) and Gamma Pod (GP), can be very time-consuming and may require large data storage with an enormous number of possible spots. We proposed a novel kernel decomposition (KD) model to address accurate and fast (real-time) dose calculation with reduced data storage requirements for spot-based treatment planning. The application of the KD model was demonstrated for clinical GK and GP radiotherapy platforms.

METHODS

The dose deposition kernel at each spot (shot position) is modeled as the product of a shift-invariant kernel based on a reference kernel and spatially variant scale factor. The reference kernel, one for each collimator, is defined at the center of the commissioning phantom for GK and at the center of the treatment target for GP and calculated using the Monte Carlo (MC) method. The spatially variant scale factor is defined as the ratio of the mean tissue maximum ratio (TMR) at the candidate shot position to that at the reference kernel position, and the mean TMR map is calculated within the entire volume through parallel beam ray tracing on the density image followed by averaging over all source directions. The proposed KD dose calculations were compared with the MC method and with the GK and GP treatment planning system (TPS) computations for various shot positions and collimator sizes utilizing a phantom and 14 and 12 clinical plans for GK and GP, respectively.

RESULTS

For the phantom study, the KD Gamma index (3%/1 mm) passing rates were greater than 99% (median 100%) relative to the MC doses, except for the shots close to the boundary. The passing rates dropped below 90% for 8 mm (16 mm) shots positioned within ∼1 cm (∼2 cm) of the boundary. For the clinical GK plans, the KD Gamma passing rates were greater than 99% (median 100%) compared to the MC and greater than 92% (median 99%) compared to the TPS. For the clinical GP plans, the KD Gamma passing rates were greater than 95% (median 98%) compared to the MC and greater than 91% (median 97%) compared to the TPS. The scale factors were calculated in sub-seconds with GPU implementation and only need to be calculated once before treatment plan optimization. The calculation of the dose kernel was also within sub-seconds without requiring beam-by-beam calculation commonly done in the TPS.

CONCLUSION

The proposed model can provide an accurate dose and enables real-time dose and derivative calculations by kernel shifting and scaling without pre-calculating or requiring large data storage for GK and GP dose deposition kernels during treatment planning. This model could be useful for spot-based radiotherapy treatment planning by allowing an efficient global fine search for optimal spots.

Expanded Radiosurgery Capabilities Utilizing Gamma Knife Icon™

The indications and techniques for the treatment of intracranial lesions continue to evolve with the advent of novel technologies. The Gamma Knife Icon™ (GK Icon™) is the most recent model available from Elekta, providing a frameless solution for stereotactic radiosurgery. At our institution, 382 patients with 3,213 separate intracranial lesions have been treated with frameless stereotactic radiotherapy using the GK Icon. The wide range of diagnoses include brain metastases, meningiomas, arteriovenous malformations, acoustic neuromas, pituitary adenomas, and several other histologies. The ability to perform both frame and frameless treatments on the GK Icon has significantly increased our daily volume by almost 50% on a single machine. Although the frameless approach allows one to take advantage of the precision in radiosurgery, the intricacies regarding treatment with this frameless system are not well established. Our initial experience will help to serve as a guide to those wishing to implement this novel technology in their practice.

A general algorithm for distributed treatments of multiple brain metastases

PURPOSE

Stereotactic radiosurgery (SRS) has become a primary treatment for multiple brain metastases (BM) but may require distribution of BMs over several sessions to make delivery time and radiation toxicity manageable. Contrasting to equal fraction dose in conventional fractionation, distributed SRS delivers full dose to a subset of BMs in each session while avoiding adjacent BMs in the same session to reduce toxicity from overlapping radiation. However, current clinical treatment planning for distributed SRS relies on manual BM assignment, which can be tedious and error prone. This work describes a novel approach to automate the distribution of BM in the Gamma Knife (GK) clinical workflow.

METHODS

We represent each BM as an electrostatic field of the same polarity that exerts repulsive forces on other BMs in the same session. This representation naturally leads to separation of close BMs into different sessions to lower the potential energy. Indeed, the BM distribution problem can be formulated as minimization of the total potential energy from all treatment sessions subject to delivery time constraints in mixed-integer quadratic programming (MIQP). We retrospectively studied eight clinical GK cases of multiple BM and compared the automated MIQP solution with clinically used BM distribution to demonstrate the efficacy of the proposed approach.

