Predicting early stage lung cancer recurrence and survival from combined tumor motion amplitude and radiomics on free-breathing 4D-CT

IF 3.2 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Medical physics Pub Date : 2024-12-20 DOI:10.1002/mp.17586
Emilie Ouraou, Marion Tonneau, William T. Le, Edith Filion, Marie-Pierre Campeau, Toni Vu, Robert Doucet, Houda Bahig, Samuel Kadoury
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引用次数: 0

Abstract

Background

Cancer control outcomes of lung cancer are hypothesized to be affected by several confounding factors, including tumor heterogeneity and patient history, which have been hypothesized to mitigate the dose delivery effectiveness when treated with radiation therapy. Providing an accurate predictive model to identify patients at risk would enable tailored follow-up strategies during treatment.

Purpose

Our goal is to demonstrate the added prognostic value of including tumor displacement amplitude in a predictive model that combines clinical features and computed tomography (CT) radiomics for 2-year recurrence and survival in non-small-cell lung cancer (NSCLC) patients treated with curative-intent stereotactic body radiation therapy.

Methods

A cohort of 381 patients treated for primary lung cancer with radiotherapy was collected, each including a planning CT with a dosimetry plan, 4D-CT, and clinical information. From this cohort, 101 patients (26.5%) experienced cancer progression (locoregional/distant metastasis) or death within 2 years of the end of treatment. Imaging data was analyzed for radiomics features from the tumor segmented image, as well as tumor motion amplitude measured on 4D-CT. A random forest (RF) model was developed to predict the overall outcomes, which was compared to three other approaches — logistic regression, support vector machine, and convolutional neural networks.

Results

A 6-fold cross-validation study yielded an area under the receiver operating characteristic curve of 72% for progression-free survival when combining clinical data with radiomics features and tumor motion using a RF model (72% sensitivity and 81% specificity). The combined model showed significant improvement compared to standard clinical data. Model performances for loco-regional recurrence and overall survival sub-outcomes were established at 73% and 70%, respectively. No comparative methods reached statistical significance in any data configuration.

Conclusions

Combined tumor respiratory motion and radiomics features from planning CT showed promising predictive value for 2-year tumor control and survival, indicating the potential need for improving motion management strategies in future studies using machine learning-based prognosis models.

Abstract Image

自由呼吸4D-CT联合肿瘤运动幅度和放射组学预测早期肺癌复发和生存。
背景:肺癌的癌症控制结果被假设受到几个混杂因素的影响,包括肿瘤异质性和患者病史,这些因素被假设会降低放射治疗时剂量传递的有效性。提供一个准确的预测模型来识别有风险的患者,将有助于在治疗期间制定量身定制的随访策略。目的:我们的目标是证明在结合临床特征和计算机断层扫描(CT)放射组学的非小细胞肺癌(NSCLC)患者2年复发和生存率的预测模型中包括肿瘤位移幅度的附加预后价值。方法:收集381例接受放疗的原发性肺癌患者,包括计划CT、剂量学计划、4D-CT和临床资料。在该队列中,101例患者(26.5%)在治疗结束后2年内出现癌症进展(局部/远处转移)或死亡。对成像数据进行放射组学特征分析,从肿瘤分割图像中提取放射组学特征,并在4D-CT上测量肿瘤运动幅度。开发了一个随机森林(RF)模型来预测总体结果,并将其与其他三种方法(逻辑回归、支持向量机和卷积神经网络)进行了比较。结果:一项6倍交叉验证研究显示,当使用RF模型将临床数据与放射组学特征和肿瘤运动相结合(72%的敏感性和81%的特异性)时,接受者工作特征曲线下的无进展生存率为72%。与标准临床数据相比,联合模型有显著改善。局部-区域复发率和总生存亚结局的模型性能分别为73%和70%。没有比较方法在任何数据配置中达到统计学意义。结论:计划CT的肿瘤呼吸运动和放射组学特征对2年肿瘤控制和生存具有良好的预测价值,表明在未来使用基于机器学习的预后模型的研究中,可能需要改进运动管理策略。
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
Medical physics
Medical physics 医学-核医学
CiteScore
6.80
自引率
15.80%
发文量
660
审稿时长
1.7 months
期刊介绍: Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.
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