Evidence map›Paper›PMID 37653551›Full record

ArticleEuropean journal of medical research2023

Study on the transferability of the knowledge-based VMAT model to predict IMRT plans in prostate cancer radiotherapy.

Suyan Bi, Xingru Sun, Wan Fatihah Binti Wan Sohaimi, Ahmad Lutfi Bin Yusoff

Open access · goldAbstract read
In one paragraph

Article in European journal of medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
–field-weighted citation impact, top 85% of its field
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed, 0 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors at 2 institutions in 3 countries.

Suyan Bi *Department of Nuclear Medicine, Radiotherapy & Oncology, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, 16150, Kubang Kerian, Kelantan, Malaysia.
Xingru Sun *Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, Guangdong, China.
Wan Fatihah Binti Wan SohaimiHospital Universiti Sains Malaysia, Health Campus, 16150, Kubang Kerian, Kelantan, Malaysia. fatihahkk@usm.my.
Ahmad Lutfi Bin YusoffHospital Universiti Sains Malaysia, Health Campus, 16150, Kubang Kerian, Kelantan, Malaysia. alutfi@usm.my.
Hospital Universiti Sains Malaysia · MYChinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

Shenzhen Key Medical Discipline Construction Fund SZXK013Youth Start-up Fund of Shenzhen Cancer Hospital E010321021
6 · The paper itself

Abstract

objectiveThe aim of this study was to investigate the feasibility of VMAT library-derived model transfer in the prediction of IMRT plans by dosimetry comparison among with three groups of IMRT plans: two groups of automatic IMRT plans generated by the knowledge-based the volumetric modulated arc therapy (VMAT) model and intensity-modulated radiation therapy (IMRT) model and one group of manual IMRT plans.

methods52 prostate cancer patients who had completed radiotherapy were selected and randomly divided into 2 groups with 40 and 12 separately. Then both VMAT and IMRT plans were manually designed for all patients. The total plans in the group with 40 cases as training datasets were added to the knowledge-based planning (KBP) models for learning and finally obtained VMAT and IMRT training models. Another 12 cases were selected as the validation group to be used to generated auto IMRT plans by KBP VMAT and IMRT models. At last, the radiotherapy plans from three groups were obtained: the automated IMRT plan (V-IMRT) predicted by the VMAT model, the automated IMRT plan (I-IMRT) predicted by the IMRT model and the manual IMRT plan (M-IMRT) designed before. The dosimetric parameters of planning target volume (PTV) and organ at risks (OARs) as well as the time parameters (monitor unit, MU) were statistically analyzed.

resultsThe dose limit of all plans in the training datasets met the clinical requirements. Compared with the training plans added to VMAT model, the dosimetry parameters have no statistical differences in PTV (P > 0.05); the dose of X% volume (Dx%) with D25% and D35% in rectal and the maximum dose (Dmax) in the right femoral head were lower (P = 0.04, P = 0.01, P = 0.00) while D50% in rectal was higher (< 0.05) in the IMRT model plans. In the 12 validation cases, both automated plans showed better dose distribution compared with the M-IMRT plan: the Dmax of PTV in the I-IMRT plans and the dose in volume of interesting (VOI) of bladder and bilateral femoral heads were lower with a statistically significant difference (P < 0.05). Compared with the I-IMRT plans, dosimetric parameters in PTV and VOI of all OARs had no statistically significant differences (P > 0.05), but the Dmax in left femoral heard and D15% in the right femoral head were lower and have significant differences (P < 0.05). Furthermore, the low-dose regions, which was defined as all volumes outside of the PTV (RV) with the statistical parameters of mean dose (Dmean), the volume of covering more than 5 Gy dose (V5Gy), and also the time parameter (MU) required to perform the plan were considered. The results showed that Dmean in V-IMRT was smaller than that in the I-IMRT plan (P = 0.02) and there was no significant difference in V5Gy and MU (P > 0.05).

conclusionCompared with the manual plan, the IMRT plans generated by the KBP models had a significant advantage in dose control of both OARs and PTV. Compared to the I-IMRT plans, the V-IMRT plans was not only without significant disadvantages, but it also achieved slightly better control of the low-dose region, which meet the clinical requirements and can used in the clinical treatment. This study demonstrates that it is feasible to transfer the KBP VMAT model in the prediction of IMRT plans.

Indexed as

Prostatic NeoplasmsRadiotherapy, Intensity-ModulatedFemurHumansKnowledgeMaleIMRT planningKnowledge-based planningModel transferProstate radiotherapyVMAT automated planning model

Identifiers

PMID37653551
PMCPMC10469823
OpenAlexW4386328564

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.