RESULTS

With the problem size equal to the number of BMs times the number of sessions, this MIQP can be solved in a minute on a personal workstation. The MIQP solution effectively separated BMs for a given number of treatment sessions and evened out the delivery time distribution among sessions. Compared to the clinically used manual BM distributions in paired t-test for a similar range of delivery time variation, the automated BM distributions had lower energy objectives (range of decrease: [11% 89%]; median: 25%; P = . 073 ), more uniformly distributed treatment volumes (range of decrease for the normalized standard deviation of volume distribution: [0.02 0.95]; median: 0.16; P = . 013 ), more scattered BMs in each treatment session (range of increase for the mean minimum BM distance: [0 14] mm; median: 6 mm; P = . 008 ), and lower overall V 12 (range of decrease: [0.0 1.6] cc; median: 0.2 cc; P = . 052 ). Moreover, without distribution, that is, with all BMs treated in the same session, V 12 was substantially larger compared to both manual and automated BM distributions; the increase ranged from 0.1 to 16.6 cc with a median of 1.3 cc.

CONCLUSIONS

The proposed approach models the clinical practice and provides an efficient solution for optimal selection of BM subsets for distributed SRS. Further evaluations are underway to establish this approach as a tool for improving clinical workflow and to facilitate systematic study on the benefits of distributed SRS treatments.

Dose rate determination for preclinical total body irradiation

The accuracy of delivered radiation dose and the reproducibility of employed radiotherapy methods are key factors for preclinical radiobiology applications and research studies. In this work, ionization chamber (IC) measurements and Monte Carlo (MC) simulations were used to accurately determine the dose rate for total body irradiation (TBI), a classic radiobiologic and immunologic experimental method. Several phantom configurations, including large solid water slab, small water box and rodentomorphic mouse and rat phantoms were simulated and measured for TBI setup utilizing a preclinical irradiator XRad320. The irradiator calibration and the phantom measurements were performed using an ADCL calibrated IC N31010 following the AAPM TG-61 protocol. The MC simulations were carried out using Geant4/GATE to compute absorbed dose distributions for all phantom configurations. All simulated and measured geometries had favorable agreement. On average, the relative dose rate difference was 2.3%. However, the study indicated large dose rate deviations, if calibration conditions are assumed for a given experimental setup as commonly done for a quick determination of irradiation times utilizing lookup tables and hand calculations. In a TBI setting, the reference calibration geometry at an extended source-to-surface distance and a large reference field size is likely to overestimate true photon scatter. Consequently, the measured and hand calculated dose rates, for TBI geometries in this study, had large discrepancies: 16% for a large solid water slab, 27% for a small water box, and 31%, 36%, and 30% for mouse phantom, rat phantom, and mouse phantom in a pie cage, respectively. Small changes in TBI experimental setup could result in large dose rate variations. MC simulations and the corresponding measurements specific to a designed experimental setup are vital for accurate preclinical dosimetry and reproducibility of radiobiological findings. This study supports the well-recognized need for physics consultation for all radiobiological investigations.

Photocatalytic degradation of methylene blue under natural sunlight using iron titanate nanoparticles prepared by a modified sol-gel method

The aim of this work was to synthesize semiconducting oxide nanoparticles using a simple method with low production cost to be applied in natural sunlight for photocatalytic degradation of pollutants in waste water. Iron titanate (Fe2TiO5) nanoparticles with an orthorhombic structure were successfully synthesized using a modified sol-gel method and calcination at 750°C. The as-prepared Fe2TiO5 nanoparticles exhibited a moderate specific surface area. The mesoporous Fe2TiO5 nanoparticles possessed strong absorption in the visible-light region and the band gap was estimated to be around 2.16 eV. The photocatalytic activity was evaluated by the degradation of methylene blue under natural sunlight. The effect of parameters such as the amount of catalyst, initial concentration of the dye and pH of the dye solution on the removal efficiency of methylene blue was investigated. Fe2TiO5 showed high degradation efficiency in a strong alkaline medium that can be the result of the facilitated formation of OH radicals due to an increased concentration of hydroxyl ions.

A web-based brain metastases segmentation and labeling platform for stereotactic radiosurgery

PURPOSE

Stereotactic radiosurgery (SRS) has become a standard of care for patients' with brain metastases (BMs). However, the manual multiple BMs delineation can be time-consuming and could create an efficiency bottleneck in SRS workflow. There is a clinical need for automatic delineation and quantitative evaluation tools. In this study, building on our previous developed deep learning-based segmentation algorithms, we developed a web-based automated BMs segmentation and labeling platform to assist the SRS clinical workflow.

