The performance of the TrueBeam Linac model was shown to be consistent based on automated analysis of trajectory log files and EPID images acquired during delivery of a standardized test suite. The results can be compared directly to tolerance thresholds. In addition, sharing of results from standard tests across institutions can facilitate the identification of QA process and Linac changes. These reference values are presented along with the standard deviation for common tests so that the test suite can be used by other centers to evaluate their Linac performance against those in this consortium.

PurposeTo prepare for big data analyses on radiation therapy data, we developed Stature, a tool-supported approach for standardization of structure names in existing radiation therapy plans. We applied the widely endorsed nomenclature standard TG-263 as the mapping target and quantified the structure name inconsistency in 2 real-world data sets.Methods and MaterialsThe clinically relevant structures in the radiation therapy plans were identified by reference to randomized controlled trials. The Stature approach was used by clinicians to identify the synonyms for each relevant structure, which was then mapped to the corresponding TG-263 name. We applied Stature to standardize the structure names for 654 patients with prostate cancer (PCa) and 224 patients with head and neck squamous cell carcinoma (HNSCC) who received curative radiation therapy at our institution between 2007 and 2017. The accuracy of the Stature process was manually validated in a random sample from each cohort. For the HNSCC cohort we measured the resource requirements for Stature, and for the PCa cohort we demonstrated its impact on an example clinical analytics scenario.ResultsAll but 1 synonym group (“Hydrogel”) was mapped to the corresponding TG-263 name, resulting in a TG-263 relabel rate of 99% (8837 of 8925 structures). For the PCa cohort, Stature matched a total of 5969 structures. Of these, 5682 structures were exact matches (ie, following local naming convention), 284 were matched via a synonym, and 3 required manual matching. This original radiation therapy structure names therefore had a naming inconsistency rate of 4.81%. For the HNSCC cohort, Stature mapped a total of 2956 structures (2638 exact, 304 synonym, 14 manual; 10.76% inconsistency rate) and required 7.5 clinician hours. The clinician hours required were one-fifth of those that would be required for manual relabeling. The accuracy of Stature was 99.97% (PCa) and 99.61% (HNSCC).ConclusionsThe Stature approach was highly accurate and had significant resource efficiencies compared with manual curation.

Glioblastomas express a notable heterogeneity in both the histological and cell patterns with glial astrocytic differentiation. Primary glioblastoma, which is the most frequent presentation (90-95%), occurs mainly in older patients and arises de novo, without any clinical or histological evidence of a less malignant precursor lesion. EGFR amplification has been identified as a genetic hallmark of primary glioblastomas and occurs in 40-60% of cases. However, there exist primary glioblastomas without EGFR amplification/overexpression. The purpose of this study was to stabilize the association between cases with and without EGFR gene amplification with clinical and genetic parameters in 45 cases of primary glioblastomas. EGFR amplification was observed in 24 cases (53%), while in the remaining 21 cases (47%) this alteration was not displayed. And whereas EGFR was overexpressed in 79% of cases with EGFR amplification, only 33% of the cases without EGFR amplification showed overexpression. The amplification of EGFR was associated with amplifications in MDM2 and CDK4 and a higher percentage of cases with promoter methylation of INK4a. Only one case of glioblastoma with EGFR amplification presented TP53 mutation simultaneously. Seven remaining cases with TP53 mutations were glioblastomas without EGFR amplification. The INK4a, INK4b and ARF deletions were similar in the two groups. Primary glioblastomas with and without EGFR amplification did not show any significant differences in average survival. The genetic studies suggest the existence of molecular subtypes within primary glioblastoma that may, when fully defined, contribute toward the development of drugs that specifically target tumors with divergent genetic profiles.

