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基于原始和差值锥形束 CT 放射组学预测肝癌 SBRT 反应:一项初步研究。

Prediction of SBRT response in liver cancer by combining original and delta cone-beam CT radiomics: a pilot study.

机构信息

Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China.

Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China.

出版信息

Phys Eng Sci Med. 2024 Mar;47(1):295-307. doi: 10.1007/s13246-023-01366-w. Epub 2024 Jan 2.

Abstract

This study aims to explore the feasibility of utilizing a combination of original and delta cone-beam CT (CBCT) radiomics for predicting treatment response in liver tumors undergoing stereotactic body radiation therapy (SBRT). A total of 49 patients are included in this study, with 36 receiving 5-fraction SBRT, 3 receiving 4-fraction SBRT, and 10 receiving 3-fraction SBRT. The CBCT and planning CT images from liver cancer patients who underwent SBRT are collected to extract overall 547 radiomics features. The CBCT features which are reproducible and interchangeable with pCT are selected for modeling analysis. The delta features between fractions are calculated to depict tumor change. The patients with 4-fraction SBRT are only used for screening robust features. In patients receiving 5-fraction SBRT, the predictive ability of both original and delta CBCT features for two-level treatment response (local efficacy vs. local non-efficacy; complete response (CR) vs. partial response (PR)) is assessed by utilizing multivariable logistic regression with leave-one-out cross-validation. Additionally, univariate analysis is conducted to validate the capability of CBCT features in identifying local efficacy in patients receiving 3-fraction SBRT. In patients receiving 5-fraction SBRT, the combined models incorporating original and delta CBCT radiomics features demonstrate higher area under the curve (AUC) values compared to models using either original or delta features alone for both classification tasks. The AUC values for predicting local efficacy vs. local non-efficacy are 0.58 for original features, 0.82 for delta features, and 0.90 for combined features. For distinguishing PR from CR, the respective AUC values for original, delta and combined features are 0.79, 0.80, and 0.89. In patients receiving 3-fraction SBRT, eight valuable CBCT radiomics features are identified for predicting local efficacy. The combination of original and delta radiomics derived from fractionated CBCT images in liver cancer patients undergoing SBRT shows promise in providing comprehensive information for predicting treatment response.

摘要

本研究旨在探索利用原始和增量锥形束 CT(CBCT)放射组学来预测行立体定向体放射治疗(SBRT)的肝肿瘤治疗反应的可行性。本研究共纳入 49 例患者,其中 36 例接受 5 分次 SBRT,3 例接受 4 分次 SBRT,10 例接受 3 分次 SBRT。收集接受 SBRT 的肝癌患者的 CBCT 和计划 CT 图像,提取总共有 547 个放射组学特征。选择与 pCT 具有可重复性和可互换性的 CBCT 特征进行建模分析。计算分次间的增量特征以描绘肿瘤变化。仅对接受 4 分次 SBRT 的患者进行稳健特征筛选。在接受 5 分次 SBRT 的患者中,利用具有留一交叉验证的多变量逻辑回归评估原始和增量 CBCT 特征对两级治疗反应(局部疗效与局部非疗效;完全缓解(CR)与部分缓解(PR))的预测能力。此外,进行单变量分析以验证在接受 3 分次 SBRT 的患者中,CBCT 特征识别局部疗效的能力。在接受 5 分次 SBRT 的患者中,与仅使用原始或增量 CBCT 放射组学特征的模型相比,纳入原始和增量 CBCT 放射组学特征的联合模型在两个分类任务中均显示出更高的曲线下面积(AUC)值。用于预测局部疗效与局部非疗效的 AUC 值分别为原始特征的 0.58、增量特征的 0.82 和联合特征的 0.90。对于区分 PR 与 CR,原始、增量和联合特征的 AUC 值分别为 0.79、0.80 和 0.89。在接受 3 分次 SBRT 的患者中,确定了 8 个有价值的 CBCT 放射组学特征来预测局部疗效。接受 SBRT 的肝癌患者的分次 CBCT 图像的原始和增量放射组学特征的组合为预测治疗反应提供了全面的信息,具有很大的应用潜力。

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