Impact of delivery time factor on treatment time and plan quality in tomotherapy

doi:10.21203/rs.3.rs-2694774/v1

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Impact of delivery time factor on treatment time and plan quality in tomotherapy

https://doi.org/10.21203/rs.3.rs-2694774/v1

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Background

The delivery time factor (DTF) is a new parameter introduced by the RayStation treatment planning system for tomotherapy treatment planning. This study investigated the effects of this factor on various tomotherapy plans.

Methods

Overall, 25 patients with cancer (head and neck, 6; lung, 9; prostate, 10) were enrolled in this study. For these patients, helical tomotherapy plans with a field width of 2.5 cm, pitch of 0.287, and DTF of 2.0 were created. All the initial plans were recalculated by changing the DTF parameter from 1.0 to 3.0 in increments of 0.1 (21 plans per patient). For each plan, we calculated the following evaluation indices: the treatment time, modulation factor, conformity index (CI), homogeneity index (HI), and doses to critical organs at risk (OARs). For every index, the values normalized by the corresponding values with a DTF of 1.0 were plotted, and fitted curves with polynomial functions were created as a function of the DTF. These curves were used to evaluate the impact of the DTF on both delivery efficiency and plan quality.

Results

Treatment time and modulation factor increased monotonically with increasing DTF. Increasing the DTF by 0.1 resulted in an increase in the treatment time and modulation factor by almost 10%. This relationship was similar for all treatment sites. An increase in DTF was found to improve CI, HI, and OAR doses compared to plans with a DTF of 1.0, except for the CI in the lung cancer case. However, the improvement in most indices ceased at a certain DTF; nevertheless, treatment time continued to increase in accordance with an increase in DTF.

Conclusions

DTF is a critical parameter for improving the quality of tomotherapy plans; however, increase of this factor above a certain level leads to an unnecessary increase in treatment time and deterioration of plan quality.

Trial registration:

None.

delivery time factor

helical tomotherapy

plan quality

treatment time

Radixact

RayStation

Tomotherapy (Accuray Inc., Sunnyvale, CA, USA) is an intensity-modulated radiotherapy (IMRT) technology that uses a 6 MV linear accelerator mounted on a slip-ring-gantry that moves in synchronization with a treatment couch [1]. Equipped megavolt computed tomography (CT) systems can be used for pretreatment patient setup verification, dose recalculation, and patient and machine quality assurance purposes [1–3]. The machine enables the delivery of a highly conformal dose to complex target volumes while minimizing the dose to critical normal tissues by slice-by-slice delivery with 64 binary multileaf collimators. In addition, the latest unit “Radixact” has improved the treatment time efficiency with respect to imaging and delivery compared to previous versions [4]. Many studies have demonstrated that tomotherapy is suitable for the treatment of a wide spectrum of anatomical sites, including the brain [5, 6], head and neck [7–9], lung [10–12], breast [13, 14], abdomen and pelvis [15–17], and prostate [18–20].

Treatment plan quality depends on the performance of the optimizer and the selection of appropriate planning parameters in the treatment planning system (TPS). Tomotherapy has specific planning parameters such as field width (FW), pitch, and modulation factor (MF), which can affect the quality of plans and treatment times. The FW is defined as the longitudinal thickness of the fan beam. Pitch is defined as the couch travel distance for a complete gantry rotation relative to the axial beam width at the axis of rotation. Generally, a pitch of 0.86/n (n is an integer) is used to mitigate the thread effect [21]. The MF is defined as the maximum leaf opening time divided by the average (non-zero) leaf opening time and is an estimate of plan complexity [22, 23]. Increasing the MF enables larger beam modulation resulting in high-quality plans; however, this leads to increased treatment time [4, 24]. In clinical practice, the selection of these parameters is unique to the treatment site and represents a compromise between longer treatment times with superior dose distribution and shorter treatment times with poorer dose distribution. Consequently, planning is subject to the planner’s experience, and the created plans can be biased [25].

Recently, RayStation TPS (RaySearch Lab; Stockholm, Sweden) has implemented a dose calculation module for tomotherapy. In the optimization process, three different types of delivery time constraints are supported: 1) delivery time factor (DTF), 2) maximum gantry period, and 3) maximum delivery time, while the MF is unavailable [26]. A DTF of x, where x has a value of 1.0 to 5.0, means that the delivery time cannot exceed x × t. The delivery time (t) denotes the time when the leaf opening times are uniform and scaled so that the average target dose is equal to the prescription dose. As this factor is associated with multileaf collimators, it is expected to be related to the delivery efficiency and plan quality as well as MF. Thus, understanding the impact of the DTF on the treatment planning could influence the decision to adjust it to reduce the number of trial-and-error attempts during planning. However, to the best of our knowledge, no study has investigated the impacts of the use of DTF in treatment planning.

