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1. High‐Definition Zoom Mode: A High Resolution X‐ray Microscope for Neurointerventional Treatment Procedures

   https://doi.org/10.1111/jon.12652  

   Setlur Nagesh, Swetadri Vasan ; Vakharia, Kunal ; Waqas, Muhammad ; Fennell, Vernard S. ; Atwal, Gursant S. ; Shallwani, Hussain ; Bednarek, Daniel R. ; Davies, Jason M. ; Snyder, Kenneth V. ; Mokin, Maxim ; et al ( July 2019 , Journal of Neuroimaging)

   Visualization of structural details of treatment devices during neurointerventional procedures can be challenging. A new true two‐resolution imaging X‐ray detector system features a 194 µm pixel conventional flat‐panel detector (FPD) mode and a 76 µm pixel high‐resolution high‐definition (Hi‐Def) zoom mode in one detector panel. The Hi‐Def zoom mode was developed for use in interventional procedures requiring superior image quality over a small field of view (FOV). We report successful use of this imaging system during intracranial aneurysm treatment in 1 patient with a Pipeline‐embolization device and 1 patient with a low‐profile visualized intramural support (LVIS Blue) device plus adjunctive coiling.

   A guide catheter was advanced from the femoral artery insertion site to the proximity of each lesion using standard FPD mode. Under magnified small FOV Hi‐Def imaging mode, an intermediate catheter and microcatheters were guided to the treatment site, and the PED and LVIS Blue plus coils were deployed. Radiation doses were tracked intraprocedurally.

   Critical details, including structural changes in the PED and LVIS Blue and position and movement of the microcatheter tip within the coil mass, were more readily apparent in Hi‐Def mode. Skin‐dose mapping indicated that Hi‐Def mode limited radiation exposure to the smaller FOV of the treatment area.

   Visualization of device structures was much improved in the high‐resolution Hi‐Def mode, leading to easier, more controlled deployment of stents and coils than conventional FPD mode.

    

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2. Estimating Individualized Treatment Rules for Ordinal Treatments

   https://doi.org/10.1111/biom.12865  

   Chen, Jingxiang ; Fu, Haoda ; He, Xuanyao ; Kosorok, Michael R. ; Liu, Yufeng ( March 2018 , Biometrics)

   Precision medicine is an emerging scientific topic for disease treatment and prevention that takes into account individual patient characteristics. It is an important direction for clinical research, and many statistical methods have been proposed recently. One of the primary goals of precision medicine is to obtain an optimal individual treatment rule (ITR), which can help make decisions on treatment selection according to each patient's specific characteristics. Recently, outcome weighted learning (OWL) has been proposed to estimate such an optimal ITR in a binary treatment setting by maximizing the expected clinical outcome. However, for ordinal treatment settings, such as individualized dose finding, it is unclear how to use OWL. In this article, we propose a new technique for estimating ITR with ordinal treatments. In particular, we propose a data duplication technique with a piecewise convex loss function. We establish Fisher consistency for the resulting estimated ITR under certain conditions, and obtain the convergence and risk bound properties. Simulated examples and an application to a dataset from a type 2 diabetes mellitus observational study demonstrate the highly competitive performance of the proposed method compared to existing alternatives.

    

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3. Age‐dependent dose calculations for common PET radionuclides and brain radiotracers in nonhuman primate computational models

   https://doi.org/10.1002/mp.14333  

   Xie, Tianwu ; Chen, Xin ; Zaidi, Habib ( July 2020 , Medical Physics)

   The combination of nonhuman primates (NHPs) with the state‐of‐the‐art molecular imaging technologies allows for within‐subject longitudinal research aiming at gaining new insights into human normal and disease conditions and provides an ideal foundation for future translational studies of new diagnostic tools, medical interventions, and therapies. However, radiation dose estimations for nonhuman primates from molecular imaging probes are lacking and are difficult to perform experimentally. The aim of this work is to construct age‐dependent NHP computational model series to estimate the absorbed dose to NHP specimens in common molecular imaging procedures.

   A series of NHP models from baby to adult were constructed based on nonuniform rational B‐spline surface (NURBS) representations. Particle transport was simulated using Monte Carlo calculations to estimate S‐values from nine positron‐emitting radionuclides and absorbed doses from PET radiotracers.

   Realistic age‐dependent NHP computational model series were developed. For most source‐target pairs in computational NHP models, differences between C‐11 S‐values were between −13.4% and −8.8%/kg difference in body weight while differences between F‐18 S‐values were between −12.9% and −8.0%/kg difference in body weight. The absorbed doses of¹¹C‐labeled brain receptor substances,¹⁸F‐labeled brain receptor substances, and¹⁸F‐FDG in the brain ranged within 0.047–0.32 mGy/MBq, 0.25–1.63 mGy/MBq, and 0.32–2.12 mGy/MBq, respectively.

