结合放射组学和剂量组学与肺生物等效剂量预测肺部SBRT后症状性放射性肺炎:一项双中心研究

IF 5.3 1区 医学 Q1 ONCOLOGY
Yuxin Jiao , Yawen Wen , Shihong Li , Hongbo Gao , Di Chen , Li Sun , Guangwu Lin , Yanping Ren
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引用次数: 0

摘要

背景与目的本研究的重点是开发和验证基于肺生物等效剂量分割方法的综合放射组学和剂量组学特征的复合模型,以预测肺部SBRT后的症状性放射性肺炎(SRP)。材料和方法182例接受SBRT治疗的肺癌患者的双中心队列分为训练组、验证组和外部测试组。放射组学和剂量组学特征是从规划的计算机断层扫描(CT)图像和3D剂量分布图中的两个不同的兴趣区域(roi)中提取的,其中包括整个肺和生物等效剂量(BED)区域。特征选择包括相关滤波和LASSO正则化。五种机器学习算法生成了三个单独的模型(剂量-体积直方图[DVH]、放射组学[R]、剂量组学[D])和三个组合模型(R + DVH、R + D、R + D + DVH)。通过ROC分析、校准和决策曲线分析来评估绩效。结果在临床和剂量学因素中,肺VBED70 (α/β = 3 Gy)被认为是SRP的独立危险因素。基于bed的放射组学和剂量组学模型优于全肺模型(auc分别为0.806 vs. 0.674和0.821 vs. 0.647)。R + D + DVH三元模型具有最高的预测准确度(AUC: 0.889, 95% CI: 0.701-0.956),具有稳健的校准和临床实用性。结论基于肺生物等效剂量分割方法的R + D + DVH三元模型在预测各种SBRT分割方案的SRP方面优于其他模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating radiomics and dosiomics with lung biologically equivalent dose for predicting symptomatic radiation pneumonitis after lung SBRT: A dual-center study

Background and purpose

This study focused on developing and validating a composite model that integrates radiomic and dosiomic features based on a lung biologically equivalent dose segmentation approach to predict symptomatic radiation pneumonitis (SRP) following lung SBRT.

Materials and methods

A dual-centered cohorts of 182 lung cancer patients treated with SBRT were divided into training, validation, and external testing sets. Radiomic and dosiomic features were extracted from two distinct regions of interest (ROIs) in the planning computed tomography (CT) images and 3D dose distribution maps, which encompassed both the entire lung and biologically equivalent dose (BED) regions. Feature selection involved correlation filters and LASSO regularization. Five machine learning algorithms generated three individual models (dose-volume histogram [DVH], radiomic [R], dosiomic [D]) and three combined models (R + DVH, R + D, R + D + DVH). Performance was evaluated via ROC analysis, calibration, and decision curve analysis.

Results

Among the clinical and dosimetric factors, VBED70 (α/β = 3 Gy) of the lung was recognized as an independent risk factor for SRP. BED-based radiomic and dosiomic models outperformed whole-lung models (AUCs: 0.806 vs. 0.674 and 0.821 vs. 0.647, respectively). The R + D + DVH trio model achieved the highest predictive accuracy (AUC: 0.889, 95 % CI: 0.701–0.956), with robust calibration and clinical utility.

Conclusions

The R + D + DVH trio model based on lung biologically equivalent dose segmentation approach outperforms other models in predicting SRP across various SBRT fractionation schemes.
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来源期刊
Radiotherapy and Oncology
Radiotherapy and Oncology 医学-核医学
CiteScore
10.30
自引率
10.50%
发文量
2445
审稿时长
45 days
期刊介绍: Radiotherapy and Oncology publishes papers describing original research as well as review articles. It covers areas of interest relating to radiation oncology. This includes: clinical radiotherapy, combined modality treatment, translational studies, epidemiological outcomes, imaging, dosimetry, and radiation therapy planning, experimental work in radiobiology, chemobiology, hyperthermia and tumour biology, as well as data science in radiation oncology and physics aspects relevant to oncology.Papers on more general aspects of interest to the radiation oncologist including chemotherapy, surgery and immunology are also published.
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