Radiotherapy treatment planning hinges on a critical factor: the prescribed dose. Surprisingly, no consistent, standardised global approach to interpreting this prescription exists. This study aimed ...to examine and illustrate the variations in prescribed doses for the same treatment across North European and North American centres.
The study analysed consecutively treated oropharynx cancer patients from six globally recognised radiotherapy departments. The criteria for inclusion encompassed curative IMRT or VMAT radiotherapy administered in 2017 or later. These centres were divided into three North American and three North European centres.
Dose-volume histogram (DVH) data were extracted from the local treatment planning system for the Gross Tumour Volume (GTV), the high-dose Clinical Target Volume (CTV), and Planning Target Volume (PTV) for each patient. The DVH was sampled in 1 cGy dose bins across the 0 to 100 Gy range.
All DVHs were scaled to a standard prescription of 70 Gy delivered in 35 fractions to facilitate straightforward comparisons across centres. No biological corrections were applied.
For the three target volumes (GTV, CTV, PTV), we extracted and compared metrics such as D95% (Dose to 95% of the volume), D98%, D99%, V95% (Volume receiving 95% of the prescription dose), V105%, and V107%. We visually compared these metrics and conducted statistical testing using the Mann-Whitney U-test.
Our study encompassed 1,375 patients treated across six centres, revealing a spectrum of 38 different dose prescriptions, ranging from 55 Gy in 20 to 70 Gy in 35 fractions.
When normalised to 70 Gy, the median mean CTV dose exhibited a 4% difference, ranging from 70.12 Gy to 72.93 Gy across centres. Notably, the three European centres showed a high consistency, deviating by only 0.4%, while the three North American centres showed a slight variation within 2%. Figure 1 presents the mean CTV dose and the D98% boxplots. The interquartile range (IQR) for the mean CTV dose from 0.17 Gy to 1.22 Gy, with European centres showing the smallest IQR. Display omitted
Examining the near-minimum CTV doses (D98% of CTV), we observed a range from 68.65 Gy to 70.96 Gy, with an IQR from 0.30 Gy to 1.15 Gy. A scatter plot of CTV mean dose against CTV D98% revealed distinct clusters for each of the six centres (Figure 2). Notably, the European centres cluster more densely compared to the North American centres. Display omitted
The variation in prescribed doses for the same treatment regimen poses a significant challenge. Clinical interpretation of 70 Gy varies widely between centres and is influenced by each centre's individual experience, which, in turn, impacts the interpretation of published results. European centres primarily adhere to the ICRU dose prescription, targeting the median CTV dose. Conversely, North American centres tend to prescribe based on the minimum dose to the CTV or, in some cases, the PTV, as outlined in various RTOG protocols.
Our study underscores that prescribing a dose of 70 Gy in 35 fractions for standard oropharynx cancer patients results in substantial variations in mean and near-minimum CTV doses. These dose prescription discrepancies significantly impact the interpretation of clinical trial outcomes comparison. Furthermore, this significant dosimetric variability has important implications for dose de-escalation strategies for HPV+ oropharynx cancer treatment.
Radiotherapy (RT) plan quality is critical in ensuring treatment efficacy. Poor quality RT can increase the risks of treatment failure, overall mortality and detrimentally impacting a patient's ...quality of life 1–4. This is especially important within RT clinical trials, where standardisation of treatment plan quality is paramount. However, widespread objective quantitative assessment of plan quality within trials is not performed routinely, leading to uncertainty on the magnitude of quality variations. Automated planning enables the possibility to efficiency and objectively assess the quality of individual clinical plans (CP) through comparison with an automatically generated standardised 'baseline’ plan. Utilising this innovative auditing methodology within a trial enables full quantitative characterisation of: (i) overall plan quality, (ii) potential outliers and (iii) variation solely due to planning practice. The aim of this study was to use fully automated planning to objectively assess plan quality within the Cancer Research UK funded (A25317) multi-centre international phase III trial PATHOS.
337 patients enrolled in the PATHOS clinical trial before 1st July 2021 were included in this study. 55 cases were excluded due to incomplete data and 16 for calibrating the automated solution, leaving 264 patients for analysis. 219 (83%) and 45 (17%) cases were treated with unilateral (Unilat) and bilateral (Bilat) volumes respectively. Planning was performed in alignment with the PATHOS protocol, with prescriptions of Bilat66Gy, Bilat60Gy, Unilat66Gy or Unilat60Gy in 30 fractions and Unilat50Gy in 25 fractions. Automated treatment plans (AP) were generated in RayStation using a locally developed 'Protocol Based Automatic Iterative Optimization’ automated planning solution 5. CP were quantitatively compared to AP across all the PATHOS trial metrics (including: Parotid Dmean; SpinalCord/BrainStem PRV D1cc; and PTV D98%, D2% and D50%) together with conformality (CI) and homogeneity (HI) indices. Analysis was performed with data categorised in terms of prescription and also tumour laterality. Statistical significance was assessed via a two-sided Wilcoxon matched-paired signed-rank test.
