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. 2025 May 1;17(5):e83330.
doi: 10.7759/cureus.83330. eCollection 2025 May.

Investigation of Dosimetric Evaluation and Treatment Planning Time of Inverse Planning Optimization in Combined Intracavitary and Interstitial Brachytherapy for Cervical Cancer

Yoshifumi Oku  1 , Souichirou Itou  2 , Shigeyoshi Wakamatsu  1 , Yuushi Niiyama  1 , Masahiko Toyota  1
Affiliations

Investigation of Dosimetric Evaluation and Treatment Planning Time of Inverse Planning Optimization in Combined Intracavitary and Interstitial Brachytherapy for Cervical Cancer

Yoshifumi Oku et al. Cureus. .

Abstract

Clinical evidence demonstrating the effectiveness of optimization and efficiency of treatment plan is limited because the Inverse planning optimization of source position and dwell time variations is complex. Our purpose was to investigate the comparison of the dosimetric evaluations and treatment planning time in two inverse planning algorithms with the conventional Manchester treatment planning for cervical cancer brachytherapy. We retrospectively identified 14 patients who underwent manually and inversely optimized treatment plans using inverse planning simulated annealing (IPSA) and hybrid inverse planning optimization (HIPO). The analysis was performed to analyze the effects of various factors on the dosimetric evaluation indices, such as the D90 for the high-risk clinical target volume (HR-CTV) and D2cc of the organ at risk (OAR), and the distribution of dwell time and optimization time in each algorithm. In most plans, D90 of the HR-CTV exceeded 7 Gy, and the D2cc of the OARs, on average, was below the tolerance dose for all plans. However, the HR-CTV D90 and D2cc of the IPSA-optimized treatment plan tended to be smaller than those of the other plans when the dwell time deviation constraint value of the optimization parameters was increased. The treatment plans used in the Manchester method and those obtained by IPSA and HIPO have similar dose distributions and dose volume histogram parameters. Moreover, the time required to create a treatment plan was reduced by the IPSA and HIPO. Also, it was suggested that IPSA may result in extreme source dwell positions and dwell times.

Keywords: cervical cancer; hipo; inverse planning; ipsa; optimization.

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Conflict of interest statement

Human subjects: Consent for treatment and open access publication was obtained or waived by all participants in this study. Kagoshima University Hospital, Kagoshima, Japan issued approval 230071 Epi. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1. Overall scheme of the workflow for the inverse planning optimization.
Figure 2
Figure 2. Box plots of the dosimetric parameters for the optimization methods.
IPSA: inverse planning simulated annealing, HIPO: hybrid inverse planning optimization, DTDC: dwell time deviation constraint, DTGR: dwell time gradient restriction
Figure 3
Figure 3. Dose distribution for each technique.
Axial dose distributions made by each method are shown, respectively. A dotted line indicates HR-CTV (yellow). IPSA: inverse planning simulated annealing, HIPO: hybrid inverse planning optimization
Figure 4
Figure 4. Distribution of dwell times due to different optimization methods.
IPSA: inverse planning simulated annealing, HIPO: hybrid inverse planning optimization
Figure 5
Figure 5. Optimization times due to different optimization methods.
*Kruskal-Walli's test; P<0.05 IPSA: inverse planning simulated annealing, HIPO: hybrid inverse planning optimization
See this image and copyright information in PMC

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