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ObjectiveDosing errors can cause significant harm in paediatric healthcare settings. Our objective was to investigate the effects of paediatric dose range checking (DRC) clinical decision support (CDS) software on overdosing-related outcomes.MethodsA before-after study and a semistructured survey of prescribers was conducted across inpatient wards (excluding intensive care) in a regional children’s hospital. DRC CDS software linked to a paediatric drug formulary was integrated into an existing electronic prescribing system. The main outcome measures were; the proportion of prescriptions with overdosing errors; overdosing-related clinical incidents; severity of clinical incidents; and acceptability of the intervention.ResultsThe prescription overdosing error rate did not change significantly following the introduction of DRC CDS software: in the preintervention period 12/847 (1.4%) prescriptions resulted in prescription errors and in the postintervention period there were 9/684 (1.3%) prescription overdosing errors (n=21, Pearson χ2 value=0.028, p=0.868). However, there was a significant trend towards a reduction in the severity of harm associated with reported overdosing incidents (n=60, Mann-Whitney U value=301.0, p=0.012). Prescribers reported that the intervention was beneficial and they were also able to identify factors that may have contributed to the persistence of overdosing errors.ConclusionDRC CDS software did not reduce the incidence of prescription overdosing errors in a paediatric hospital setting but the level of harm associated with the overdosing errors may have been reduced. Use of the software seemed to be safe and it was perceived to be beneficial by prescribers.

The dosimetric leaf gap (DLG) and tongue‐and‐groove (T&G) effects are critical aspects in the modeling of multileaf collimators (MLC) in the treatment planning system (TPS). In this study, we investigated the dosimetric impact of limitations associated with the T&G modeling in stereotactic plans and its relationship with the need for tuning the DLG in the Eclipse TPS. Measurements were carried out using Varian TrueBeam STx systems from two different institutions. Test fields presenting MLC patterns with several MLC gap sizes (meanGap) and different amounts of T&G effect (TGi) were first evaluated. Secondly, dynamic conformal arc (DCA) and volumetric modulated arc therapy (VMAT) deliveries of stereotactic cases were analyzed in terms of meanGap and TGi. Two DLG values were used in the TPS: the measured DLG (DLGmeas) and an optimal DLG (DLGopt). Measured and calculated doses were compared according to dose differences and gamma passing rates (GPR) with strict local gamma criteria of 2%/2 mm. The discrepancies were analyzed for DLGmeas and DLGopt, and their relationships with both TGi and meanGap were investigated. DCA arcs involved significantly lower TGi and larger meanGap than VMAT arcs (P < 0.0001). By using DLGmeas in the TPS, the dose discrepancies increased as TGi increased and meanGap decreased for both test fields and clinical plans. Dose discrepancies dramatically increased with the ratio TGi/meanGap. Adjusting the DLG value was then required to achieve acceptable calculations and configuring the TPS with DLGopt led to an excellent agreement with median GPRs (2%/2 mm) > 99% for both institutions. We also showed that DLGopt could be obtained from the results of the test fields. We demonstrated that the need for tuning the DLG is due to the limitations of the T&G modeling in the Eclipse TPS. A set of sweeping gap tests modified to incorporate T&G effects can be used to determine the optimal DLG value.

Purpose To quantify the impact of treatment planning system beam model parameters, based on the actual spread in radiotherapy community data, on clinical treatment plans and determine which complexity metrics best describe the impact beam modeling errors have on dose accuracy. Methods Ten beam modeling parameters for a Varian accelerator were modified in RayStation to match radiotherapy community data at the 2.5, 25, 50, 75, and 97.5 percentile levels. These modifications were evaluated on 25 patient cases, including prostate, non‐small cell lung, H&N, brain, and mesothelioma, generating 1,000 plan perturbations. Differences in the mean planned dose to clinical target volumes (CTV) and organs at risk (OAR) were evaluated with respect to the planned dose using the reference (50th‐percentile) parameter values. Correlation between CTV dose differences, and 18 different complexity metrics were evaluated using linear regression; R‐squared values were used to determine the best metric. Results Perturbations to MLC offset and transmission parameters demonstrated the greatest changes in dose: up to 5.7% in CTVs and 16.7% for OARs. More complex clinical plans showed greater dose perturbation with atypical beam model parameters. The mean MLC Gap and Tongue & Groove index (TGi) complexity metrics best described the impact of TPS beam modeling variations on clinical dose delivery across all anatomical sites; similar, though not identical, trends between complexity and dose perturbation were observed among all sites. Conclusion Extreme values for MLC offset and MLC transmission beam modeling parameters were found to most substantially impact the dose distribution of clinical plans and careful attention should be given to these beam modeling parameters. The mean MLC Gap and TGi complexity metrics were best suited to identifying clinical plans most sensitive to beam modeling errors; this could help provide focus for clinical QA in identifying unacceptable plans.

