利用锥形束计算机断层扫描研究放疗期间肺癌恶病质动态的全自动工作流程。

IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Physics in medicine and biology Pub Date : 2024-10-04 DOI:10.1088/1361-6560/ad7d5b
Lars H B A Daenen, Wouter R P H van de Worp, Behzad Rezaeifar, Joël de Bruijn, Peiyu Qiu, Justine M Webster, Stéphanie Peeters, Dirk De Ruysscher, Ramon C J Langen, Cecile J A Wolfs, Frank Verhaegen
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引用次数: 0

摘要

目的:恶病质是一种破坏性疾病,其特征是肌肉质量不自主地减少,同时伴有或不伴有脂肪组织质量的减少。半数以上的肺癌患者都会出现这种症状,从而影响治疗效果并增加死亡率。在放疗过程中常规获取的锥形束计算机断层扫描(CBCT)图像可能包含有价值的解剖信息,可用于监测与恶病质相关的身体成分变化。为此,我们提出了一种基于人工智能(AI)的自动工作流程,包括将 CBCT 转换为 CT,然后对胸肌进行分割。我们使用了 140 名 III 期非小细胞肺癌患者的数据。两个深度学习模型,即循环一致性生成对抗网络(CycleGAN)和对比性无配对转换(CUT),被用于 CBCT 到 CT 转换的无配对训练,以生成合成 CT(sCT)图像。无新 U-Net (nnU-Net)模型用于自动胸肌分割。为了评估在没有地面实况标签的情况下的组织分割性能,我们开发并验证了一种基于蒙特卡洛剔除的不确定性度量(UM)。与规划 CT(pCT)图像相比,CycleGAN 和 CUT 都恢复了 CBCT 图像的 Hounsfield 单位保真度,并在视觉上减少了条纹伪影。在独立测试集上,nnU-Net 模型对 CT、sCT 和 CBCT 图像的 Dice 相似系数(DSC)分别达到了 0.93、0.94 和 0.92。UM 与 DSC 的相关性很高,pCT 数据集的相关系数为-0.84,sCT 数据集的相关系数为-0.89。本文展示了基于 CBCT 图像的人工智能自动监测肺癌患者放化疗期间胸肌面积的概念验证,它提供了前所未有的恶病质进展期间肌肉质量损失的时间分辨率。最终,所提出的工作流程可为恶病质的早期干预提供有价值的信息,从而改善癌症治疗效果。
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Towards a fully automatic workflow for investigating the dynamics of lung cancer cachexia during radiotherapy using cone beam computed tomography.

Objective.Cachexia is a devastating condition, characterized by involuntary loss of muscle mass with or without loss of adipose tissue mass. It affects more than half of patients with lung cancer, diminishing treatment effects and increasing mortality. Cone-beam computed tomography (CBCT) images, routinely acquired during radiotherapy treatment, might contain valuable anatomical information for monitoring body composition changes associated with cachexia. For this purpose, we propose an automatic artificial intelligence (AI)-based workflow, consisting of CBCT to CT conversion, followed by segmentation of pectoralis muscles.Approach.Data from 140 stage III non-small cell lung cancer patients was used. Two deep learning models, cycle-consistent generative adversarial network (CycleGAN) and contrastive unpaired translation (CUT), were used for unpaired training of CBCT to CT conversion, to generate synthetic CT (sCT) images. The no-new U-Net (nnU-Net) model was used for automatic pectoralis muscle segmentation. To evaluate tissue segmentation performance in the absence of ground truth labels, an uncertainty metric (UM) based on Monte Carlo dropout was developed and validated.Main results.Both CycleGAN and CUT restored the Hounsfield unit fidelity of the CBCT images compared to the planning CT (pCT) images and visually reduced streaking artefacts. The nnU-Net model achieved a Dice similarity coefficient (DSC) of 0.93, 0.94, 0.92 for the CT, sCT and CBCT images, respectively, on an independent test set. The UM showed a high correlation with DSC with a correlation coefficient of -0.84 for the pCT dataset and -0.89 for the sCT dataset.Significance.This paper shows a proof-of-concept for automatic AI-based monitoring of the pectoralis muscle area of lung cancer patients during radiotherapy treatment based on CBCT images, which provides an unprecedented time resolution of muscle mass loss during cachexia progression. Ultimately, the proposed workflow could provide valuable information for early intervention of cachexia, ideally resulting in improved cancer treatment outcome.

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来源期刊
Physics in medicine and biology
Physics in medicine and biology 医学-工程:生物医学
CiteScore
6.50
自引率
14.30%
发文量
409
审稿时长
2 months
期刊介绍: The development and application of theoretical, computational and experimental physics to medicine, physiology and biology. Topics covered are: therapy physics (including ionizing and non-ionizing radiation); biomedical imaging (e.g. x-ray, magnetic resonance, ultrasound, optical and nuclear imaging); image-guided interventions; image reconstruction and analysis (including kinetic modelling); artificial intelligence in biomedical physics and analysis; nanoparticles in imaging and therapy; radiobiology; radiation protection and patient dose monitoring; radiation dosimetry
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