METHOD

This platform was developed based on the Django framework, including a web client and a back-end server. The web client enables interactions as database access, data import, and image viewing. The server performs the segmentation and labeling tasks including: skull stripping; deep learning-based BMs segmentation; and affine registration-based BMs labeling. Additionally, the client can display BMs contours with corresponding atlas labels, and allows further postprocessing tasks including: (a) adjusting window levels; (b) displaying/hiding specific contours; (c) removing false-positive contours; (d) exporting contours as DICOM RTStruct files; etc. RESULTS: We evaluated this platform on 10 clinical cases with BMs number varied from 12-81 per case. The overall operation took about 4-5 min per patient. The segmentation accuracy was evaluated between the manual contour and automatic segmentation with several metrics. The averaged center of mass shift was 1.55 ± 0.36 mm, the Hausdorff distance was 2.98 ± 0.63 mm, the mean of surface-to-surface distance (SSD) was 1.06 ± 0.31 mm, and the standard deviation of SSD was 0.80 ± 0.16 mm. In addition, the initial averaged false-positive over union (FPoU) and false-negative rate (FNR) were 0.43 ± 0.19 and 0.15 ± 0.10 respectively. After case-specific postprocessing, the averaged FPoU and FNR were 0.19 ± 0.10 and 0.15 ± 0.10 respectively.

CONCLUSION

The evaluated web-based BMs segmentation and labeling platform can substantially improve the clinical efficiency compared to manual contouring. This platform can be a useful tool for assisting SRS treatment planning and treatment follow-up.

A Mail Audit Independent Peer Review System for Dosimetry Verification of a Small Animal Irradiator

Dedicated precision orthovoltage small animal irradiators have become widely available in the past decade and are commonly used for radiation biology research. However, there is a lack of dosimetric standardization among these irradiators, which affects the reproducibility of radiation-based animal studies. The purpose of this study was to develop a mail-based, independent peer review system to verify dose delivery among institutions using X-RAD 225Cx irradiators (Precision X-Ray, North Branford, CT). A robust, user-friendly mouse phantom was constructed from high-impact polystyrene and designed with dimensions similar to those of a typical laboratory mouse. The phantom accommodates three thermoluminescent dosimeters (TLDs) to measure dose. The mouse peer review system was commissioned in a small animal irradiator using anterior-posterior and posterior-anterior beams of 225 kVp and then mailed to three institutions to test the feasibility of the audit service. The energy correction factor for TLDs in the mouse phantom was derived to validate the delivered dose using this particular animal irradiation system. This feasibility study indicated that three institutions were able to deliver a radiation dose to the mouse phantom within ±10% of the target dose. The developed mail audit independent peer review system for the verification of mouse dosimetry can be expanded to characterize other commercially available orthovoltage irradiators, thereby enhancing the reproducibility of studies employing these irradiators.

Considerations of target surface area and the risk of radiosurgical toxicity

OBJECTIVE

The goal of this study was to explore conceptual benefits of characterizing delineated target volumes based on surface area and to utilize the concept for assessing risk of therapeutic toxicity in radiosurgery.

METHODS AND MATERIALS

Four computer-generated targets, a sphere, a cylinder, an ellipsoid and a box, were designed for two distinct scenarios. In the first scenario, all targets had identical volumes, and in the second one, all targets had identical surface areas. High quality stereotactic radiosurgery plans with at least 95% target coverage and selectivity were created for each target in both scenarios. Normal brain volumes V12Gy, V14Gy and V16Gy corresponding to received dose of 12 Gy, 14 Gy and 16 Gy, respectively, were computed and analyzed. Additionally, V12Gy and V14Gy volumes and values for seven prospective toxicity variables were recorded for 100 meningioma patients after Gamma Knife radiosurgery. Multivariable stepwise linear regression and best subset linear regression analyses were performed in two statistical software packages, SAS/STAT and R, respectively.

RESULTS

In a phantom study, for the constant volume targets, the volumes of 12 Gy, 14 Gy and 16 Gy isodose clouds were the lowest for the spherical target as an expected corollary of the isoperimetric inequality. For the constant surface area targets, a conventional wisdom is confirmed, as the target volume increases the corresponding volumes V12Gy, V14Gy and V16Gy also increase. In the 100-meningioma patient cohort, the best univariate model featured tumor surface area as the most significantly associated variable with both V12Gy and V14Gy volumes, corresponding to the adjusted R2 values of 0.82 and 0.77, respectively. Two statistical methods converged to matching multivariable models.

CONCLUSIONS

In a univariate model, target surface area is a better predictor of spilled dose to normal tissue than target largest dimension or target volume itself. In complex multivariate models, target surface area is an independent variable for modeling radiosurgical normal tissue toxicity risk.

