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Single ventricle patients typically undergo some form of advanced diagnostic imaging prior to superior cavopulmonary connection (SCPC). We sought to evaluate variability of diagnostic practice and associated comprehensive risk. A retrospective evaluation across 4 institutions was performed (1/1/2010–9/30/2016) comparing the primary modalities of cardiac catheterization (CC), cardiac magnetic resonance (CMR), and cardiac computed tomography (CT). Associated risks included anesthesia/sedation, vascular access, total room time, contrast agent usage, radiation exposure, and adverse events (AEs). Of 617 patients undergoing SCPC, 409 (66%) underwent at least one advanced diagnostic imaging study in the 60 days prior to surgery. Seventy-eight of these patients (13%) were analyzed separately because of a concomitant cardiac intervention during CC. Of 331 (54%) with advanced imaging and without catheterization intervention, diagnostic CC was most common (59%), followed by CT (27%) and CMR (14%). Primary modality varied significantly by institution (p < 0.001). Median time between imaging and SCPC was 13 days (IQR 3–33). Anesthesia/sedation varied significantly (p < 0.001). Pre-procedural vascular access did not vary significantly across modalities (p = 0.111); procedural access varied between CMR/CT and CC, in which central access was used in all procedures. Effective radiation dose was significantly higher for CC than CT (p < 0.001). AE rate varied significantly, with 12% CC, 6% CMR, and 1% CT (p = 0.004). There is significant practice variability in the use of advanced diagnostic imaging prior to SCPC, with important differences in associated procedural risk. Future studies to identify differences in diagnostic accuracy and long-term outcomes are warranted to optimize diagnostic protocols. [ABSTRACT FROM AUTHOR]

Purpose/objective(s): A biologically guided radiotherapy (BgRT) system was developed and equipped with a novel O-ring gantry Linac, positron emission tomography (PET) and computed tomography technologies. This architectural design is to support real-time PET-guided treatment, termed biology-guided radiotherapy. To reduce latency between PET data acquisition and radiation delivery, the BgRT system incorporates a fast-transitioning binary multi-leaf collimator (MLC) leaves sandwiched between a split-jaw. This study reports the dosimetric commissioning of the first clinical BgRT machines at our institution, and compared with the reference data provided by the manufacturer.Materials/methods: The dosimetric commissioning of the 6 MV flattening filter-free (FFF) photon beam was characterized using a 3D water phantom and diode detectors at a source to surface distance of 85 cm. In-air profiles of 40cm x 2cm field, 40cm x 1cm field, and various leaf combinations plus in water PDD data of 12 field sizes (1.25, 2.5, 5, 10, 20 and 40 cm MLC opened leaves with 1 and 2cm jaw settings) were acquired first for beam modeling. Additional longitudinal and transverse profile scans were performed for these 12 field sizes at the depth of 1.5, 5, 10, 15 and 20 cm to verify the beam model accuracy. The relative outputs factors were measured with the W2 1mmx1mm scintillator at the depth of 10cm for the field sizes from the smallest 0.6cm x 1cm to the maximum 40cm x 2cm.Results: For the PDDs of reference field size of 10cm x 2cm measured at Stanford, the maximum doses (dmax) were 12.5mm within the manufacturer's reference range of 13 ± 0.75 mm. The percentage doses at 10cm depth (d10) at reference field size were 56.9% and 57.6% for Stanford and RefleXion measurements, respectively. The longitudinal field sizes for all tested fields were agreed within 0.5mm and the transverse off-axis ratio is within 1% in the beam core (80% of nominal field size). The output factors measured at our institute were ranging from 0.706 to 1.012 relative to the reference 10cm x 2cm field.Conclusion: This evaluation represents the first commissioning evaluation of the dosimetric characteristics of the BgRT system with architecture designed to accommodate both PET detector and LINAC subsystems. This data will serve as a clinical reference for the BgRT treatment planning system's beam model and can act as a standard for comparison and beam model improvement for units installed at future clinical sites. [ABSTRACT FROM AUTHOR]

Purpose/objective(s): To evaluate Biology-guided Radiotherapy (BgRT) plans generated using PET data acquired on the first installation of the Reflexion™ X1 system. BgRT treatment planning optimizes for fluence indirectly by calculating firing filters that best translate the X1 acquired PET image to the fluences that deliver the prescribed radiation dose. During a treatment session, the firing filters are applied to limited time sampled (LTS) PET images to calculate delivery fluences.Materials/methods: An FDG fillable phantom insert that consists of a 22 mm diameter spherical target (PTV1, ball) and a C-shape object (PTV2 with 26mm axial length, 255° partial annulus with major and minor radii of 27 mm and 12 mm) was imaged using a CT Simulator to create planning contours. The water filled insert surrounding these objects was filled with FDG to simulate a warm background. Both targets were filled with FDG, simulating a target to background ratio of 8:1. Two different BgRT treatment plans were generated for PTV1 simulating homogenous uptake, and PTV2 with an inhomogeneous FDG uptake due to necrosis (a cylinder encompassing the C shape PET avid object). A structure that is 12.5 mm away from these targets was used to simulate OAR. Two PET/CT imaging acquisitions were performed on the RefleXion X1 masking the signal outside of each target plus 10 mm margin. Two different BgRT plans were generated prescribing 50 Gy delivered in 5 fractions to each target, separately. The quality of each treatment plan was evaluated based on PTV coverage, Conformity Index at 50% and 80% isodose lines (CI50, CI80) and OAR sparing. Both BgRT plans then delivered to the independent quality assurance tool for patient specific QA verification where the 3%/3 mm gamma criteria was used.Results: BgRT plans for both homogenous and heterogenous FDG uptake targets were created successfully. Prescription dose coverages were 94.2% and 96.3% for PTV1 and PTV2, respectively. The maximum OAR doses were 30.9 and 33.0 Gy and average OAR doses were 14.4 and 20.1 Gy for PTV1 and PTV2 plans, respectively. CI50 were 7.7 and 6.8 where CI80 were 2.5 and 2.6 for PTV1 and PTV2 plans. The patient specific QA passed with 100 % for both plans.Conclusion: This is the first report of the BgRT plan creation and QA delivery using a clinical installation of the Reflexion X1 system, opening the potential towards BgRT dose delivery using the real time FDG uptake measurements to biologically guide active radiation beam delivery. The X1 system was able to optimize and deliver the BgRT plans for both homogenous and heterogenous FDG uptake scenarios. BgRT is a new paradigm of dose optimization, where a new set of optimization and evaluation approaches will be needed. [ABSTRACT FROM AUTHOR]

