Automated conversion of Millennium-120 VMAT plans to HDMLC geometry: Software development and treatment of first patients.

PURPOSE: To provide plan backup resiliency for patients treated on a solitary high definition multileaf collimator (HDMLC) linac by developing a fully integrated Eclipse script, which converts patient plans initially optimized on Millennium-120 (M120) MLC to dosimetrically equivalent leaf motions for delivery on HDMLC. In the event of HDMLC machine downtime, affected patients can be transferred to Millennium-120 units, and their backup plan delivered without delay. METHODS: Write-enabled Eclipse scripting is leveraged to generate HDMLC treatment fields with control points parameterized to mimic apertures of an existing Millennium-120 VMAT plan. Non-parity between intermediate control point gantry angles of script generated arcs relative to VMAT is reconciled through an interpolation subroutine to correct for the apertures and monitor units that would have existed at intermediate angles. Differences in dosimetric leaf gap are corrected by displacing the subset of leaves undergoing dynamic motion. A nominal change to plan normalization corrects for remaining discrepancies between beam models. RESULTS: Over 220 non-SABR VMAT patients were treated on a solitary HDMLC linac with plans converted using the developed script. All have undergone streamlined RO review and physics quality assurance (QA), where the converted plan replicates the original leaf patterns, representing a minor dosimetric perturbation. Analyzing a subset of converted plans delivered at four anatomical sites, on average 99.3% of points pass the 1%/1 mm gamma criterion. Dose-volume histograms between the original and converted plans are in excellent agreement. ArcCheck measurements comparing delivery of the converted HDMLC plan to the calculated M120 dose distribution averaged a gamma pass rate of 99.4% (95.2%) at a 3%/3 mm (2%/2 mm) criterion. The conversion process takes 30 s to run, avoids errors in exporting/re-importing, and generates leaf motions deliverable within machine limits. CONCLUSION: The methodology developed for automated plan conversion helped maximize the utilization of a solitary HDMLC linac, while preserving backup interoperability with minimal overhead.

Automated prediction of dosimetric eligibility for hypofractionated prostate radiotherapy.

Clinical implementation of hypofractionated prostate radiotherapy (PROFIT trial, NCT003046759) represents an opportunity to significantly reduce the burden of treatment on the patient and clinic. However, efficacy was only demonstrated among the patient demographic who could meet the trial dose constraints and so it is necessary to emulate this triage step in clinical practice. The purpose of this study was to build a convenient tool to address the challenge of determining patient eligibility for hypofractionated treatment within the clinic. The tool was implemented within the EclipseTM treatment planning system using the scripting environment. Prior to planning a new case, the script computes and displays in a plot the fractional overlap of rectal and bladder wall with the planning target volume. Radial decision boundaries separate the plot into three zones and the new case is then classified as "feasible", "uncertain", or "not feasible". The radial decision boundaries were derived from a retrospective analysis of the overlap values and dosimetric eligibility of 150 patients with intermediate risk prostate cancer. Two-fold cross validation with repetitions demonstrated an average prediction accuracy of over 90%. The tool has been integrated into our clinical planning workflow to enable early identification of the need for planning consults and rapid a-priori determination of dosimetric eligibility for hypofractionated radiotherapy. The tool can be readily adopted by other centres since the underlying metrics can be evaluated without scripting if desired.

Simulation of a medical linear accelerator for teaching purposes.

Simulation software for medical linear accelerators that can be used in a teaching environment was developed. The components of linear accelerators were modeled to first order accuracy using analytical expressions taken from the literature. The expressions used constants that were empirically set such that realistic response could be expected. These expressions were programmed in a MATLAB environment with a graphical user interface in order to produce an environment similar to that of linear accelerator service mode. The program was evaluated in a systematic fashion, where parameters affecting the clinical properties of medical linear accelerator beams were adjusted independently, and the effects on beam energy and dose rate recorded. These results confirmed that beam tuning adjustments could be simulated in a simple environment. Further, adjustment of service parameters over a large range was possible, and this allows the demonstration of linear accelerator physics in an environment accessible to both medical physicists and linear accelerator service engineers. In conclusion, a software tool, named SIMAC, was developed to improve the teaching of linear accelerator physics in a simulated environment. SIMAC performed in a similar manner to medical linear accelerators. The authors hope that this tool will be valuable as a teaching tool for medical physicists and linear accelerator service engineers.

The effect of spatial resolution on deep learning classification of lung cancer histopathology.

