Organic generation of real-world real-time data for clinical evidence in radiation oncology.

PURPOSE: We introduce a system devoted to automatically produce structured data in radiotherapy to: (i) relate clinical outcomes with any variable; and (ii) optimize resources and procedures. METHODS AND MATERIAL: We have designed a detailed workflow for a patient to follow during radiotherapy treatments. Four elements of Oncology Information Systems (OISs) can be mainly interrelated in our system: (a) task lists to be accomplished by the staff; (b) forms to fill in at each step of the workflow; (c) generation of reports; and (d) a system to trigger new tasks, forms or reports when an needed, either automatically or manually. We handle the data dumped into reports with Visual Basic for Word code to store structured data for patients in electronic medical records (EMRs). These EMRs can be further analyzed, generating clinical real-world data in real time, i.e., at any step of the process. RESULTS: Our system was implemented about the beginning of 2019, producing a database filled with a pool of 1,184 patients in a year. Although one year is not long enough to produce statistically clinical outcomes, we show our results for cancer by anatomical location so far to meet the first goal stated above. With respect to the second goal, we here (1) show the distribution of times taken for the whole radiotherapy process divided by anatomical locations for, (2) study the fractionations schemes used throughout 2019, and (3) evaluate the number of missed sessions of treatment in our institution. Our system also leads to better communication among staff members, dramatically reducing misunderstandings because of the centralization of the information. CONCLUSIONS: We present an integrated customization of an OIS, yet adaptable to others, that makes possible an optimized performance of the department by driving an automatized paperless workflow; and allows for an automatized and effortless collection of structured data throughout the radiotherapy process.

Dosimetric impact assessment using a general algorithm in geant4 simulations for a complex-shaped multileaf collimator.

PURPOSE: We have developed an inhouse algorithm for the multileaf collimator (MLC) geometry model construction with an appropriate accuracy for dosimetric tests. Our purpose is to build a complex type of MLC and analyze the influence of the modeling parameters on the dose calculation. METHODS: Using radiochromic films as detector the following tests were done: (I) Density test field: to compare measured and calculated dose distributions in order to determine the tungsten alloy physical density value. (II) Leaf ends test field: to verify the penumbra shape sensitivity against the discretization level set to simulate the curved leaf ends. (III) MLC-closed field: to obtain the value of the air gap between opposite leaves for a closed configuration which completes the modeling of the MLC leakage radiation. (IV) Picket-fence field: to fit the leaf tilt angle with respect of the divergent ray emerging from the source. RESULTS: For a 18.5g/cm3 density value we have obtained a maximum, minimum and mean leakage values of 0.43%, 0.36% and 0.38%, similar to the experimental ones. The best discretization level in the leaf ends field shows a 5.51mm FWHM, very close to the measured value (5.49mm). An air gap of 370µm has been used in the simulation for the separation between opposite leaves. Using a 0.44° tilt angle, we found the same pattern as the experimental values. CONCLUSIONS: Our code can reproduce complex MLC designs with a submilimetric dosimetric accuracy which implies the necessary background for dose calculation of high clinical interest small fields.

MCTP system model based on linear programming optimization of apertures obtained from sequencing patient image data maps.

PURPOSE: The authors present a hybrid direct multileaf collimator (MLC) aperture optimization model exclusively based on sequencing of patient imaging data to be implemented on a Monte Carlo treatment planning system (MC-TPS) to allow the explicit radiation transport simulation of advanced radiotherapy treatments with optimal results in efficient times for clinical practice. METHODS: The planning system (called CARMEN) is a full MC-TPS, controlled through aMATLAB interface, which is based on the sequencing of a novel map, called "biophysical" map, which is generated from enhanced image data of patients to achieve a set of segments actually deliverable. In order to reduce the required computation time, the conventional fluence map has been replaced by the biophysical map which is sequenced to provide direct apertures that will later be weighted by means of an optimization algorithm based on linear programming. A ray-casting algorithm throughout the patient CT assembles information about the found structures, the mass thickness crossed, as well as PET values. Data are recorded to generate a biophysical map for each gantry angle. These maps are the input files for a home-made sequencer developed to take into account the interactions of photons and electrons with the MLC. For each linac (Axesse of Elekta and Primus of Siemens) and energy beam studied (6, 9, 12, 15 MeV and 6 MV), phase space files were simulated with the EGSnrc/BEAMnrc code. The dose calculation in patient was carried out with the BEAMDOSE code. This code is a modified version of EGSnrc/DOSXYZnrc able to calculate the beamlet dose in order to combine them with different weights during the optimization process. RESULTS: Three complex radiotherapy treatments were selected to check the reliability of CARMEN in situations where the MC calculation can offer an added value: A head-and-neck case (Case I) with three targets delineated on PET/CT images and a demanding dose-escalation; a partial breast irradiation case (Case II) solved with photon and electron modulated beams (IMRT + MERT); and a prostatic bed case (Case III) with a pronounced concave-shaped PTV by using volumetric modulated arc therapy. In the three cases, the required target prescription doses and constraints on organs at risk were fulfilled in a short enough time to allow routine clinical implementation. The quality assurance protocol followed to check CARMEN system showed a high agreement with the experimental measurements. CONCLUSIONS: A Monte Carlo treatment planning model exclusively based on maps performed from patient imaging data has been presented. The sequencing of these maps allows obtaining deliverable apertures which are weighted for modulation under a linear programming formulation. The model is able to solve complex radiotherapy treatments with high accuracy in an efficient computation time.

CloudMC: a cloud computing application for Monte Carlo simulation.

This work presents CloudMC, a cloud computing application-developed in Windows Azure®, the platform of the Microsoft® cloud-for the parallelization of Monte Carlo simulations in a dynamic virtual cluster. CloudMC is a web application designed to be independent of the Monte Carlo code in which the simulations are based-the simulations just need to be of the form: input files → executable → output files. To study the performance of CloudMC in Windows Azure®, Monte Carlo simulations with penelope were performed on different instance (virtual machine) sizes, and for different number of instances. The instance size was found to have no effect on the simulation runtime. It was also found that the decrease in time with the number of instances followed Amdahl's law, with a slight deviation due to the increase in the fraction of non-parallelizable time with increasing number of instances. A simulation that would have required 30 h of CPU on a single instance was completed in 48.6 min when executed on 64 instances in parallel (speedup of 37 ×). Furthermore, the use of cloud computing for parallel computing offers some advantages over conventional clusters: high accessibility, scalability and pay per usage. Therefore, it is strongly believed that cloud computing will play an important role in making Monte Carlo dose calculation a reality in future clinical practice.