Display omitted Fig. 1 and Fig. 2 present a summary of the dosimetric results, categorised in terms of prescription. When comparing CP to the AP baseline (CP-AP), statistically significant (p≤0.05) differences, Δ, in median values were observed across most key metrics. For HI, small changes across all prescriptions were detected for the primary PTV with the largest Δ equalling (-0.012, p<0.001) for Unilat50Gy prescriptions. This indicated CP were marginally more homogeneous that the AP baseline. For CI, significant differences were observed across primary PTVs for three prescriptions (Unilat50Gy, Unilat60Gy and Bilat60Gy) and all secondary PTVs. Median differences were substantial, with a max Δ of +0.110 (p<0.001, Unilat66:PTV54), which represented a 10% increase in the volume treated to 54Gy for CP. When categorised in terms of tumour laterality, differences in contralateral Parotid (Parotid_CL) Dmean were small for Unilat (Δ=+2.2Gy, p<0.001) and moderate for Bilat cases (Δ=+3.5Gy, p<0.001). For ipsilateral Parotids (Parotid_IL), differences were substantial for Unilat cases (Δ=+4.8Gy, p<0.001) but nominally equivalent to Parotid_CL for Bilat (Δ=+3.1Gy, p<0.001).
At an individual patient level, AP baseline plans highlighted potential quality improvements that could have been realised for CP. For 50% of all patients, AP led to a reduction in Parotid_IL and Parotid_CL Dmean of between 4.4Gy-14.7Gy and 2.5Gy-8.9Gy respectively. In terms of conformality, for 50% of all patients AP reduced CI by between 0.06-0.35 and 0.08-0.28 for PTV60 and PTV54 respectively.
In terms of overall variation with the trial, Fig. 1 and Fig. 2 demonstrate that a high proportion of the variation observed in the majority dose metrics was a direct result of plan quality. For example, a standardised AP planning method would have reduced the inter-quartile range (IQR) for Parotid_CL Dmean from 5.4Gy to 1.4Gy, for HI (PTV54) from 0.031 to 0.015 and for CI (PTV54) from 0.194 to 0.071. Parotid_IL Dmean was a key exception, with similar IQRs for both AP and CP.
Clinics participating in PATHOS undergo a comprehensive quality assurance process prior to patient recruitment, with additional 'on trial’ qualitative reviews performed on small subset of patients. Furthermore, all patient plans must, where practicable, meet trial dose metric tolerances. Results of this study demonstrate that despite these procedures, which are common to many high-quality trials, meaningful variations in plan quality remain. Automated planning was found to be an effective tool in objectively assessing plan quality within a large trial. Implementation on a prospective basis could be a powerful QA tool to reduce this observed variation.
The aim in radiotherapy treatment planning is to have sufficient target coverage and as low a dose to the Organs at Risk (OARs) as possible, adhering to the relevant guidelines. A high and consistent ...radiotherapy plan quality is vital when treatment plans are used as the foundation for patient selection in clinical trials. Proton therapy, being a substantially newer treatment modality than conventional photon therapy, is at risk of having a steeper learning curve in treatment planning. This inequality is important to investigate in a clinical study comparing the two, as this could influence the trial results.
This study aims to evaluate the development of radiotherapy treatment plan quality for head and neck cancer patients receiving photon and proton therapy over time in the context of the DAHANCA 35 trial.
From May 2019 to June 2023,189 patients were included in the ongoing DAHANCA 35 trial, with 63 patients in the pilot phase and 126 in the subsequent randomisation phase. In the pilot phase, all included patients were offered proton treatment, and in the randomisation phase, patients were randomised 1:2 (photon:proton). Patients were first seen at a local treatment centre, where a photon and comparative proton plan were prepared. If patients were offered proton treatment, a new clinical proton plan was made at the proton treatment centre and subsequently used for treatment. This study analysed 189 photon plans, 189 comparative proton plans, and 140 clinical proton plans.
The treatment plans were prepared conforming to the DAHANCA guidelines 1 to ensure the clinical relevance of all treatment plans
The plan quality was assessed separately for photon plans, comparative proton plans, and clinical proton plans in three time intervals.
