Do We Need to Add the Type of Treatment Planning System, Dose Calculation Grid Size, and CT Density Curve to Predictive Models?

IF 3 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Reza Reiazi, Surendra Prajapati, Leonardo Che Fru, Dongyeon Lee, Mohammad Salehpour
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引用次数: 0

Abstract

Background: Generalizability and domain dependency are critical challenges in developing predictive models for healthcare, particularly in medical diagnostics and radiation oncology. Predictive models designed to assess tumor recurrence rely on comprehensive and high-quality datasets, encompassing treatment planning parameters, imaging protocols, and patient-specific data. However, domain dependency, arising from variations in dose calculation algorithms, computed tomography (CT) density conversion curves, imaging modalities, and institutional protocols, can significantly undermine model reliability and clinical utility. Methods: This study evaluated dose calculation differences in the head and neck cancer treatment plans of 19 patients using two treatment planning systems, Pinnacle 9.10 and RayStation 11, with similar dose calculation algorithms. Variations in the dose grid size and CT density conversion curves were assessed for their impact on domain dependency. Results: Results showed that dose grid size differences had a more significant influence within RayStation than Pinnacle, while CT curve variations introduced potential domain discrepancies. The findings underscore the critical role of precise and standardized treatment planning in enhancing the reliability of predictive modeling for tumor recurrence assessment. Conclusions: Incorporating treatment planning parameters, such as dose distribution and target volumes, as explicit features in model training can mitigate the impact of domain dependency and enhance prediction accuracy. Solutions such as multi-institutional data harmonization and domain adaptation techniques are essential to improve model generalizability and robustness. These strategies support the better integration of predictive modeling into clinical workflows, ultimately optimizing patient outcomes and personalized treatment strategies.

背景:通用性和领域依赖性是开发医疗保健预测模型的关键挑战,尤其是在医疗诊断和放射肿瘤学领域。用于评估肿瘤复发的预测模型依赖于全面、高质量的数据集,包括治疗计划参数、成像方案和患者特定数据。然而,由于剂量计算算法、计算机断层扫描(CT)密度转换曲线、成像模式和机构协议的不同而产生的领域依赖性,会严重影响模型的可靠性和临床实用性。方法:本研究评估了使用两种治疗计划系统(Pinnacle 9.10 和 RayStation 11)的 19 名患者头颈癌治疗计划中的剂量计算差异,这两种系统的剂量计算算法相似。评估了剂量网格大小和 CT 密度转换曲线的变化对域依赖性的影响。结果显示结果表明,RayStation 中剂量网格大小的差异比 Pinnacle 中的影响更大,而 CT 曲线的变化则带来了潜在的域差异。这些发现强调了精确和标准化的治疗规划在提高肿瘤复发评估预测建模的可靠性方面的关键作用。结论:将剂量分布和靶体积等治疗规划参数作为明确特征纳入模型训练,可减轻领域依赖性的影响并提高预测准确性。多机构数据协调和领域适应技术等解决方案对于提高模型的通用性和稳健性至关重要。这些策略有助于将预测建模更好地融入临床工作流程,最终优化患者预后和个性化治疗策略。
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
Diagnostics
Diagnostics Biochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
4.70
自引率
8.30%
发文量
2699
审稿时长
19.64 days
期刊介绍: Diagnostics (ISSN 2075-4418) is an international scholarly open access journal on medical diagnostics. It publishes original research articles, reviews, communications and short notes on the research and development of medical diagnostics. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodological details must be provided for research articles.
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