Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, 28040, Madrid, Spain.
Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.
J Transl Med. 2023 Mar 5;21(1):174. doi: 10.1186/s12967-023-04004-x.
Identifying predictive non-invasive biomarkers of immunotherapy response is crucial to avoid premature treatment interruptions or ineffective prolongation. Our aim was to develop a non-invasive biomarker for predicting immunotherapy clinical durable benefit, based on the integration of radiomics and clinical data monitored through early anti-PD-1/PD-L1 monoclonal antibodies treatment in patients with advanced non-small cell lung cancer (NSCLC).
In this study, 264 patients with pathologically confirmed stage IV NSCLC treated with immunotherapy were retrospectively collected from two institutions. The cohort was randomly divided into a training (n = 221) and an independent test set (n = 43), ensuring the balanced availability of baseline and follow-up data for each patient. Clinical data corresponding to the start of treatment was retrieved from electronic patient records, and blood test variables after the first and third cycles of immunotherapy were also collected. Additionally, traditional radiomics and deep-radiomics features were extracted from the primary tumors of the computed tomography (CT) scans before treatment and during patient follow-up. Random Forest was used to implementing baseline and longitudinal models using clinical and radiomics data separately, and then an ensemble model was built integrating both sources of information.
The integration of longitudinal clinical and deep-radiomics data significantly improved clinical durable benefit prediction at 6 and 9 months after treatment in the independent test set, achieving an area under the receiver operating characteristic curve of 0.824 (95% CI: [0.658,0.953]) and 0.753 (95% CI: [0.549,0.931]). The Kaplan-Meier survival analysis showed that, for both endpoints, the signatures significantly stratified high- and low-risk patients (p-value< 0.05) and were significantly correlated with progression-free survival (PFS6 model: C-index 0.723, p-value = 0.004; PFS9 model: C-index 0.685, p-value = 0.030) and overall survival (PFS6 models: C-index 0.768, p-value = 0.002; PFS9 model: C-index 0.736, p-value = 0.023).
Integrating multidimensional and longitudinal data improved clinical durable benefit prediction to immunotherapy treatment of advanced non-small cell lung cancer patients. The selection of effective treatment and the appropriate evaluation of clinical benefit are important for better managing cancer patients with prolonged survival and preserving quality of life.
识别预测免疫治疗反应的非侵入性生物标志物对于避免过早的治疗中断或无效的延长至关重要。我们的目的是基于通过早期抗 PD-1/PD-L1 单克隆抗体治疗在晚期非小细胞肺癌（NSCLC）患者中监测的放射组学和临床数据的整合，为预测免疫治疗的临床持久获益建立一种非侵入性生物标志物。
本研究回顾性收集了来自两个机构的 264 名经病理证实的 IV 期 NSCLC 患者，接受免疫治疗。该队列被随机分为训练集（n = 221）和独立测试集（n = 43），以确保每位患者的基线和随访数据的平衡可用性。从电子病历中检索治疗开始时的临床数据，并在免疫治疗的第一和第三个周期后收集血液检测变量。此外，从治疗前和患者随访期间的计算机断层扫描（CT）扫描中提取传统放射组学和深度放射组学特征。使用随机森林分别使用临床和放射组学数据构建基线和纵向模型，然后构建一个集成两种信息源的综合模型。
纵向临床和深度放射组学数据的整合显著提高了独立测试集中治疗后 6 个月和 9 个月的临床持久获益预测，在独立测试集中获得了 0.824（95%CI：[0.658,0.953]）和 0.753（95%CI：[0.549,0.931]）的受试者工作特征曲线下面积。Kaplan-Meier 生存分析表明，对于两个终点，该特征均显著地将高风险和低风险患者分层（p 值<0.05），并与无进展生存期（PFS6 模型：C 指数 0.723，p 值 = 0.004；PFS9 模型：C 指数 0.685，p 值 = 0.030）和总生存期（PFS6 模型：C 指数 0.768，p 值 = 0.002；PFS9 模型：C 指数 0.736，p 值 = 0.023）显著相关。
整合多维和纵向数据提高了对晚期非小细胞肺癌患者免疫治疗的临床持久获益预测。为了更好地管理延长生存期和保持生活质量的癌症患者，选择有效的治疗方法和适当评估临床获益非常重要。
