Effectiveness of robust optimization against geometric uncertainties in TomoHelical planning for prostate cancer

Yagihashi, Takayuki; Inoue, Kazuaki; Nagata, Hironori; Yamanaka, M.; Yamano, Akihiro; Suzuki, Satoshi; Yamakabe, Wataru; Sato, Naoki; Omura, Motoko; Inoue, Tatsuya

doi:10.1002/acm2.13881

Cited by 6 publications

(3 citation statements)

References 55 publications

(72 reference statements)

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“…This could be due to several reasons, including more stringent dose constraints were set and the DTF parameter in A-TOMO plans was set to 1.7, which made the execution time of each rotation approximately 20 seconds. A previous study indicated that increasing the DTF, while improving plan quality, led to longer dose delivery times [37] . Additionally, compared to TOMO plans, VMAT plans had shorter dose delivery times, consistent with previous studies [38] .…”

Section: Discussionmentioning

confidence: 99%

Development and validation of an automated Tomotherapy planning method for cervical cancer

Han,

Xue,

Huang

et al. 2024

Preprint

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Purpose This study aimed to develop an automated Tomotherapy (TOMO) planning method for cervical cancer treatment, and to validate its feasibility and effectiveness. Materials and Methods The study enrolled 30 cervical cancer patients treated with TOMO at our center. Utilizing scripting and Python environment within the RayStation (RaySearch Labs, Sweden) treatment planning system (TPS), we developed automated planning methods for TOMO and volumetric modulated arc therapy (VMAT) techniques. The clinical manual TOMO (M-TOMO) plans for the 30 patients were re-optimized using automated planning scripts for both TOMO and VMAT, creating automated TOMO (A-TOMO) and automated VMAT (A-VMAT) plans. we compared it with M-TOMO and A-VMAT plans. The primary evaluated relevant dosimetric parameters and treatment plan efficiency were assessed using the two-sided Wilcoxon signed-rank test for statistical analysis,with a P-value < 0.05 indicating statistical significance. Results A-TOMO plans maintained similar target dose uniformity compared to M-TOMO plans, with improvements in target conformity and faster dose drop-off outside the target, and demonstrated significant statistical differences (P+<0.01). A-TOMO plans also significantly outperformed M-TOMO plans in reducing V50Gy, V40Gy, and Dmean for the bladder and rectum, as well as Dmean for the bowel bag, femoral heads, and kidneys (all P+<0.05). Additionally, A-TOMO plans demonstrated better consistency in plan quality. Furthermore, the quality of A-TOMO plans was comparable to or superior than A-VMAT plans. In terms of efficiency, A-TOMO significantly reduced the time required for treatment planning to approximately 20 minutes. Conclusion We have successfully developed an A-TOMO planning method for cervical cancer. Compared to M-TOMO plans, A-TOMO plans improved target conformity and reduced radiation dose to OARs. Additionally, the quality of A-TOMO plans was on par with or surpasses that of A-VMAT plans. The A-TOMO planning method significantly improved the efficiency of treatment planning.

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Section: Discussionmentioning

confidence: 99%

Development and validation of an automated Tomotherapy planning method for cervical cancer

Han,

Xue,

Huang

et al. 2024

Preprint

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“…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].…”

Section: (C) Prostate Cancermentioning

confidence: 99%

“…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.…”

Section: Treatment Planningmentioning

confidence: 99%

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

Yagihashi

Inoue

Shiba

et al. 2023

Preprint

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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.

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Effectiveness of robust optimization against geometric uncertainties in TomoHelical planning for prostate cancer

Yagihashi

Inoue

Nagata

et al. 2022

J Applied Clin Med Phys

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Background Geometrical uncertainties in patients can severely affect the quality of radiotherapy. Purpose We evaluated the dosimetric efficacy of robust optimization for helical intensity‐modulated radiotherapy (IMRT) planning in the presence of patient setup uncertainty and anatomical changes. Methods Two helical IMRT plans for 10 patients with localized prostate cancer were created using either minimax robust optimization (robust plan) or a conventional planning target volume (PTV) margin approach (PTV plan). Plan robustness was evaluated by creating perturbed dose plans with setup uncertainty from isocenter shifts and anatomical changes due to organ variation. The magnitudes of the geometrical uncertainties were based on the patient setup uncertainty considered during robust optimization, which was identical to the PTV margin. The homogeneity index, and target coverage (TC, defined as the V100% of the clinical target volume), and organs at risk (OAR; rectum and bladder) doses were analyzed for all nominal and perturbed plans. A statistical t ‐test was performed to evaluate the differences between the robust and PTV plans. Results Comparison of the nominal plans showed that the robust plans had lower OAR doses and a worse homogeneity index and TC than the PTV plans. The evaluations of robustness that considered setup errors more than the PTV margin demonstrated that the worst‐case perturbed scenarios for robust plans had significantly higher TC while maintaining lower OAR doses. However, when anatomical changes were considered, improvement in TC from robust optimization was not observed in the worst‐case perturbed plans. Conclusions For helical IMRT planning in localized prostate cancer, robust optimization provides benefits over PTV margin–based planning, including better OAR sparing, and increased robustness against systematic patient‐setup errors.

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Effectiveness of robust optimization against geometric uncertainties in TomoHelical planning for prostate cancer

Cited by 6 publications

References 55 publications

Development and validation of an automated Tomotherapy planning method for cervical cancer

Development and validation of an automated Tomotherapy planning method for cervical cancer

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

Effectiveness of robust optimization against geometric uncertainties in TomoHelical planning for prostate cancer

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