Biology-guided radiotherapy (BgRT) is a novel tracked dose delivery modality using real-time positron emission tomography (PET) to guide radiotherapy beamlets. The present study was performed with sequential cohorts of participants to evaluate the performance and safety of BgRT. Primary endpoints were previously reported. We hereby report on one of the secondary endpoints assessing a novel treatment planning machine with integrated dual kVCT/PET imaging ("novel device") performance in comparison to a third-party diagnostic PET/CT scan. This single-arm, open-label, prospective study included participants with at least 1 FDG-avid targetable primary or metastatic tumor (≥2cm and ≤5cm) in the lung or bone. PET imaging data were collected on the novel device and on a third-party diagnostic PET/CT performed in sequence once at the planning timepoint in Cohort I, and immediately before the last fraction among patients undergoing stereotactic radiotherapy in Cohort II. Three central read radiation oncologists (CRRO) provided an interpretation of the novel device PET scans which were compared to an agreement standard based on 3 central radiologists' review of the paired diagnostic PET/CT scan. Positive percent agreement for localization of the target tumor within the biology-tracking zone (BTZ) was the key metric because it reflects whether advancing patients to subsequent steps in the BgRT workflow based on the novel device's imaging was ultimately appropriate. In Cohort 1, 6 image comparisons were performed. The positive (%) agreement for the aggregate radiation oncologist review was 100% (5/5), reflecting that in all 5 cases where the aggregate radiation oncologists deemed the tumor to fall within the BTZ based upon the novel device PET images, the central radiologists came to the same conclusion upon review of the paired diagnostic PET/CT images. The overall (%) agreement for the aggregate radiation oncologist review was 83.3% (5/6): localization was not established on the novel device in 1 case, even though it was established on the diagnostic PET/CT. This would not pose risk in real world practice as BgRT candidacy would be aborted for tumors not visible on the novel device. In Cohort II, among the 7 image comparisons, there was 100% positive percent agreement between the aggregate CRRO and the agreement standard as the localization criteria was met in both scans for all 7 patients. This was concordant with a 100% overall percent agreement. This investigation demonstrated a 100% positive percent agreement between central review of this novel device images by radiation oncologists and central review of the accompanying third-party PET/CT images by radiologists. There were no cases where a positive localization by the aggregate CRRO was not confirmed by the third-party PET/CT standard, providing evidence against the likelihood of falsely positive localizations on the novel device that would inappropriately advance patients in the workflow. [ABSTRACT FROM AUTHOR]

Contouring the target and organs at risk (OARs) of the treated patient in each fraction is a key step of daily adaptive radiotherapy. We designed a patient-specific deep learning study to improve the auto-segmentation performance for adaptive prostate radiotherapy, by leveraging the daily image-guidance fan-beam CT images of the specific patient available in the current IGRT workflow. Our institution has set up a newly developed biology-guided radiotherapy (BgRT) system enabling high-quality fan-beam CT imaging for daily treatments. The data of the first prostate patient treated with this system including 26 daily fan-beam CT scans were utilized in this study. A population network was first trained for the auto-segmentation of the prostate and seven pelvic OARs, based on a population dataset containing 56 different patient cases treated on the regular Linac. The pre-trained population network was intentionally fine-tuned to adapt to this specific patient with a transfer learning method. The selected OARs of this patient were contoured by two radiation oncologists on the initial treatment planning and all sets of daily fan-beam CT images as the ground truth for the patient-specific learning. Three different fine-tuning methods, i.e., fine-tuning the last convolution layer, the last convolution block and the whole network were analyzed. A longitudinal study was conducted by exploring the relationship between the auto-segmentation performance and the number of the sequential prior data used for learning. The performance of the patient-specific network was compared with the population network as well as the clinical rigid registration method to highlight its efficiency in radiotherapy practice. The Dice similarity coefficient (DSC) results of the proposed patient-specific network were 0.882, 0.934, 0.864, 0.827, 0.952, 0.954, 0.921 and 0.904 for the prostate, bladder, rectum, seminal vesicle, left and right femoral head, bowel and lymph nodes, respectively, outperforming the population network (mean DSC of 0.627) and the clinical registration method (mean DSC of 0.716). The whole network fine-tuning method outperformed the other two fine-tuning methods, showing it is more effective in this study. The contouring accuracy of the patient-specific network gradually increased with the increment of longitudinal training cases and approached saturation with more than four training cases. This study proposes an accurate auto-segmentation method based on the patient-specific deep learning for adaptive prostate radiotherapy. By utilizing the image and contour similarity between the treatment fractions, the patient-specific auto-segmentation could outperform the common population network and the clinical registration method by a large margin, and thus is promising to facilitate more precise radiotherapy based on the new BgRT system. [ABSTRACT FROM AUTHOR]