BackgroundUnscheduled accelerator downtime can negatively impact the quality of life of patients during their struggle against cancer. Currently digital data accumulated in the accelerator system is not being exploited in a systematic manner to assist in more efficient deployment of service engineering resources. The purpose of this study is to develop an effective process for detecting unexpected deviations in accelerator system operating parameters and/or performance that predicts component failure or system dysfunction and allows maintenance to be performed prior to the actuation of interlocks.MethodsThe proposed predictive maintenance (PdM) model is as follows: 1) deliver a daily quality assurance (QA) treatment; 2) automatically transfer and interrogate the resulting log files; 3) once baselines are established, subject daily operating and performance values to statistical process control (SPC) analysis; 4) determine if any alarms have been triggered; and 5) alert facility and system service engineers. A robust volumetric modulated arc QA treatment is delivered to establish mean operating values and perform continuous sampling and monitoring using SPC methodology. Chart limits are calculated using a hybrid technique that includes the use of the standard SPC 3σ limits and an empirical factor based on the parameter/system specification.ResultsThere are 7 accelerators currently under active surveillance. Currently 45 parameters plus each MLC leaf (120) are analyzed using Individual and Moving Range (I/MR) charts. The initial warning and alarm rule is as follows: warning (2 out of 3 consecutive values ≥ 2σ hybrid) and alarm (2 out of 3 consecutive values or 3 out of 5 consecutive values ≥ 3σ hybrid). A customized graphical user interface provides a means to review the SPC charts for each parameter and a visual color code to alert the reviewer of parameter status. Forty-five synthetic errors/changes were introduced to test the effectiveness of our initial chart limits. Forty-three of the forty-five errors (95.6 %) were detected in either the I or MR chart for each of the subsystems monitored.ConclusionOur PdM model shows promise in providing a means for reducing unscheduled downtime. Long term monitoring will be required to establish the effectiveness of the model.

The authors describe «he data structure necessary to provide real-time simulation and visualization of complex environments and situations.HE limitations imposed by rrrarket costs and technology have so far obstrvrcted the development of small commercial driving simirlators with good performance levels, while a small set of expensive simirlators exist in car manufacturing companies and tratfic research institutes. In order to obtain the proper immersion sensation for the driver within the synthetic scene, simulator developers have resorted to large panoramic screens and high-resolution video projectors. For many applications it is also reqirired to simulate the accelerations (centriftrgal forces, hraking, etc.) by nsing a mobile platform (see [10] for a sumrrrary of conventional sirntrlation technology). The.se platforms must be able to lift the huge weight of the vehicle cockpit, viewing screen, and projection system-^which is costly and requires an elaborate hydraulic .system.The development of smaller and portable simirlators for application in tr-aining has made it necessary for this equipment to be substituted by single tlat projection screens or multiple video monitors [9], and very simple motion or vibration systems. Virtiral Reality (VR) de'vices wotild offer the natural solution for' inexpensive and compact driving simulatiorr, maintaining a high degree of the immersion feeling. The headmouuted displays (HMDs) provide a means to reproduce the role of panoranric screerrs with rro lirrritalion of viewing area (since drivers carr turrr thtir' heads in all directions), the limitation of sight to the synthetic images and qnality 3D sotmd with mirch lower cost, size, and weight. Some companies (Volvo in Sweden, for example) are fX|3l<)r'ing the a|)pli( ation ot \'R in limited driving simulation for design arrd di'uiorrstration purposes.For the past three years the LISITT (¡jibiiratorio Jnlegrado de Sistettias Inteligentes y Tecnologías de hi iiijortnacion en Trafico) has been working on the SIRCA {Simulador Reactivo de Conducdon de Automóviles) research pr()ject to study the development of small and medirrrn-size sut)ject-oriented simulators, using \'R aird I'valuating il in comparison with conventiorral ti-chrrologies. SIRC.A is currently running on Silicon Graphics platforms with different per-forrnance levels. The gr'a])hical output (see Figure 1) can be pr-e.serited irr a higli-dctiuitioii computer or video nronitor or in head-rrrorrrrti'd displays. The driver's interaction is pr-oduced du'ough a sensorized cockpit. Users are able to steer, speed irp, and brake while thev wear the HMD or look at a moni-72 Mav 1<)96/Vul. .W. N... '

identified one outlier cluster (0.34%) along Leaf offset Constancy (LoC) axis that coincided with TG-142 limits. Conclusion: Machine learning methods based on SVDD clustering are promising for developing automated QA tools and providing insights into their reliability and reproducibility.

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