Improving Radiotherapy Plan Quality for Nasopharyngeal Carcinoma With Enhanced UNet Dose Prediction

IF 3.1 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2025-02-15 DOI:10.1002/cam4.70688

Junming Jian, Xingxing Yuan, Longfei Xu, Changfei Gong, Xiaochang Gong, Yun Zhang

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引用次数: 0

Abstract

Background

Individualized dose prediction is critical for optimizing radiation treatment planning. This study introduces DESIRE, an enhanced UNet-based dose prediction model with progrEssive feature fuSion and dIfficult Region lEarning, tailored for nasopharyngeal carcinoma (NPC) patients receiving volumetric modulated arc therapy. We aimed to assess the impact of integrating DESIRE into the treatment planning process to improve plan quality.

Methods

This retrospective study included 131 NPC patients diagnosed at Jiangxi Cancer Hospital between 2017 and 2020. Twenty patients were randomly allocated to a testing cohort, while the remaining 111 comprised a training cohort. Target delineation included three planning target volumes (PTVs): PTV70, PTV60, and PTV55, along with several organs at risk (OARs). The DESIRE model predicted dose distributions, and discrepancies between DESIRE's predictions and the ground truth (GT) were quantified using dosimetric metrics and gamma pass rates. Two junior physicians used DESIRE's predictions for treatment planning, and their plans were compared to the GT.

Results

Most of DESIRE's predicted dosimetric metrics closely aligned with GT (mean difference < 1 Gy), with no significant differences (p > 0.05) in D_mean and D₁ values across OARs. While significant differences were observed in PTV metrics, the mean differences in D₉₈, D₉₅, D₅₀, and D_mean between DESIRE and GT did not exceed 1 Gy. Assisted by DESIRE, the junior physicians' plans were comparable to the GT in nearly all OARs, with no significant differences in dosimetric metrics. The conformity index (CI) and homogeneity index (HI) for PTV70 surpassed the GT (0.847 ± 0.036 vs. 0.827 ± 0.037 for CI, and 0.057 ± 0.009 vs. 0.052 ± 0.008 for HI). The average three-dimensional gamma passing rates were 0.85 for PTV70 and 0.87 for the 35-Gy isodose line.

Conclusions

The DESIRE model shows promise for patient-specific dose prediction, enhancing junior physicians' treatment planning capabilities and improving plan quality.

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应用增强UNet剂量预测提高鼻咽癌放疗计划质量

背景个体化剂量预测是优化放射治疗计划的关键。本研究介绍了DESIRE，一种基于unet的增强剂量预测模型，具有渐进式特征融合和困难区域学习，专为接受体积调节弧治疗的鼻咽癌（NPC）患者量身定制。我们的目的是评估将DESIRE纳入治疗计划过程以提高计划质量的影响。方法回顾性研究2017 - 2020年在江西省肿瘤医院诊断的131例鼻咽癌患者。20名患者被随机分配到测试队列，而剩下的111名患者被分配到训练队列。靶区划分包括三个规划靶区（PTVs）： PTV70、PTV60和PTV55，以及几个危险器官（OARs）。DESIRE模型预测剂量分布，DESIRE预测与基础真实（GT）之间的差异使用剂量计量学和伽马通过率进行量化。两名初级医生使用DESIRE的预测来制定治疗计划，并将他们的计划与GT进行比较。结果大多数DESIRE预测的剂量学指标与GT密切相关（平均差值<； 1 Gy），各OARs的Dmean和D1值无显著差异（p > 0.05）。虽然在PTV指标上观察到显著差异，但DESIRE和GT之间的D98、D95、D50和Dmean的平均差异不超过1 Gy。在DESIRE的帮助下，初级医生的计划几乎在所有桨中都与GT相当，剂量学指标没有显着差异。PTV70的符合性指数（CI）和均匀性指数（HI）均优于GT （CI为0.847±0.036比0.827±0.037，HI为0.057±0.009比0.052±0.008）。PTV70的平均三维伽马通过率为0.85,35-Gy等剂量线的平均三维伽马通过率为0.87。结论DESIRE模型可用于患者特异性剂量预测，提高初级医师的治疗计划能力，提高计划质量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.