The mean dose was investigated individually for 13 OARs relevant for head and neck cancer: oesophagus, glottic larynx, supraglottic larynx, mandible, extended oral cavity, left and right parotid glands, upper-, middle-, and lower pharyngeal constrictor muscles, left and right submandibular glands, and thyroid gland.
Furthermore, treatment plan quality was analysed using a new metric called Normalised Toxicity Index (NTI), calculated as a normalised average of the mean dose to the OARs compared to the threshold mean dose recommended by the DAHANCA guidelines.
An NTI > 1 indicated that the OARs, on average, received a dose higher than the recommended thresholds, and an NTI < 1 indicated that the OARs received a dose below the thresholds. Hence, a lower NTI indicated better plan quality concerning OAR doses.
The Kruskal-Wallis test was used to investigate a potential difference in the intervals for mean dose and NTI for each treatment type. The significance level was Bonferroni adjusted to account for multiple testing.
The three time intervals were defined with 63 patients in the pilot phase constituting one interval (Pilot phase), the subsequent 64 patients from the randomisation phase in the next interval (Randomisation 1), and the remaining 62 patients from the randomisation phase in the third interval (Randomisation 2). The periods were 22 months for the Pilot phase, 19 months for Randomisation 1, and 14 months for Randomisation 2.
Across the 13 OARs, the mean dose to individual OARs did not show a general time-dependent change, except for the right parotid gland in the clinical proton plans. Figure 1 shows a box plot with samples overlaid for the mean dose to the extended oral cavity as an example of the OARs. Display omitted
The NTI was not significantly different for the photon plans, comparative proton plans, and clinical proton plans in the three consecutive intervals, as shown in Figure 2. The median NTI for the clinical proton plans was 0.88 (interquartile range 0.70,1.00) for the Pilot phase, 0.83 0.75,0.89 for Randomization 1, and 0.79 0.67,0.98 for Randomization 2. The plan quality of the clinical proton plans appears stable from this new NTI metric. Display omitted
The analyses conducted in this study did not show a general time-dependent change in plan quality in any of the three types of plans. This could be caused by the nationally developed proton treatment planning template.
A stable treatment plan quality can help ensure a consistent selection for clinical trials, thus providing transparency for analysis of the outcome of the trials. The plan quality will continuously be followed to ensure consistency.
Glioblastoma is a deadly brain tumour with a low survival time. The INDYGO clinical trial aims to increase survival by maximizing tumour resection via PDT. A simulation of the trial protocol is ...independently produced with the aim of increasing understanding of the treatment parameters and their effect on treatment outcome.
Monte Carlo Radiative Transport (MCRT) is used to simulate light travel and PDT within an in-silico brain model containing a glioblastoma. Treatment parameters were changed, and the effect determined based on the percentage of tumour killed.
Simulating the standard trial setup with a 5 μM photosensitizer concentration resulted in 42 % cell kill by the end of treatment. Increasing treatment time and light power resulted in increased cell kill, however an increase in photosentisiser concentration had the biggest effect.
These results provide detailed information that can be used when planning intraoperative PDT treatments for glioblastoma.
ABSTRACTBackground Understanding the implications of aging on endodontic treatment is important to appropriately care for patients with increasingly complex and diverse health needs in conjunction ...with increasingly intact and complex dentition.Types of Studies Reviewed Content from existing studies and literature on aging, oral health and endodontics was reviewed and integrated to present a holistic approach to endodontics for older adults. The full spectrum of need and ability of older adults, from well to frail elders, was taken into consideration.Results Age-related changes in the dentition present challenges to endodontic diagnosis and treatment that are compounded by physical or cognitive impairments that alter presentation of disease and response to treatment. Communication may need to extend beyond the patient and the referring provider to establish a diagnosis, clear expectations and goals of treatment.Practical Implications It is important to understand the complexities unique to aging and aging’s potential impact on clinical outcomes to ensure the appropriate inclusion and delivery of endodontic care to older adults.Continuing Education Credit Available The practice worksheet is available online in the supplementary material tab for this article. A CDA Continuing Education quiz is online for this article: https://www.cdapresents360.com/learn/catalog/view/20.
Purpose:
To evaluate the feasibility and accuracy of magnetic resonance imaging (MRI)-based treatment planning using pseudo CTs generated through atlas registration.