Tetraploidy, or whole-genome duplication, is a common phenomenon in cancer and preludes chromosome instability, which strongly correlates with disease progression, metastasis, and treatment failure. Therefore, it is reasonable to hypothesize that tetraploidization confers multidrug resistance. Nevertheless, the contribution of whole-genome duplication to chemo-radiotherapy resistance remains unclear. Here, using isogenic diploid and near-tetraploid clones from three colorectal cancer cell lines and one non-transformed human epithelial cell line, we show a consistent growth impairment but a divergent tumorigenic potential of near-tetraploid cells. Next, we assessed the effects of first-line chemotherapeutic drugs, other commonly used agents and ionizing radiation, and found that whole-genome duplication promoted increased chemotherapy resistance and also conferred protection against irradiation. When testing the activation of apoptosis, we observed that tetraploid cells were less prone to caspase 3 activation after treatment with first-line chemotherapeutic agents. Furthermore, we found that pre-treatment with ataxia telangiectasia and Rad3 related (ATR) inhibitors, which targets response to replication stress, significantly enhanced the sensitivity of tetraploid cells to first-line chemotherapeutic agents as well as to ionizing radiation. Our findings provide further insight into how tetraploidy results in greater levels of tolerance to chemo-radiotherapeutic agents and, moreover, we show that ATR inhibitors can sensitize near-tetraploid cells to commonly used chemo-radiotherapy regimens.

The dosimetric leaf gap (DLG) and tongue‐and‐groove (T&G) effects are critical aspects in the modeling of multileaf collimators (MLC) in the treatment planning system (TPS). In this study, we investigated the dosimetric impact of limitations associated with the T&G modeling in stereotactic plans and its relationship with the need for tuning the DLG in the Eclipse TPS. Measurements were carried out using Varian TrueBeam STx systems from two different institutions. Test fields presenting MLC patterns with several MLC gap sizes (meanGap) and different amounts of T&G effect (TGi) were first evaluated. Secondly, dynamic conformal arc (DCA) and volumetric modulated arc therapy (VMAT) deliveries of stereotactic cases were analyzed in terms of meanGap and TGi. Two DLG values were used in the TPS: the measured DLG (DLGmeas) and an optimal DLG (DLGopt). Measured and calculated doses were compared according to dose differences and gamma passing rates (GPR) with strict local gamma criteria of 2%/2 mm. The discrepancies were analyzed for DLGmeas and DLGopt, and their relationships with both TGi and meanGap were investigated. DCA arcs involved significantly lower TGi and larger meanGap than VMAT arcs (P < 0.0001). By using DLGmeas in the TPS, the dose discrepancies increased as TGi increased and meanGap decreased for both test fields and clinical plans. Dose discrepancies dramatically increased with the ratio TGi/meanGap. Adjusting the DLG value was then required to achieve acceptable calculations and configuring the TPS with DLGopt led to an excellent agreement with median GPRs (2%/2 mm) > 99% for both institutions. We also showed that DLGopt could be obtained from the results of the test fields. We demonstrated that the need for tuning the DLG is due to the limitations of the T&G modeling in the Eclipse TPS. A set of sweeping gap tests modified to incorporate T&G effects can be used to determine the optimal DLG value.

This study introduces a novel metric, Plan Aperture Modulation (PAM), developed to quantify the modulation of radiotherapy treatment plans. PAM aims to provide a clear geometric interpretation, addressing the limitations of previous complexity metrics and facilitating its integration into treatment planning systems (TPSs) and clinical workflows. The PAM metric was defined as the average fraction of the target area outside the beam aperture, weighted over all control points in a treatment plan. The metric was evaluated in VMAT plans for two sites: prostate with lymph nodes and lung SBRT. Plans with varying complexities were generated using the Eclipse TPS, and PAM was compared to established metrics, including Plan Modulation (PM), Modulation Complexity Score (MCS), and monitor units per Gray (MU/Gy). The relationship between PAM and the Modulation Factor (MF), which quantifies the increase in MUs due to plan modulation, was also investigated. PAM provided a more intuitive assessment of plan modulation compared to the other metrics, and was validated across different delivery systems, such as C-arm linacs and Halcyon systems. The metric outperformed the previous metrics, indicated a zero modulation for Dynamic Conformal Arc plans, and was independent of confounding variables, such as treatment technique, beam energy, delivery system, and patient anatomy. Derived equations enabled the calculation of MF based on PAM, allowing for a robust quantification of plan modulation. PAM is a robust and intuitive metric for quantifying modulation in radiotherapy plans. It overcomes the limitations of previous metrics and can be readily implemented in TPSs to control plan modulation during optimization and for reporting. PAM is a promising tool for improving treatment planning workflows and for comparing and benchmarking radiotherapy plans in multi-institutional studies, clinical trials, and audits.