The purpose of this study was to evaluate the impact of changes in DTF in helical tomotherapy plans for head and neck, lung, and prostate cancers on target dose conformity and homogeneity, organs at risk (OARs) doses, and treatment time.

This retrospective study was approved by the Institutional Review Board of Shonan Kamakura General Hospital (No. 2035). Overall, 25 patients were enrolled: six with head and neck cancer, nine with lung cancer, and ten with prostate cancer. These patients were previously treated with helical IMRT, which was planned using tomotherapy-dedicated TPS Precision (Accuray, Inc., Madison, Wisconsin, USA) between January 2021 and July 2022 at Shonan Kamakura General Hospital. The patient characteristics are summarized in Table 1.

Structure contouring

All CT images were obtained using a Siemens 20-slice CT scanner (Somatom Confidence, Siemens, Germany) with a reconstruction resolution of 0.967 × 0.967 × 2 mm³. Radiation oncologists with more than 10 years of experience delineated the target volume and OARs using RayStation version 10A.

(a) Head and neck cancer

TNM classification was done using magnetic resonance imaging (MRI). These MRI images were registered with planning CT. According to the pan endoscopy information and the imaging entity, the gross tumor volume (GTV), the high-risk clinical target volume (CTV) involved 5 mm expansion from the GTV; intermediate-risk CTV involved high-risk CTV with at-risk lymphatic drainage in the node-positive region; and low-risk CTV involved low-risk lymphatic regions were delineated. The corresponding planning target volumes (PTV_low, PTV_intermediate, and PTV_high) were created by adding an isotropic 5 mm margin to the CTVs. The PTV volumes in Table 1 are the PTV_high volumes. Both parotid glands, the brainstem, and the spinal cord were defined as the OARs.

(b) Lung cancer

For patients with lung cancer, four-dimensional CT was obtained using a respiration management system (Abches ET®; APEX Medical, Japan). The images were separated into 10 phases, which equally divided the breathing cycles. Thereafter, the GTV was delineated for each after referring to all available clinical information. The internal target volume (ITV) was defined as the union of 10 GTVs in each phase. The CTV was created by the expansion of the ITV by a 5 mm margin in all directions, and non-invasive and bone regions were excluded from the CTV. The PTV was created by the expansion of the CTV by a 5 mm margin. The entire lung, esophagus, heart, and spinal cord were defined as OARs. These delineations were consequently performed on maximum inspiration CT.

The CTV, rectum, and bladder were delineated for treatment planning. The PTV consisted of the CTV with 8 mm margins added in each direction, except for the posterior direction in which a 5 mm margin was added. The contouring details for prostate cancer have been previously described [27].

Treatment planning

Treatment planning was performed by a medical physicist using the RayStation commissioned for Radixact. First, helical tomotherapy plans were optimized using a FW of 2.5 cm, pitch of 0.287, and a DTF of 2.0. For head and neck plans, the prescription doses were 77, 70, and 56 Gy in 35 fractions for PTV_high, PTV_intermediate, and PTV_low, respectively [28]. The dose was normalized using 95% of the target volume with 77 Gy. The prescription dose was 66 Gy in 33 fractions, which covered at least 99% of the PTV by 95% of the prescription dose for lung plans [29]. The plans for prostate cancer were optimized to achieve a prescription dose of 60 Gy in 20 fractions covering 95% of the PTV [27, 30]. The dose constraints for the target volume and OARs for each treatment plan are shown in Table 2. In addition, the dose fall-off option was used for the volume up to 0.3 cm, and from 0.3 cm to 0.8 cm outside the PTV to control the dose fall-off for the volume outside the PTV. The objective weights were varied to spare the OAR doses as much as possible without compromising the target criteria. All plans were optimized using a dose grid size of 2 mm and 60 iterations with the final calculation every 20 iterations. After creating the initial plans, all plans were recalculated by changing the DTF parameter from 1.0 to 3.0 in increments of 0.1. For fair comparison purposes, the planning and optimization parameters, except for DTF, were kept identical to the initial plans. Totally, 525 treatment plans (21 per patient) were included in this study.

