Sako Chiharu, Duan Chong, Maresca Kevin, Kent Sean, Schmidt Taly Gilat, Aerts Hugo J W L, Parikh Ravi B, Simon George R, Jordan Petr
Onc.AI, San Carlos, CA.
Pfizer, Cambridge, MA.
JCO Clin Cancer Inform. 2024 Dec;8:e2400133. doi: 10.1200/CCI.24.00133. Epub 2024 Dec 13.
This study developed and validated a novel deep learning radiomic biomarker to estimate response to immune checkpoint inhibitor (ICI) therapy in advanced non-small cell lung cancer (NSCLC) using real-world data (RWD) and clinical trial data.
Retrospective RWD of 1,829 patients with advanced NSCLC treated with PD-(L)1 ICIs were collected from 10 academic and community institutions in the United States and Europe. The RWD included data sets for discovery (Data Set A-Discovery, n = 1,173) and independent test (Data Set B, n = 458). A radiomic pipeline, containing a deep learning feature extractor and a survival model, generated the computed tomography (CT) response score (CTRS) applied to the pretreatment routine CT/positron emission tomography (PET)-CT scan. An enhanced CTRS (eCTRS) also incorporated age, sex, treatment line, and lesion annotations. Performance was evaluated against progression-free survival (PFS) and overall survival (OS). Biomarker generalizability was further evaluated using a secondary analysis of a prospective clinical trial (ClinicalTrials.gov identifier: NCT02573259) evaluating the PD-1 inhibitor sasanlimab in second or later line of treatment (Data Set C, n = 54).
In RWD Test Data Set B, the CTRS identified patients with a high probability of response to ICI with a PFS hazard ratio (HR) of 0.46 (95% CI, 0.26 to 0.82) and an OS HR of 0.50 (95% CI, 0.28 to 0.92) in the first-line ICI monotherapy cohort, after adjustment for baseline covariates including the PD-L1 tumor proportion score. In Clinical Trial Data Set C, the CTRS demonstrated an adjusted PFS HR of 1.03 (95% CI, 0.43 to 2.47) and an OS HR of 0.33 (95% CI, 0.14 to 0.91). The CTRS and eCTRS outperformed traditional imaging biomarkers of lesion size in PFS and OS for RWD Test Data Set B and in OS for the Clinical Trial Data Set.
The study developed and validated a deep learning radiomic biomarker using pretreatment routine CT/PET-CT scans to identify ICI benefit in advanced NSCLC.
本研究开发并验证了一种新型深度学习放射组学生物标志物，以利用真实世界数据（RWD）和临床试验数据评估晚期非小细胞肺癌（NSCLC）对免疫检查点抑制剂（ICI）治疗的反应。
从美国和欧洲的10家学术及社区机构收集了1829例接受PD-（L）1 ICI治疗的晚期NSCLC患者的回顾性RWD。RWD包括发现数据集（数据集A-发现，n = 1173）和独立测试数据集（数据集B，n = 458）。一个包含深度学习特征提取器和生存模型的放射组学流程生成了应用于治疗前常规计算机断层扫描（CT）/正电子发射断层扫描（PET）-CT扫描的CT反应评分（CTRS）。增强的CTRS（eCTRS）还纳入了年龄、性别、治疗线数和病变注释。根据无进展生存期（PFS）和总生存期（OS）评估性能。使用一项评估PD-1抑制剂沙善利单抗在二线或更后线治疗中的前瞻性临床试验（ClinicalTrials.gov标识符：NCT02573259）的二次分析（数据集C，n = 54）进一步评估生物标志物的可推广性。
在RWD测试数据集B中，在对包括PD-L1肿瘤比例评分在内的基线协变量进行调整后，CTRS在一线ICI单药治疗队列中识别出对ICI反应可能性高的患者，其PFS风险比（HR）为0.46（95%CI，0.26至0.82），OS HR为0.50（95%CI，0.28至0.92）。在临床试验数据集C中，CTRS显示调整后的PFS HR为1.（95%CI，0.43至2.47），OS HR为0.33（95%CI，0.14至0.91）。对于RWD测试数据集B的PFS和OS以及临床试验数据集的OS，CTRS和eCTRS均优于传统的病变大小成像生物标志物。
本研究开发并验证了一种深度学习放射组学生物标志物，使用治疗前常规CT/PET-CT扫描来识别晚期NSCLC患者从ICI治疗中获益的情况。
