Gainey Jordan C, He Yusen, Zhu Robert, Baek Stephen S, Wu Xiaodong, Buatti John M, Allen Bryan G, Smith Brian J, Kim Yusung
Department of Radiation Oncology, The University of Iowa, Iowa City, IA, United States.
Department of Data Science, Grinnell College, Grinnell, IA, United States.
Front Oncol. 2023 Apr 4;13:868471. doi: 10.3389/fonc.2023.868471. eCollection 2023.
The study aims to create a model to predict survival outcomes for non-small cell lung cancer (NSCLC) after treatment with stereotactic body radiotherapy (SBRT) using deep-learning segmentation based prognostication (DESEP).
The DESEP model was trained using imaging from 108 patients with NSCLC with various clinical stages and treatment histories. The model generated predictions based on unsupervised features learned by a deep-segmentation network from computed tomography imaging to categorize patients into high and low risk groups for overall survival (DESEP-predicted-OS), disease specific survival (DESEP-predicted-DSS), and local progression free survival (DESEP-predicted-LPFS). Serial assessments were also performed using auto-segmentation based volumetric RECISTv1.1 and computer-based unidimensional RECISTv1.1 patients was performed.
There was a concordance between the DESEP-predicted-LPFS risk category and manually calculated RECISTv1.1 (φ=0.544, p=0.001). Neither the auto-segmentation based volumetric RECISTv1.1 nor the computer-based unidimensional RECISTv1.1 correlated with manual RECISTv1.1 (p=0.081 and p=0.144, respectively). While manual RECISTv1.1 correlated with LPFS (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding DSS (p=0.942) or OS (p=0.662). In contrast, the DESEP-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). The promising results of the DESEP model were reproduced for the independent, external datasets of Stanford University, classifying survival and 'dead' group in their Kaplan-Meyer curves (p = 0.019).
Deep-learning segmentation based prognostication can predict LPFS as well as OS, and DSS after SBRT for NSCLC. It can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients receiving SBRT.
While current standard of care, manual RECISTv1.1 correlated with local progression free survival (LPFS) (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding disease specific survival (DSS) (p=0.942) or overall survival (OS) (p=0.662). In contrast, the deep-learning segmentation based prognostication (DESEP)-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). DESEP can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients.
本研究旨在创建一种模型，用于预测接受立体定向体部放射治疗（SBRT）的非小细胞肺癌（NSCLC）患者的生存结局，该模型采用基于深度学习分割的预后分析（DESEP）。
使用108例具有不同临床分期和治疗史的NSCLC患者的影像数据对DESEP模型进行训练。该模型基于深度学习分割网络从计算机断层扫描影像中学习到的无监督特征生成预测结果，将患者分为总生存（DESEP预测总生存，DESEP-predicted-OS）、疾病特异性生存（DESEP预测疾病特异性生存，DESEP-predicted-DSS）和局部无进展生存（DESEP预测局部无进展生存，DESEP-predicted-LPFS）的高风险和低风险组。还使用基于自动分割的容积RECISTv1.1和基于计算机的一维RECISTv1.1对患者进行了系列评估。
DESEP预测的LPFS风险类别与手动计算的RECISTv1.1之间存在一致性（φ=0.544，p=0.001）。基于自动分割的容积RECISTv1.1和基于计算机的一维RECISTv1.1均与手动RECISTv1.1无相关性（分别为p=0.081和p=0.144）。虽然手动RECISTv1.1与LPFS相关（HR=6.97，3.51 - 13.85，c=0.70，p<0.001），但它无法提供关于DSS（p=0.942）或OS（p=0.662）的见解。相比之下，DESEP预测方法可预测LPFS（HR=3.58，1.66 - 7.18，c=0.60，p<0.001）、OS（HR=6.31，3.65 - 10.93，c=0.71，p<0.001）和DSS（HR=9.25，4.50 - 19.02，c=0.69，p<0.001）。DESEP模型的良好结果在斯坦福大学的独立外部数据集中得到重现，在其Kaplan - Meyer曲线中对生存组和“死亡”组进行了分类（p = 0.019）。
基于深度学习分割的预后分析可以预测NSCLC患者接受SBRT后的LPFS以及OS和DSS。它可以与当前的护理标准手动RECISTv1.1结合使用，以提供关于接受SBRT的NSCLC患者DSS和OS的更多见解。
虽然当前的护理标准手动RECISTv1.