Data reporting and analysis

The Paddick conformity index (CI) [31] and homogeneity index (HI) [32] were calculated for all plans to evaluate the change in the quality of the plan with changes in the DTF parameter. The CI was calculated using Eq. (1):

$$\text{C}\text{I}= \frac{{\text{T}\text{V}}_{\text{P}\text{I}\text{V}}^{2}}{\left(\text{T}\text{V}\times {\text{V}}_{\text{R}\text{I}}\right)} \left(1\right)$$

where TV is the target volume, TV_PIV is the target volume covered by the prescribed dose, and V_RI is the total volume covered by the prescribed dose. The HI was calculated using Eq. (2):

$$\text{H}\text{I}= \frac{{\text{D}}_{2\text{\%}}-{\text{D}}_{98\text{\%}}}{{\text{D}}_{50\text{\%}}} \left(2\right)$$

where D_2%, D_98%, and D_50% are the doses covering 2, 98, and 50% of the target volume, respectively. Values close to 1 and 0 are the ideal values for the CI and HI, respectively. For the head and neck plans, these indices were calculated for PTV_high. For OARs, the maximum dose, defined as D_2% or the mean dose was calculated. The treatment time and MF for each plan were also gathered to evaluate the delivery efficiency.

Table 3 lists the mean values and standard deviations of the treatment time, MF, CI, HI, and OAR indices for each DTF plan.

Figure 1–3 show diagrams that plot every evaluation index normalized by the corresponding indices of the plans with a DTF of 1.0. Figure 4 shows fitted curves as a function of the DTF for each index. The curves that fit the plot data in Fig. 2–4 were created using a first-order polynomial for the treatment time and MF, and a fifth-order polynomial for the CI, HI, and OAR indices.

Table 4 summarizes the rate of change for each index on the fitted curves. The change of each normalized index between DTF x-0.1 and DTF x (x ranges from 1.1 to 3.0) is denoted “Δ”. Accum is the accumulation of the changing rates from a DTF of 1.1 to the corresponding DTF. The treatment time demonstrated an approximately linear correlation with DTF. The treatment time increased by almost 200% when using a DTF of 3.0 than when using a DTF of 1.0. The MF also exhibited the same trend as treatment time. In head and neck and prostate cancers, the CI improved by 0.9% and 1.9% when the DTF was 1.9 and 1.8, respectively, and did not improve thereafter. In contrast, the CI exhibited the best value when the DTF was 1.0 in lung cancer. In terms of HI, an increase in the DTF initially improved the index by 5.4% (head and neck cancer case; DTF 1.7), 9.0% (lung cancer case; DTF 1.6), and 1.9% (prostate cancer case; DTF 1.8), after which the improvement plateaued. The doses for both parotid glands in cases with head and neck cancer continued to decrease until a DTF of 3.0 was applied, and the rates of decrease were 6.9%. The maximum dose to the brainstem and cord decreased with an increase in DTF up to 2.4 and 1.7, and the decrease plateaued at higher DTF values. The decrease in the OAR doses for lung cases plateaued when the DTF was in the range of 1.5–2.3. The bladder and rectum doses were the lowest when the plan was created using a DTF of 2.4, and their doses decreased by 6.5% and 13.1%, respectively.

This study demonstrated that increasing the DTF by 0.1 results in increasing the treatment time by almost 10% when the treatment time for plans with a DTF of 1.0 is normalized to 100%. This relationship was similar for all treatment sites investigated in this study, and the same relationship was observed for the MF. Previous studies have shown that the MF has a direct effect on treatment time [4, 24, 25]. In general, a high MF results in increasing in treatment time although it provides superior dose distribution and lower doses in normal tissues. However, when the MF is small, the delivery time shortens, resulting in poorer dose conformity and homogeneity.

A higher DTF indicates that the leaf opening times become inhomogeneous, leading to an increase in the MF. In other words, a higher DTF allows for the use of a further modulated beam intensity, which could improve the quality of the treatment plan. As can be seen from our results, it has been observed that an increase in DTF can improve HI, CI, and OAR doses compared to plans with a DTF of 1.0, except for the CI in the lung cancer case. However, the improvement of indices plateaued at a certain DTF in this study except for the mean doses to both parotid glands in the head and neck plans. Nevertheless, treatment time and MF continued to increase linearly with increasing DTF. This finding indicated that excess DTF leads to an unnecessary increase in treatment time, consequently leading to disadvantages for patients and medical workers. However, the CI markedly deteriorated when the DTF increased above 1.0 in the lung cancer case. Unfortunately, we could not find a clear explanation for this finding; however, this may be caused by a trade-off between the target dose conformity and sparing doses to the surrounding OARs, such as the lung.

