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Mendelian disorders are prevalent in neonatal and pediatric intensive care units and are a leading cause of morbidity and mortality in these settings. Current diagnostic pipelines that integrate phenotypic and genotypic data are expert-dependent and time-intensive. Artificial intelligence (AI) tools may help address these challenges. Dx29 is an open-source AI tool designed for use by clinicians. It analyzes the patient’s phenotype and genotype to generate a ranked differential diagnosis. We used Dx29 to retrospectively analyze 25 acutely ill infants who had been diagnosed with a Mendelian disorder, using a targeted panel of ~5000 genes. For each case, a trio (proband and both parents) file containing gene variant information was analyzed, alongside patient phenotype, which was provided to Dx29 by three approaches: (1) AI extraction from medical records, (2) AI extraction with manual review/editing, and (3) manual entry. We then identified the rank of the correct diagnosis in Dx29’s differential diagnosis. With these three approaches, Dx29 ranked the correct diagnosis in the top 10 in 92–96% of cases. These results suggest that non-expert use of Dx29’s automated phenotyping and subsequent data analysis may compare favorably to standard workflows utilized by bioinformatics experts to analyze genomic data and diagnose Mendelian diseases.

Human Phenotype Ontology (HPO) terms are increasingly used in diagnostic settings to aid in the characterization of patient phenotypes. The HPO annotation database is updated frequently and can provide detailed phenotype knowledge on various human diseases, and many HPO terms are now mapped to candidate causal genes with binary relationships. To further improve the genetic diagnosis of rare diseases, we incorporated these HPO annotations, gene-disease databases, and gene-gene databases in a probabilistic model to build a novel HPO-driven gene prioritization tool, Phen2Gene. Phen2Gene accesses a database built upon this information called the HPO2Gene Knowledgebase (H2GKB), which provides weighted and ranked gene lists for every HPO term. Phen2Gene is then able to access the H2GKB for patient-specific lists of HPO terms or PhenoPackets descriptions supported by GA4GH (http://phenopackets.org/), calculate a prioritized gene list based on a probabilistic model, and output gene-disease relationships with great accuracy. Phen2Gene outperforms existing gene prioritization tools in speed, and acts as a real-time phenotype driven gene prioritization tool to aid the clinical diagnosis of rare undiagnosed diseases. In addition to a command line tool released under the MIT license (https://github.com/WGLab/Phen2Gene), we also developed a web server and web service (https://phen2gene.wglab.org/) for running the tool via web interface or RESTful API queries. Finally, we have curated a large amount of benchmarking data for phenotype-to-gene tools involving 197 patients across 76 scientific articles and 85 patients’ de-identified HPO term data from CHOP.

Radiation therapy treatments are typically planned based on a single image set, assuming that the patient’s anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.

The purpose of this study was to investigate internal tumor volume density overwrite strategies to minimize intensity modulated proton therapy (IMPT) plan degradation of mobile lung tumors. Four planning paradigms were compared for nine lung cancer patients. Internal gross tumor volume (IGTV) and internal clinical target volume (ICTV) structures were defined encompassing their respective volumes in every 4DCT phase. The paradigms use different planning CT (pCT) created from the average intensity projection (AIP) of the 4DCT, overwriting the density within the IGTV to account for movement. The density overwrites were: (a) constant filling with 100 HU (C100) or (b) 50 HU (C50), (c) maximum intensity projection (MIP) across phases, and (d) water equivalent path length (WEPL) consideration from beam's-eye-view. Plans were created optimizing dose-influence matrices calculated with fast GPU Monte Carlo (MC) simulations in each pCT. Plans were evaluated with MC on the 4DCTs using a model of the beam delivery time structure. Dose accumulation was performed using deformable image registration. Interplay effect was addressed applying 10 times rescanning. Significantly less DVH metrics degradation occurred when using MIP and WEPL approaches. Target coverage ([Formula: see text] Gy(RBE)) was fulfilled in most cases with MIP and WEPL ([Formula: see text] Gy (RBE)), keeping dose heterogeneity low ([Formula: see text] Gy(RBE)). The mean lung dose was kept lowest by the WEPL strategy, as well as the maximum dose to organs at risk (OARs). The impact on dose levels in the heart, spinal cord and esophagus were patient specific. Overall, the WEPL strategy gives the best performance and should be preferred when using a 3D static geometry for lung cancer IMPT treatment planning. Newly available fast MC methods make it possible to handle long simulations based on 4D data sets to perform studies with high accuracy and efficiency, even prior to individual treatment planning.