Modeling Elekta VersaHD using the Varian Eclipse treatment planning system for photon beams: A single-institution experience

The aim of this study was to report a single‐institution experience and commissioning data for Elekta VersaHD linear accelerators (LINACs) for photon beams in the Eclipse treatment planning system (TPS). Two VersaHD LINACs equipped with 160‐leaf collimators were commissioned. For each energy, the percent‐depth‐dose (PDD) curves, beam profiles, output factors, leaf transmission factors and dosimetric leaf gaps (DLGs) were acquired in accordance with the AAPM task group reports No. 45 and No. 106 and the vendor‐supplied documents. The measured data were imported into Eclipse TPS to build a VersaHD beam model. The model was validated by creating treatment plans spanning over the full‐spectrum of treatment sites and techniques used in our clinic. The quality assurance measurements were performed using MatriXX, ionization chamber, and radiochromic film. The DLG values were iteratively adjusted to optimize the agreement between planned and measured doses. Mobius, an independent LINAC logfile‐based quality assurance tool, was also commissioned both for routine intensity‐modulated radiation therapy (IMRT) QA and as a secondary check for the Eclipse VersaHD model. The Eclipse‐generated VersaHD model was in excellent agreement with the measured PDD curves and beam profiles. The measured leaf transmission factors were less than 0.5% for all energies. The model validation study yielded absolute point dose agreement between ionization chamber measurements and Eclipse within ±4% for all cases. The comparison between Mobius and Eclipse, and between Mobius and ionization chamber measurements lead to absolute point dose agreement within ±5%. The corresponding 3D dose distributions evaluated with 3%global/2mm gamma criteria resulted in larger than 90% passing rates for all plans. The Eclipse TPS can model VersaHD LINACs with clinically acceptable accuracy. The model validation study and comparisons with Mobius demonstrated that the modeling of VersaHD in Eclipse necessitates further improvement to provide dosimetric accuracy on par with Varian LINACs.

Improved Survival Outcomes for Kidney Cancer Patients With Brain Metastases

BACKGROUND

Brain metastases (BM) occur frequently in patients with metastatic kidney cancer and are a significant source of morbidity and mortality. Although historically associated with a poor prognosis, survival outcomes for patients in the modern era are incompletely characterized. In particular, outcomes after adjusting for systemic therapy administration and International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) risk factors are not well-known.

PATIENTS AND METHODS

A retrospective database of patients with metastatic renal cell carcinoma (RCC) treated at University of Texas Southwestern Medical Center between 2006 and 2015 was created. Data relevant to their diagnosis, treatment course, and outcomes were systematically collected. Survival was analyzed by the Kaplan-Meier method. Patients with BM were compared with patients without BM after adjusting for the timing of BM diagnosis, either prior to or during first-line systemic therapy. The impact of stratification according to IMDC risk group was assessed.

RESULTS

A total of 56 (28.4%) of 268 patients with metastatic RCC were diagnosed with BM prior to or during first-line systemic therapy. Median overall survival (OS) for systemic therapy-naive patients with BM compared with matched patients without BM was 19.5 versus 28.7 months (P = .0117). When analyzed according to IMDC risk group, the median OS for patients with BM was similar for favorable- and intermediate-risk patients (not reached vs. not reached; and 29.0 vs. 36.7 months; P = .5254), and inferior for poor-risk patients (3.5 vs. 9.4 months; P = .0462). For patients developing BM while on first-line systemic therapy, survival from the time of progression did not significantly differ by presence or absence of BM (11.8 vs. 17.8 months; P = .6658).

CONCLUSIONS

Survival rates for patients with BM are significantly better than historical reports. After adjusting for systemic therapy, the survival rates of patients with BM in favorable- and intermediate-risk groups were remarkably better than expected and not statistically different from patients without BM, though this represents a single institution experience, and numbers are modest.

Stereotactic Radiosurgery for Multiple Brain Metastases From Renal-Cell Carcinoma

BACKGROUND

Brain metastases (BM) pose a significant problem in patients with metastatic renal-cell carcinoma (mRCC). Local and systemic therapies including stereotactic radiosurgery (SRS) are rapidly evolving, necessitating reassessments of outcomes for modern patient management.

PATIENTS AND METHODS

The mRCC patients with BM treated with SRS were reviewed. Patient demographics, clinical history, and SRS treatment parameters were identified.