This study introduces a novel metric, Plan Aperture Modulation (PAM), developed to quantify the modulation of radiotherapy treatment plans. PAM aims to provide a clear geometric interpretation, addressing the limitations of previous complexity metrics and facilitating its integration into treatment planning systems (TPSs) and clinical workflows. The PAM metric was defined as the average fraction of the target area outside the beam aperture, weighted over all control points in a treatment plan. The metric was evaluated in VMAT plans for two sites: prostate with lymph nodes and lung SBRT. Plans with varying complexities were generated using the Eclipse TPS, and PAM was compared to established metrics, including Plan Modulation (PM), Modulation Complexity Score (MCS), and monitor units per Gray (MU/Gy). The relationship between PAM and the Modulation Factor (MF), which quantifies the increase in MUs due to plan modulation, was also investigated. PAM provided a more intuitive assessment of plan modulation compared to the other metrics, and was validated across different delivery systems, such as C-arm linacs and Halcyon systems. The metric outperformed the previous metrics, indicated a zero modulation for Dynamic Conformal Arc plans, and was independent of confounding variables, such as treatment technique, beam energy, delivery system, and patient anatomy. Derived equations enabled the calculation of MF based on PAM, allowing for a robust quantification of plan modulation. PAM is a robust and intuitive metric for quantifying modulation in radiotherapy plans. It overcomes the limitations of previous metrics and can be readily implemented in TPSs to control plan modulation during optimization and for reporting. PAM is a promising tool for improving treatment planning workflows and for comparing and benchmarking radiotherapy plans in multi-institutional studies, clinical trials, and audits.

Robust fine tuning of multi-leaf collimator (MLC) Treatment Planning System (TPS) modeling parameters is crucial for creating an optimal beam model, particularly with the ever-increasing accuracy required for advancing techniques. Challenges arise from balancing the trade-off between multiple parameters and therefore the quality of tuning depends on the experience of the physicist and the procedures used. This is in part due to limitations of the MLC modeling within the TPS. As a result, the actual values used have been shown to vary widely between centers. We present and evaluate two different MLC transmission maps to improve the modeling of the Elekta Agility MLC in the RayStation TPS. The model prototypes were developed with discrete and continuous MLC transmission maps assigned to the tongue-and-groove and leaf tip regions. The prototypes aimed to replicate the average doses for synchronous and asynchronous sweeping gap fields, measured using a Farmer chamber in a solid water phantom. This study investigated the impact of these improvements using test fields and a wide variety of measured clinical plans in three different centers. The models achieved good accuracy and the improvements facilitated the standardization of the configuration and commissioning processes and extended the range of validity of TPS dose calculations in wide variety of treatment plans and measuring systems. The simpler MLC prototype with discrete transmission maps performed similarly to the more sophisticated one both in tests and clinical plans and constitutes a good option for routine clinical practice. The need for trade-offs were reduced and the models were successfully configured using a common set of parameters across the three centers, which is useful for reducing the workload and the risks associated with the configuration process, thus improving the accuracy and safety of radiotherapy treatments.

Background Genetic association studies (GAS) aims to evaluate the association between genetic variants and phenotypes. In the last few years, the number of this type of study has increased exponentially, but the results are not always reproducible due to experimental designs, low sample sizes and other methodological errors. In this field, meta-analysis techniques are becoming very popular tools to combine results across studies to increase statistical power and to resolve discrepancies in genetic association studies. A meta-analysis summarizes research findings, increases statistical power and enables the identification of genuine associations between genotypes and phenotypes. Meta-analysis techniques are increasingly used in GAS, but it is also increasing the amount of published meta-analysis containing different errors. Although there are several software packages that implement meta-analysis, none of them are specifically designed for genetic association studies and in most cases their use requires advanced programming or scripting expertise. Results We have developed MetaGenyo, a web tool for meta-analysis in GAS. MetaGenyo implements a complete and comprehensive workflow that can be executed in an easy-to-use environment without programming knowledge. MetaGenyo has been developed to guide users through the main steps of a GAS meta-analysis, covering Hardy-Weinberg test, statistical association for different genetic models, analysis of heterogeneity, testing for publication bias, subgroup analysis and robustness testing of the results. Conclusions MetaGenyo is a useful tool to conduct comprehensive genetic association meta-analysis. The application is freely available at http://bioinfo.genyo.es/metagenyo/ .