Prediction models as decision-support tools for virtual patient-specific quality assurance of helical tomotherapy plans

Cavinato, Samuele; Bettinelli, Andrea; Dusi, Francesca; Fusella, M.; Germani, Alessandra; Marturano, Francesca; Paiusco, M.; Pivato, N.; Rossato, M.A.; Scaggion, A.

doi:10.1016/j.phro.2023.100435

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…Other metrics, such as sdFLOT, nOC, TA, and CLS, which are not correlated with the LOT cutoff value, appear to be correlated with the gamma index. We note that the number of evaluated plans is relatively small, as we measured 36 plans, compared with 881 measured plans in another study; thus, the test results should be considered carefully [13].…”

Section: Synthesis and Suggested Planning Parameter Valuesmentioning

confidence: 95%

“…Various local and global absolute gamma index values were recorded, with a threshold of 10%: 3%/3 mm, 2%/2 mm, 1%/1 mm, 3%/2 mm, and 2%/1 mm. For each of these measured plans, we extracted 65 complexity metrics using the MATLAB (version R2023) program TCoMX developed by Cavinato et al [12,13] to study the correlations among the LOT cutoff parameter, the gamma index values, and these metrics.…”

Section: Impact Of the Lot Cutoff On The Delivery Accuracymentioning

confidence: 99%

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Influence of the Planning Parameters of a New Algorithm on the Dosimetric Quality, Beam-On Time and Delivery Accuracy of Tomotherapy Plans

Burckbuchler,

Dehaynin,

Niederst

et al. 2024

Cancers

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Background: This work aimed to determine the optimum VOLOTM Ultra algorithm parameters for tomotherapy treatments. Methods: 1056 treatment plans were generated with VOLOTM Ultra for 36 patients and six anatomical locations. The impact of varying four parameters was studied: the accelerated treatment (AT), leaf open/close time (LOT) cutoff, normal tissue objective (NTO) weight, and number of iterations. The beam-on time and dosimetric metrics were quantified for the target volumes and organs at risk (OARs). Delivery quality assurance measurements were obtained for 36 plans to assess the delivery accuracy. Results: The mean beam-on time for the helical tomotherapy and TomoDirect (TD) plans decreased by 26.6 ± 2.8% and 17.4 ± 4.3%, respectively, when the accelerated treatment parameter was increased from 0 to 10, at the expense of the planning target volume (PTV) coverage (2% lower D98%) and OAR dose (up to 15% increase). For TD plans, it seems preferable to systematically use an AT value of 10. Increasing the number of iterations beyond six seems unnecessary. In this study, an NTO weight of approximately 10 appears to be ideal and eliminates the need to use rings in the treatment plan. Finally, no correlation was found between the leaf open/close time cutoff and the delivery accuracy, while a leaf open/close cutoff of 60 ms seemed to degrade dosimetry quality. Conclusion: Optimal values for the AT, LOT cutoff, NTO weight, and number of optimization rounds were identified and should help improve the management of patients whose tomotherapy treatments are planned with VOLOTM Ultra.

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Section: Synthesis and Suggested Planning Parameter Valuesmentioning

confidence: 95%

Section: Impact Of the Lot Cutoff On The Delivery Accuracymentioning

confidence: 99%

Influence of the Planning Parameters of a New Algorithm on the Dosimetric Quality, Beam-On Time and Delivery Accuracy of Tomotherapy Plans

Burckbuchler,

Dehaynin,

Niederst

et al. 2024

Cancers

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“…This stage involves all elements of CLC Al-Madinah, such as managers, teachers, students and work partners. This stage also refers to national non-formal education standards and community needs (Cavinato et al, 2023). This stage aims to determine the vision, mission, goals, objectives, strategies, programs and activities of CLC Al-Madinah.…”

Section: Planningmentioning

confidence: 99%

Quality Assurance Model for Non-formal Institutions at Community Learning Center Al-Madinah, Kediri City

Abdurochman,

Zulkarnain,

Hardika

et al. 2023

kolokium

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This article examines the quality assurance model implemented by Community Learning Center (CLC) Al-Madinah in Kediri City. This research uses a qualitative approach with a case study method. Research data was collected through observation, interviews and documentation techniques. Research data was analyzed using an interactive analysis model. The research results show that CLC Al-Madinah implements an internal quality assurance model that is participatory, collaborative and sustainable. This internal quality assurance model includes four stages, namely planning, implementation, assessment and improvement. This internal quality assurance model has several advantages and disadvantages that need to be considered and improved by CLC Al-Madinah and other non-formal institutions. This research provides several recommendations for CLC Al-Madinah and other non-formal institutions in improving the quality of educational services. This study also has several limitations that need to be addressed by future research.