RESULTS

Among 268 patients with mRCC treated between 2006 and 2015, 38 patients were identified with BM. A total of 243 BM were treated with SRS with 1 to 26 BMs treated per SRS session (median, 2 BMs). The median (range) BM size was 0.6 (0.2-3.1) cm and median (range) SRS treatment dose was 18 (12-24) Gy. Treated BM local control rates at 1 and 2 years were 91.8% (95% confidence interval, 85.7-95.4) and 86.1% (95% confidence interval, 77.1-91.7), respectively. BM control declined for larger tumors. Survival after 1-year was 57.5% (95% CI 40.2-71.4) for all patients. Survival was not statistically different between patients with < 5 BM versus ≥ 5 BM. Survival was prognostic based on International Metastatic Renal Cell Carcinoma Database (IMDC) risk groups in patients with < 5 BM. Two patients experienced grade 3 radiation necrosis requiring surgical intervention.

CONCLUSION

SRS is effective in controlling BM in patients with mRCC. Over half of treated patients survive past a year, and no differences in survival were noted in patients with > 5 metastases. Prognostic risk categories based on systemic disease (IMDC) are predictive of survival in this BM population, with limited rates of symptomatic radiation necrosis.

Tumor physiological changes during hypofractionated stereotactic body radiation therapy assessed using multi-parametric magnetic resonance imaging

Radiation therapy is a primary treatment for non-resectable lung cancer and hypoxia is thought to influence tumor response. Hypoxia is expected to be particularly relevant to the evolving new radiation treatment scheme of hypofractionated stereotactic body radiation therapy (SBRT). As such, we sought to develop non-invasive tools to assess tumor pathophysiology and response to irradiation. We applied blood oxygen level dependent (BOLD) and tissue oxygen level dependent (TOLD) MRI, together with dynamic contrast enhanced (DCE) MRI to explore the longitudinal effects of SBRT on tumor oxygenation and vascular perfusion using A549 human lung cancer xenografts in a subcutaneous rat model. Intra-tumor heterogeneity was seen on multi-parametric maps, especially in BOLD, T2* and DCE. At baseline, most tumors showed a positive BOLD signal response (%ΔSI) and increased T2* in response to oxygen breathing challenge, indicating increased vascular oxygenation. Control tumors showed similar response 24 hours and 1 week later. Twenty-four hours after a single dose of 12 Gy, the irradiated tumors showed a significantly decreased T2* (-2.9±4.2 ms) and further decrease was observed (-4.0±6.0 ms) after 1 week, suggesting impaired vascular oxygenation. DCE revealed tumor heterogeneity, but showed minimal changes following irradiation. Rats were cured of the primary tumors by 3x12 Gy, providing long term survival, though with ultimate metastatic recurrence.

A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery

Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.

Technical Note: System for evaluating local hypothermia as a radioprotector of the rectum in a small animal model

PURPOSE

The protective effects of induced or even accidental hypothermia on the human body are widespread with several medical uses currently under active research. In vitro experiments using human cell lines have shown hypothermia provides a radioprotective effect that becomes more pronounced at large, single-fraction doses common to stereotactic body radiotherapy (SBRT) and stereotactic radiosurgery (SRS) treatments. This work describes the development of a system to evaluate local hypothermia for a radioprotective effect of the rat rectum during a large dose of radiation relevant to prostate SBRT. This includes the evaluation of a 3D-printed small animal rectal cooling device and the integration with a small animal irradiator.

METHODS

A 3-cm long, dual-lumen rectal temperature control apparatus (RTCA) was designed in SOLIDWORKS CAD for 3D printing. The RTCA was capable of recirculating flow in a device small enough for insertion into the rat rectum, with a metal support rod for strength as well as visibility during radiation treatment planning. The outer walls of the RTCA comprised of thin heat shrink plastic, achieving efficient heat transfer into adjacent tissues. Following leak-proof testing, fiber optic temperature probes were used to evaluate the temperature over time when placed adjacent to the cooling device within the rat rectum. MRI thermometry characterized the relative temperature distribution in concentric ROIs surrounding the probe. Integration with an image-guided small animal irradiator and associated treatment planning system included evaluation for imaging artifacts and effect of brass tubing on dose calculation.

RESULTS

The rectal temperature adjacent to the cooling device decreased from body temperature to 15°C within 10-20 min from device insertion and was maintained at 15 ± 3°C during active cooling for the evaluated time of one hour. MR thermometry revealed a steep temperature gradient with increasing distance from the cooling device with the desired temperature range maintained within the surrounding few millimeters.

CONCLUSIONS

A 3D-printed rectal cooling device was fabricated for the purpose of inducing local hypothermia in the rat rectum. The RTCA was simply integrated with an image-guided small animal irradiator and Monte Carlo-based treatment planning system to facilitate an in vivo investigation of the radioprotective effect of hypothermia for late rectal toxicity following a single large dose of radiation.