Methods:
A pseudo CT, providing ...electron density information for dose calculation, was generated by deforming atlas CT images previously acquired on other patients. The authors tested 4 schemes of synthesizing a pseudo CT from single or multiple deformed atlas images: use of a single arbitrarily selected atlas, arithmetic mean process using 6 atlases, and pattern recognition with Gaussian process (PRGP) using 6 or 12 atlases. The required deformation for atlas CT images was derived from a nonlinear registration of conjugated atlas MR images to that of the patient of interest. The contrasts of atlas MR images were adjusted by histogram matching to reduce the effect of different sets of acquisition parameters. For comparison, the authors also tested a simple scheme assigning the Hounsfield unit of water to the entire patient volume. All pseudo CT generating schemes were applied to 14 patients with common pediatric brain tumors. The image similarity of real patient-specific CT and pseudo CTs constructed by different schemes was compared. Differences in computation times were also calculated. The real CT in the treatment planning system was replaced with the pseudo CT, and the dose distribution was recalculated to determine the difference.
Results:
The atlas approach generally performed better than assigning a bulk CT number to the entire patient volume. Comparing atlas-based schemes, those using multiple atlases outperformed the single atlas scheme. For multiple atlas schemes, the pseudo CTs were similar to the real CTs (correlation coefficient, 0.787–0.819). The calculated dose distribution was in close agreement with the original dose. Nearly the entire patient volume (98.3%–98.7%) satisfied the criteria of chi-evaluation (<2% maximum dose and 2 mm range). The dose to 95% of the volume and the percentage of volume receiving at least 95% of the prescription dose in the planning target volume differed from the original values by less than 2% of the prescription dose (root-mean-square, RMS < 1%). The PRGP scheme did not perform better than the arithmetic mean process with the same number of atlases. Increasing the number of atlases from 6 to 12 often resulted in improvements, but statistical significance was not always found.
Conclusions:
MRI-based treatment planning with pseudo CTs generated through atlas registration is feasible for pediatric brain tumor patients. The doses calculated from pseudo CTs agreed well with those from real CTs, showing dosimetric accuracy within 2% for the PTV when multiple atlases were used. The arithmetic mean process may be a reasonable choice over PRGP for the synthesis scheme considering performance and computational costs.
Over the last two decades, the computed tomography simulator became the standard of the contemporary radiotherapy treatment planning (RTP) process. Along the same time, the superb soft tissue ...contrast of magnetic resonance imaging (MRI) was widely incorporated into RTP through the process of image coregistration. This review summarizes the efforts of incorporation of MRI data into target definition process for RTP based on gained clinical evidence so far and opens a question whether the time is up for bringing a MRI-simulator as an additional standard imaging tool into radiation oncology departments.
Purpose:
To determine how training set size affects the accuracy of knowledge-based treatment planning (KBP) models.
Methods:
The authors selected four models from three classes of KBP approaches, ...corresponding to three distinct quantities that KBP models may predict: dose–volume histogram (DVH) points, DVH curves, and objective function weights. DVH point prediction is done using the best plan from a database of similar clinical plans; DVH curve prediction employs principal component analysis and multiple linear regression; and objective function weights uses either logistic regression or K-nearest neighbors. The authors trained each KBP model using training sets of sizes n = 10, 20, 30, 50, 75, 100, 150, and 200. The authors set aside 100 randomly selected patients from their cohort of 315 prostate cancer patients from Princess Margaret Cancer Center to serve as a validation set for all experiments. For each value of n, the authors randomly selected 100 different training sets with replacement from the remaining 215 patients. Each of the 100 training sets was used to train a model for each value of n and for each KBT approach. To evaluate the models, the authors predicted the KBP endpoints for each of the 100 patients in the validation set. To estimate the minimum required sample size, the authors used statistical testing to determine if the median error for each sample size from 10 to 150 is equal to the median error for the maximum sample size of 200.
Results:
The minimum required sample size was different for each model. The DVH point prediction method predicts two dose metrics for the bladder and two for the rectum. The authors found that more than 200 samples were required to achieve consistent model predictions for all four metrics. For DVH curve prediction, the authors found that at least 75 samples were needed to accurately predict the bladder DVH, while only 20 samples were needed to predict the rectum DVH. Finally, for objective function weight prediction, at least 10 samples were needed to train the logistic regression model, while at least 150 samples were required to train the K-nearest neighbor methodology.
Conclusions:
In conclusion, the minimum required sample size needed to accurately train KBP models for prostate cancer depends on the specific model and endpoint to be predicted. The authors’ results may provide a lower bound for more complicated tumor sites.
Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design ...highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence–based treatment planning applications, such as deep learning–based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence–based treatment planning are discussed for future works.
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