To develop an online plan adaptation algorithm for intensity modulated proton therapy (IMPT) based on fast Monte Carlo dose calculation and cone beam CT (CBCT) imaging. A cohort of ten head and neck cancer patients with an average of six CBCT scans were studied. To adapt the treatment plan to the new patient geometry, contours were propagated to the CBCTs with a vector field (VF) calculated with deformable image registration between the CT and the CBCTs. Within the adaptive planning algorithm, beamlets were shifted following the VF at their distal falloff and raytraced in the CBCT to adjust their energies, creating a geometrically adapted plan. Four geometric adaptation modes were studied: unconstrained geometric shifts (Free), isocenter shift (Iso), a range shifter (RS), or isocenter shift and range shifter (Iso-RS). After evaluation of the geometrical adaptation, the weights of a selected subset of beamlets were automatically tuned using MC-generated influence matrices to fulfill the original plan requirements. All beamlet calculations were done with a fast Monte Carlo running on a GPU (graphics processing unit). Geometrical adaptation alone only worked with small anatomy changes. The weight-tuned adaptation worked for every fraction, with the Free and Iso modes performing similarly and being superior than the two range shifters modes. The mean V95 and V107 were 99.4 ± 0.9 and 6.4% ± 4.7% in the Free mode with weight tuning. The calculation time per fraction was ~5 min, but further task parallelization could reduce it to ~1-2 min for delivery adaptation right after patient setup. An online adaptation algorithm was developed that significantly improved the treatment quality for inter-fractional geometry changes. Clinical implementation of the algorithm would allow delivery adaptation right before treatment and thus allow planning margin reductions for IMPT.

Monte Carlo (MC) simulation is commonly considered as the most accurate dose calculation method for proton therapy. Aiming at achieving fast MC dose calculations for clinical applications, we have previously developed a graphics-processing unit (GPU)-based MC tool, gPMC. In this paper, we report our recent updates on gPMC in terms of its accuracy, portability, and functionality, as well as comprehensive tests on this tool. The new version, gPMC v2.0, was developed under the OpenCL environment to enable portability across different computational platforms. Physics models of nuclear interactions were refined to improve calculation accuracy. Scoring functions of gPMC were expanded to enable tallying particle fluence, dose deposited by different particle types, and dose-averaged linear energy transfer (LETd). A multiple counter approach was employed to improve efficiency by reducing the frequency of memory writing conflict at scoring. For dose calculation, accuracy improvements over gPMC v1.0 were observed in both water phantom cases and a patient case. For a prostate cancer case planned using high-energy proton beams, dose discrepancies in beam entrance and target region seen in gPMC v1.0 with respect to the gold standard tool for proton Monte Carlo simulations (TOPAS) results were substantially reduced and gamma test passing rate (1%/1 mm) was improved from 82.7%-93.1%. The average relative difference in LETd between gPMC and TOPAS was 1.7%. The average relative differences in the dose deposited by primary, secondary, and other heavier particles were within 2.3%, 0.4%, and 0.2%. Depending on source proton energy and phantom complexity, it took 8-17 s on an AMD Radeon R9 290x GPU to simulate [Formula: see text] source protons, achieving less than [Formula: see text] average statistical uncertainty. As the beam size was reduced from 10 × 10 cm2 to 1 × 1 cm2, the time on scoring was only increased by 4.8% with eight counters, in contrast to a 40% increase using only one counter. With the OpenCL environment, the portability of gPMC v2.0 was enhanced. It was successfully executed on different CPUs and GPUs and its performance on different devices varied depending on processing power and hardware structure.

Purpose We describe a treatment plan optimization method for intensity modulated proton therapy (IMPT) that avoids high values of linear energy transfer (LET) in critical structures located within or near the target volume while limiting degradation of the best possible physical dose distribution. Methods and Materials To allow fast optimization based on dose and LET, a GPU-based Monte Carlo code was extended to provide dose-averaged LET in addition to dose for all pencil beams. After optimizing an initial IMPT plan based on physical dose, a prioritized optimization scheme is used to modify the LET distribution while constraining the physical dose objectives to values close to the initial plan. The LET optimization step is performed based on objective functions evaluated for the product of LET and physical dose (LET×D). To first approximation, LET×D represents a measure of the additional biological dose that is caused by high LET. Results The method is effective for treatments where serial critical structures with maximum dose constraints are located within or near the target. We report on 5 patients with intracranial tumors (high-grade meningiomas, base-of-skull chordomas, ependymomas) in whom the target volume overlaps with the brainstem and optic structures. In all cases, high LET×D in critical structures could be avoided while minimally compromising physical dose planning objectives. Conclusion LET-based reoptimization of IMPT plans represents a pragmatic approach to bridge the gap between purely physical dose-based and relative biological effectiveness (RBE)-based planning. The method makes IMPT treatments safer by mitigating a potentially increased risk of side effects resulting from elevated RBE of proton beams near the end of range.

A three layer Compton telescope for dose monitoring in hadron therapy is under development at IFIC - Valencia. It will consist of three detector layers and each layer will be made of a continuous LaBr 3 crystal coupled to four Silicon Photomultiplier (SiPM) arrays. A prototype made of two detector layers has already been assembled and tested. The detector layers consist of a 32×36×5 mm3 and 32×36×10 mm3 LaBr 3 crystal, each coupled to four SiPM arrays. The front-end electronics of each detector is based on the VATA64HDR16 application specific integrated circuit (ASIC). The gamma rays coming from a 22Na source have been detected with the two detector layers working in time coincidence, allowing energy and position estimation.