   The absorbed doses to organs are significantly higher in the baby NHP model than in the adult model. These results can be used in translational longitudinal studies to estimate the cumulated absorbed organ doses in NHPs at various ages.

    

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4. The dosimetric enhancement of GRID profiles using an external collimator in pencil beam scanning proton therapy

   https://doi.org/10.1002/mp.15523  

   Smith, Blake R. ; Nelson, Nicholas P. ; Geoghegan, Theodore J. ; Patwardhan, Kaustubh A. ; Hill, Patrick M. ; Yu, Jen ; Gutiérrez, Alonso N. ; Allen, Bryan G. ; Hyer, Daniel E. ( February 2022 , Medical Physics)

   The radiobiological benefits afforded by spatially fractionated (GRID) radiation therapy pairs well with the dosimetric advantages of proton therapy. Inspired by the emergence of energy‐layer specific collimators in pencil beam scanning (PBS), this work investigates how the spot spacing and collimation can be optimized to maximize the therapeutic gains of a GRID treatment while demonstrating the integration of a dynamic collimation system (DCS) within a commercial beamline to deliver GRID treatments and experimentally benchmark Monte Carlo calculation methods.

   GRID profiles were experimentally benchmarked using a clinical DCS prototype that was mounted to the nozzle of the IBA‐dedicated nozzle system. Integral depth dose (IDD) curves and lateral profiles were measured for uncollimated and GRID‐collimated beamlets. A library of collimated GRID dose distributions were simulated by placing beamlets within a specified uniform grid and weighting the beamlets to achieve a volume‐averaged tumor cell survival equivalent to an open field delivery. The healthy tissue sparing afforded by the GRID distribution was then estimated across a range of spot spacings and collimation widths, which were later optimized based on the radiosensitivity of the tumor cell line and the nominal spot size of the PBS system. This was accomplished by using validated models of the IBA universal and dedicated nozzles.

   Excellent agreement was observed between the measured and simulated profiles. The IDDs matched above 98.7% when analyzed using a 1%/1‐mm gamma criterion with some minor deviation observed near the Bragg peak for higher beamlet energies. Lateral profile distributions predicted using Monte Carlo methods agreed well with the measured profiles; a gamma passing rate of 95% or higher was observed for all in‐depth profiles examined using a 3%/2‐mm criteria. Additional collimation was shown to improve PBS GRID treatments by sharpening the lateral penumbra of the beamlets but creates a trade‐off between enhancing the valley‐to‐peak ratio of the GRID delivery and the dose‐volume effect. The optimal collimation width and spot spacing changed as a function of the tumor cell radiosensitivity, dose, and spot size. In general, a spot spacing below 2.0 cm with a collimation less than 1.0 cm provided a superior dose distribution among the specific cases studied.

   The ability to customize a GRID dose distribution using different collimation sizes and spot spacings is a useful advantage, especially to maximize the overall therapeutic benefit. In this regard, the capabilities of the DCS, and perhaps alternative dynamic collimators, can be used to enhance GRID treatments. Physical dose models calculated using Monte Carlo methods were experimentally benchmarked in water and were found to accurately predict the respective dose distributions of uncollimated and DCS‐collimated GRID profiles.

    

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5. Investigation of autosegmentation techniques on T2‐weighted MRI for off‐line dose reconstruction in MR‐linac workflow for head and neck cancers

   https://doi.org/10.1002/mp.16582  

   McDonald, Brigid A. ; Cardenas, Carlos E. ; O'Connell, Nicolette ; Ahmed, Sara ; Naser, Mohamed A. ; Wahid, Kareem A. ; Xu, Jiaofeng ; Thill, Dan ; Zuhour, Raed J. ; Mesko, Shane ; et al ( July 2023 , Medical Physics)

   In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)‐linear accelerator (MR‐linac), the low‐resolution T2‐weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction.

   In this pilot study, we evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on‐board setup MRIs from the MR‐linac for off‐line reconstruction of delivered dose.

   Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. Twenty total autosegmentation methods were evaluated in ADMIRE: 1–9) atlas‐based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10–19) autosegmentation using images from a patient's 1–4 prior fractions (individualized patient prior [IPP]) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance (MSD), Hausdorff distance (HD), and Jaccard index (JI). For each metric and OAR, performance was compared to the inter‐observer variability using Dunn's test with control. Methods were compared pairwise using the Steel‐Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high‐performing autosegmentation methods (DL, IPP with RF and 4 fractions [IPP_RF_4], IPP with 1 fraction [IPP_1]), and one low‐performing (PAL with STAPLE and 5 atlases [PAL_ST_5]). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics.

   DL and IPP methods performed best overall, all significantly outperforming inter‐observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter‐observer variability or from each other. DL was the fastest method (33 s per case) and PAL methods the slowest (3.7–13.8 min per case). Execution time increased with a number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within ± 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314).

   The autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on‐board T2‐weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end‐to‐end dose accumulation workflow.

    

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