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“…Many authors have been developing methods to predict the GPR from a model developed by learning characteristics of the treatment plan, such as the complexity metric, [3][4][5][6][7] dose uncertainty potential (DUP), 8,9 machine learning, [10][11][12][13][14][15][16] and deep learning techniques. [17][18][19][20][21] The achievement of the predicting model is evaluated using such as a standard deviation (SD) of the difference between the measured GPR [m] and the predicted GPR [p], [8][9][10][11][12]18 and Pearson's correlation coefficient (CC) of the (m, p) pairs.…”

Section: Introductionmentioning

confidence: 99%

Unbiased evaluation of predicted gamma passing rate by an event‐mixing technique

Koganezawa,

Matsuura,

Kawahara

et al. 2023

Medical Physics

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BackgroundPredicting models of the gamma passing rate (GPR) have been studied to substitute the measurement‐based gamma analysis. Since these studies used data from different radiotherapy systems comprising TPS, linear accelerator, and detector array, it has been difficult to compare the performances of the predicting models among institutions with different radiotherapy systems.PurposeWe aimed to develop unbiased scoring methods to evaluate the performance of the models predicting the GPR, by introducing both best and worst limits for the performance of the GPR prediction.MethodsTwo hundred head‐and‐neck VMAT plans were used to develop a framework. The GPRs were measured using the ArcCHECK device. The predicted GPR [p] was generated using a deep learning‐based model [pDL]. The predicting model was evaluated using four metrics: standard deviation (SD) [σ], Pearson's correlation coefficient (CC) [r], mean squared error (MSE) [s], and mean absolute error (MAE) [a]. The best limit [, , , and ] was estimated by measuring the SD of measured GPR [m] by shifting the device along the longitudinal direction to measure different sampling points. Mimicked best and worst p’s [pbest and pworst] were generated from pDL. The worst limit was defined such that m and p have no correlation [CC ∼ 0]. The worst limit [σMix, rMix, sMix, and aMix] was generated using the event‐mixing (EM) technique originally introduced in high‐energy physics experiments. The range of σ, r, s, and a was defined to be , , , and . The achievement score (AS) independently based on σ, r, s, and a were calculated for pDL, pbest and pworst. The probability that p fails the gamma analysis (alert frequency; AF) was estimated as a function of values within the [, σMix] range for the 3%/2 mm data with a 95% criterion.ResultsSDs of the best limit were well reproduced by . The EM technique successfully generated the pairs with no correlation. The AS using four metrics showed good agreement. This agreement indicates successful definitions of both best and worst limits, consistent definitions of the AS, and successful generations of mixed events. The AF for the DL‐based model with the 3%/2 mm tolerance was 31.5% and 63.0% with CL's 99% and 99.9%, respectively.ConclusionWe developed the AS to evaluate the predicting model of the GPR in an unbiased manner by excluding the effects of the precision of the radiotherapy system and the spreading of the GPR. The best and worst limits of the GPR prediction were successfully generated using the measured precision of the GPR and the EM technique, respectively. The AS and are expected to enable objective evaluation of the predicting model and setting exact achievement goal of precision for the predicted GPR.

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Prediction models as decision-support tools for virtual patient-specific quality assurance of helical tomotherapy plans

Cited by 4 publications

References 27 publications

Influence of the Planning Parameters of a New Algorithm on the Dosimetric Quality, Beam-On Time and Delivery Accuracy of Tomotherapy Plans

Influence of the Planning Parameters of a New Algorithm on the Dosimetric Quality, Beam-On Time and Delivery Accuracy of Tomotherapy Plans

Quality Assurance Model for Non-formal Institutions at Community Learning Center Al-Madinah, Kediri City

Unbiased evaluation of predicted gamma passing rate by an event‐mixing technique

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