Objective: The microscopic analysis of biopsied lung nodules represents the gold-standard for definitive diagnosis of lung cancer. Deep learning has achieved pathologist-level classification of non-small cell lung cancer histopathology images at high resolutions (0.5-2 µm/px), and recent studies have revealed tomography-histology relationships at lower spatial resolutions. Thus, we tested whether patterns for histological classification of lung cancer could be detected at spatial resolutions such as those offered by ultra-high-resolution CT. Methods: We investigated the performance of a deep convolutional neural network (inception-v3) to classify lung histopathology images at lower spatial resolutions than that of typical pathology. Models were trained on 2167 histopathology slides from The Cancer Genome Atlas to differentiate between lung cancer tissues (adenocarcinoma (LUAD) and squamous-cell carcinoma (LUSC)), and normal dense tissue. Slides were accessed at 2.5 × magnification (4 µm/px) and reduced resolutions of 8, 16, 32, 64, and 128 µm/px were simulated by applying digital low-pass filters. Results: The classifier achieved area under the curve ≥0.95 for all classes at spatial resolutions of 4-16 µm/px, and area under the curve ≥0.95 for differentiating normal tissue from the two cancer types at 128 µm/px. Conclusions: Features for tissue classification by deep learning exist at spatial resolutions below what is typically viewed by pathologists. Advances in knowledge: We demonstrated that a deep convolutional network could differentiate normal and cancerous lung tissue at spatial resolutions as low as 128 µm/px and LUAD, LUSC, and normal tissue as low as 16 µm/px. Our data, and results of tomography-histology studies, indicate that these patterns should also be detectable within tomographic data at these resolutions.

Assessment of intrafraction motion for spine and non-spine bone metastases treated with image-guided stereotactic body radiotherapy without 6 degrees-of-freedom couch correction.

Stereotactic body radiotherapy (SBRT) planning target volume (PTV) margins are influenced by multiple factors. Data is limited on intrafraction motion in bone SBRT, particularly non-spine lesions. We analyzed intrafraction motion in bone SBRT patients treated on a standard treatment couch without 6 degrees-of-freedom (6-DOF) correction. Extracranial bone SBRT patients were included. Patients were treated using two volumetric-modulated arcs and targets were localized using daily cone-beam computed tomography (CBCT) prior to each arc. Alignments between the first and second CBCT images yielded intrafraction positional shift values used to compute translational 3-dimensional vector shifts. 125 fractions from 43 patients were reviewed. Median vector shift for all SABR fractions was 0.7 mm (range 0-6.6 mm); spine 0.7 mm (range:0-2.3 mm) and non-spine 0.9 mm (range:0-6.6 mm). Of the 125 fractions, 95% had IFM vectors within the prescribed PTV margin. Intrafraction motion is small for bone SBRT patients treated on a standard couch without 6-DOF correction capabilities. Intrafraction motion was slightly larger for non-spine sites and may require treatment with larger PTV margins than spine cases.

Distinct residues in the carboxyl tail mediate agonist-induced desensitization and internalization of the human dopamine D1 receptor.

We have shown in a previous study that desensitization and internalization of the human dopamine D(1) receptor following short-term agonist exposure are mediated by temporally and biochemically distinct mechanisms. In the present study, we have used site-directed mutagenesis to remove potential phosphorylation sites in the third intracellular loop and carboxyl tail of the dopamine D(1) receptor to study these processes. Mutant D(1) receptors were stably transfected into Chinese hamster ovary cells, and kinetic parameters were measured. Mutations of Ser/Thr residues to alanine in the carboxyl tail demonstrated that the single substitution of Thr-360 abolished agonist-induced phosphorylation and desensitization of the receptor. Isolated mutation of the adjacent glutamic acid Glu-359 also abolished agonist-induced phosphorylation and desensitization of the receptor. These data suggest that Thr-360 in conjunction with Glu-359 may comprise a motif necessary for GRK2-mediated phosphorylation and desensitization. Agonist-induced internalization was not affected with mutation of either the Thr-360 or the Glu-359 residues. However, receptors with Ser/Thr residues mutated in the distal carboxyl tail (Thr-446, Thr-439, and Ser-431) failed to internalize in response to agonist activation, but were able to desensitize normally. These results indicate that agonist-induced desensitization and internalization are regulated by separate and distinct serine and threonine residues within the carboxyl tail of the human dopamine D(1) receptor.