Background: Cancer remains one of the leading causes of mortality worldwide, with radiotherapy playing a crucial role in its treatment. Intensity-modulated radiotherapy (IMRT) enables precise dose delivery to tumors while sparing healthy tissues. However, geometric uncertainties such as patient positioning errors and anatomical deformations can compromise treatment accuracy. Traditional methods use safety margins, which may lead to excessive irradiation of healthy organs or insufficient tumor coverage. Robust optimization techniques, such as minimax approaches, attempt to address these uncertainties but can result in overly conservative treatment plans. This study introduces an interval analysis-based optimization model for IMRT, offering a more flexible approach to uncertainty management.Methods: The proposed model represents geometric uncertainties using interval dose influence matrices and incorporates Bertoluzza’s metric to balance tumor coverage and organ-at-risk (OAR) protection. Theθparameter allows controlled robustness modulation. The model was implemented in matRad, an open-source treatment planning system, and evaluated on five prostate cancer cases. Results were compared against traditional Planning Target Volume (PTV) and minimax robust optimization approaches.Results: The interval-based model improved tumor coverage by 5.8% while reducing bladder dose by 4.2% compared to PTV. In contrast, minimax robust optimization improved tumor coverage by 25.8% but increased bladder dose by 23.2%. The interval-based approach provided a better balance between tumor coverage and OAR protection, demonstrating its potential to enhance treatment effectiveness without excessive conservatism.Conclusions: This study presents a novel framework for IMRT planning that improves uncertainty management through interval analysis. By allowing adjustable robustness modulation, the proposed model enables more personalized and clinically adaptable treatment plans. These findings highlight the potential of interval analysis as a powerful tool for optimizing radiotherapy outcomes, balancing treatment efficacy and patient safety.
背景：癌症仍是全球主要致死原因之一，放射治疗在其治疗中发挥着关键作用。调强放射治疗（IMRT）能够实现对肿瘤的精准剂量投照，同时保护健康组织。然而，患者摆位误差和解剖形变等几何不确定性可能影响治疗精度。传统方法采用安全边界策略，可能导致健康器官过度照射或肿瘤靶区覆盖不足。鲁棒优化技术（如极小极大法）虽尝试解决此类不确定性，但易产生过度保守的治疗方案。本研究提出一种基于区间分析的IMRT优化模型，为不确定性管理提供更灵活的解决方案。 方法：该模型采用区间剂量影响矩阵表征几何不确定性，并引入Bertoluzza度量平衡肿瘤靶区覆盖与危及器官保护。通过θ参数实现可控的鲁棒性调节。模型在开源治疗计划系统matRad中实现，并基于五例前列腺癌病例进行评估。结果与传统计划靶区（PTV）方法和极小极大鲁棒优化方法进行对比。 结果：相较于PTV方法，区间模型使肿瘤靶区覆盖提升5.8%，同时膀胱受照剂量降低4.2%。而极小极大鲁棒优化虽使肿瘤靶区覆盖提升25.8%，但膀胱剂量增加23.2%。区间分析方法在靶区覆盖与危及器官保护间取得更优平衡，证明其能在不过度保守的前提下提升治疗效果。 结论：本研究提出的IMRT计划新框架通过区间分析改进了不确定性管理。该模型通过可调节的鲁棒性调制，实现了更具个性化和临床适应性的治疗方案。这些发现凸显了区间分析作为优化放疗效果的有力工具，在平衡治疗效能与患者安全方面的应用潜力。
Interval Analysis-Based Optimization: A Robust Model for Intensity-Modulated Radiotherapy (IMRT)
肿瘤(癌症)患者之家