Sevilla-Moreno Andrés Camilo, Puerta-Yepes María Eugenia, Wahl Niklas, Benito-Herce Rafael, Cabal-Arango Gonzalo
School of Applied Sciences and Engineering, Universidad EAFIT, Medellín 050022, Colombia.
Division of Medical Physics in Radiation Oncology, German Cancer Research Center, 69120 Heidelberg, Germany.
Cancers (Basel). 2025 Feb 3;17(3):504. doi: 10.3390/cancers17030504.
: 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. : 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. The θ parameter 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. : 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. : 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.
癌症仍然是全球主要的死亡原因之一，放射治疗在其治疗中起着关键作用。调强放射治疗（IMRT）能够在保护健康组织的同时精确地向肿瘤输送剂量。然而，诸如患者定位误差和解剖变形等几何不确定性会影响治疗精度。传统方法使用安全边界，这可能导致对健康器官的过度照射或肿瘤覆盖不足。稳健优化技术，如极小极大方法，试图解决这些不确定性，但可能导致治疗计划过于保守。本研究介绍了一种基于区间分析的IMRT优化模型，为不确定性管理提供了一种更灵活的方法。
所提出的模型使用区间剂量影响矩阵来表示几何不确定性，并结合贝托卢扎度量来平衡肿瘤覆盖和危及器官（OAR）保护。θ参数允许进行可控的稳健性调制。该模型在开源治疗计划系统matRad中实现，并在五个前列腺癌病例上进行了评估。将结果与传统的计划靶区（PTV）和极小极大稳健优化方法进行了比较。
与PTV相比，基于区间的模型将肿瘤覆盖率提高了5.8%，同时将膀胱剂量降低了4.2%。相比之下，极小极大稳健优化将肿瘤覆盖率提高了25.8%，但膀胱剂量增加了23.2%。基于区间的方法在肿瘤覆盖和OAR保护之间提供了更好的平衡，证明了其在不过度保守的情况下提高治疗效果的潜力。
本研究提出了一种用于IMRT计划的新框架，通过区间分析改进了不确定性管理。通过允许可调的稳健性调制，所提出的模型能够实现更个性化和临床适应性更强的治疗计划。这些发现突出了区间分析作为优化放射治疗结果、平衡治疗效果和患者安全的有力工具的潜力。