Hadron-therapy (HT) aims to treat tumors by maximizing the dose released to the target and sparing the dose to normal tissues. For a successful outcome it is very important to determine where the maximum dose is deposited; therefore range verification is necessary for treatment optimization and patient safety. Secondary positron emitting isotopes and prompt gamma radiation are produced after the hadron beam passage. This secondary radiation coming from tissue activation could be used for quality control of treatment, provided that it can be detected and employed to reconstruct the beam path using imaging techniques. This is one of the main goals of the ENVISION project: to evaluate and develop on-line monitoring devices for HT, like PET for detecting the annihilation photons from positrons and Compton Cameras (CCs) for prompt gamma radiation detection. In both technologies high sensitivity is required to increase the signal-to-noise ratio of the reconstructed image for a given therapeutic dose. This simulation study focuses on the sensitivity optimization of such devices, PET and CC, taking into account its use and constraints for on-line HT monitoring. Several configurations of both technologies have been investigated using sources generated from hadron beams. In the case of PET data, the time-of-flight (TOF) information has been included too. For individual hadron beams acquired after 5 minutes, differences in range of 3 mm are detected for all the PET configurations, except for the partial-ring of 60 cm diameter. In addition, patient data from a carbon ion treatment at GSI have been simulated and reconstructed. The system with higher sensitivity and angular sampling recovers more accurately areas with no activity (nasal cavity). In the case of the CC data, the quality of the reconstructed image when using 2-interaction events is notably improved when the detector layers are placed covering a larger solid angle.

Purpose: In-vitro experiments suggest an increase in proton relative biological effectiveness (RBE) towards the end of range. However, proton treatment planning and dose reporting for clinical outcome assessment has been based on physical dose and constant RBE. Therefore, treatment planning for intensity-modulated proton therapy (IMPT) is unlikely to transition radically to pure RBE-based planning. We suggest a hybrid approach where treatment plans are initially created based on physical dose constraints and prescriptions, and are subsequently altered to avoid high linear energy transfer (LET) in critical structures while limiting the degradation of the physical dose distribution. Methods: To allow fast optimization based on dose and LET we extended a GPU-based Monte-Carlo code towards providing dose-averaged LET in addition to dose for all pencil beams. After optimizing an initial IMPT plan based on physical dose, a prioritized optimization scheme is used to modify the LET distribution while constraining the physical dose objectives to values close to the initial plan. The LET optimization step is performed based on objective functions evaluated for the product of physical dose and LET (LETxD). To first approximation, LETxD represents a measure of the additional biological dose that is caused by high LET. Regarding optimization techniques, LETxD has the advantage of being a linear function of the pencil beam intensities. Results: The method is applicable to treatments where serial critical structures with maximum dose constraint are located in or near the target. We studied intra-cranial tumors (high-grade meningiomas, base-of-skull chordomas) where the target (CTV) overlaps with the brainstem and optic structures. Often, high LETxD in critical structures can be avoided while minimally compromising physical dose planning objectives. Conclusion: LET-based re-optimization of IMPT plans represents a pragmatic approach to bridge the gap between purely physical dose-based and RBE-based planning. It can be realized with fast GPU-based Monte-Carlo dose calculation. The project was in part supported by NIH grants U19 CA 021239-36 and C06 CA 059267.

Purpose: To demonstrate the feasibility of fast Monte Carlo (MC) treatment planning and verification using four-dimensional CT (4DCT) for adaptive IMPT for lung cancer patients. Methods: A validated GPU MC code, gPMC, has been linked to the patient database at our institution and employed to compute the dose-influence matrices (Dij) on the planning CT (pCT). The pCT is an average of the respiratory motion of the patient. The Dijs and patient structures were fed to the optimizer to calculate a treatment plan. To validate the plan against motion, a 4D dose distribution averaged over the possible starting phases is calculated using the 4DCT and a model of the time structure of the delivered spot map. The dose is accumulated using vector maps created by a GPU-accelerated deformable image registration program (DIR) from each phase of the 4DCT to the reference phase using the B-spline method. Calculation of the Dij matrices and the DIR are performed on a cluster, with each field and vector map calculated in parallel. Results: The Dij production takes ∼3.5s per beamlet for 10e6 protons, depending on the energy and the CT size. Generating a plan with 4D simulation of 1000 spots in 4 fields takes approximately 1h. To test the framework, IMPT plans for 10 lung cancer patients were generated for validation. Differences between the planned and the delivered dose of 19% in dose to some organs at risk and 1.4/21.1% in target mean dose/homogeneity with respect to the plan were observed, suggesting potential for improvement if adaptation is considered. Conclusion: A fast MC treatment planning framework has been developed that allows reliable plan design and verification for mobile targets and adaptation of treatment plans. This will significantly impact treatments for lung tumors, as 4D-MC dose calculations can now become part of planning strategies.