The relationship between the DTF and MF was also evaluated. The MF was approximately 2.0 when the DTF was 1.8, which shows a plateau in plan quality for prostate cancer cases. Nevertheless, this MF is reasonable and lower than that recommended in a previous study [33]. In contrast, when the DTF was within the range of 1.2–1.3 and 1.5–1.6 for head and neck and lung cases, respectively, the MF reached 3.0, which is recognized as a much higher value for head and neck plans [34]. A higher MF leads to greater plan complexity in tomotherapy, potentially leading to unacceptable dose delivery [22, 23]. Therefore, it is necessary to select a DTF with a good balance between treatment time and plan quality. We propose that a DTF of approximately 2.0 is suitable for prostate plans; however, for complex sites such as the head and neck, and lung, a lower DTF (1.3–1.5) should be used at the beginning of treatment planning, considering the treatment time and the improvement of plan quality.

Limitations of this study are that only a small number of patients were examined for each treatment site, and only specific sites were focused on. Another limitation is not to consider changes in planning parameters (FW and pitch) other than DTF. These indicate that our findings are limited to specific datasets. In addition, the percentage difference between the plans with different DTF values may not be always clinically relevant. For example, an 6.9% reduction in the mean dose for both parotid glands when changing the DTF from 1.0 to 3.0 corresponded to reduction of the dose from 14.7 Gy to 13.6 Gy, resulting in a dose difference of 1.1 Gy. Similarly, a 4.0% reduction in the mean dose to the lung corresponded to a dose reduction of only 0.3 Gy. Therefore, patient-specific selection of the DTF and dosimetric evaluation should be considered in treatment planning in each institution. Nevertheless, due to limited time, determining patient-specific DTF values might be time consuming in clinical practice. Thus, our results could help minimize the time required for fine-tuning DTF values and can serve as a planner’s reference in routine clinical practice. This study may provide a guideline for tomotherapy treatment planning, thus increasing the understanding of how DTF interacts with plan quality and delivery efficiency.

This study investigated the influence of a new planning parameter provided by RayStation TPS, the DTF, on helical tomotherapy plans for head and neck, lung, and prostate cancers. The results demonstrated that the DTF is a critical parameter for both plan quality and treatment time. Increasing the DTF led to a monotonous increase in the treatment time and MF while improving the dose conformity and homogeneity to the target volume and sparing the OARs. However, in some cases, the positive effect on plan quality was limited to certain DTF levels. Therefore, planners and radiation oncologists need to consider the appropriate trade-off between better plan quality and shorter treatment time when deciding on the adjustment of the DTF.

CI: Conformity index

CTV: Clinical target volume

DTF: Delivery time factor

FW: Field width

GTV: Gross tumor volume

HI: Homogeneity index

IMRT: Intensity-modulated radiotherapy

ITV: Internal target volume

MF: Modulation factor

OAR: Organ at risk

PTV: Planning target volume

TPS: Treatment planning system

Ethics approval and consent to participate: This study was approved by the institutional review board of Shonan Kamakura General Hospital (No. 2035). Given its retrospective nature, the review board waived the need for informed consent by offering an opt-out option on the institution’s homepage.

Consent for publication: Not applicable.

Availability of data and materials: The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Competing interests: The authors declare no competing interests.

Funding: Not applicable.

Authors' contributions: TY and TI made substantial contributions to the conception of this study. TY, AY, YM, and SS contributed significantly to data analysis and interpretation. KI, HN, MO and TI made significant contributions to the design of the study and interpretation of data. TY and TI drafted the manuscript. All the authors critically reviewed and revised the manuscript draft and approved the final version for submission.

Acknowledgements: The authors thank Mr. Kentaro Sugi (Hitachi Ltd., Japan) for technical support.

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Tables 1-4 is available in the Supplementary Files section.

No competing interests reported.

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Impact of delivery time factor on treatment time and plan quality in tomotherapy

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Abstract

Background

Methods

Results

Conclusions

Trial registration:

Figures

Background

Methods

Structure contouring

Treatment planning

Data reporting and analysis

Results

Discussion

Conclusions

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1