Abstract：
BACKGROUND: Selecting therapeutic strategies for cancer patients is typically based on key target-molecule biomarkers that play an important role in cancer onset, progression, and prognosis. Thus, there is a pressing need for novel biomarkers that can be utilized longitudinally to guide treatment selection.
METHODS: Using data from 508 non-small cell lung cancer (NSCLC) patients across three institutions, we developed and validated a comprehensive predictive biomarker that distinguishes six genotypes and infiltrative immune phenotypes. These features were analyzed to establish the association between radiological phenotypes and tumor genotypes/immune phenotypes and to create a radiological interpretation of molecular features. In addition, we assessed the sensitivity of the models by evaluating their performance at five different voxel intervals, resulting in improved generalizability of the proposed approach.
RESULTS: The radiomics model we developed, which integrates clinical factors and multi-regional features, outperformed the conventional model that only uses clinical and intratumoral features. Our combined model showed significant performance for EGFR, KRAS, ALK, TP53, PIK3CA, and ROS1 mutation status with AUCs of 0.866, 0.874, 0.902, 0.850, 0.860, and 0.900, respectively. Additionally, the predictive performance for PD-1/PD-L1 was 0.852. Although the performance of all models decreased to different degrees at five different voxel space resolutions, the performance advantage of the combined model did not change.
CONCLUSIONS: We validated multiscale radiomic signatures across tumor genotypes and immunophenotypes in a multi-institutional cohort. This imaging-based biomarker offers a non-invasive approach to select patients with NSCLC who are sensitive to targeted therapies or immunotherapy, which is promising for developing personalized treatment strategies during therapy.
METHODS: Using data from 508 non-small cell lung cancer (NSCLC) patients across three institutions, we developed and validated a comprehensive predictive biomarker that distinguishes six genotypes and infiltrative immune phenotypes. These features were analyzed to establish the association between radiological phenotypes and tumor genotypes/immune phenotypes and to create a radiological interpretation of molecular features. In addition, we assessed the sensitivity of the models by evaluating their performance at five different voxel intervals, resulting in improved generalizability of the proposed approach.
RESULTS: The radiomics model we developed, which integrates clinical factors and multi-regional features, outperformed the conventional model that only uses clinical and intratumoral features. Our combined model showed significant performance for EGFR, KRAS, ALK, TP53, PIK3CA, and ROS1 mutation status with AUCs of 0.866, 0.874, 0.902, 0.850, 0.860, and 0.900, respectively. Additionally, the predictive performance for PD-1/PD-L1 was 0.852. Although the performance of all models decreased to different degrees at five different voxel space resolutions, the performance advantage of the combined model did not change.
CONCLUSIONS: We validated multiscale radiomic signatures across tumor genotypes and immunophenotypes in a multi-institutional cohort. This imaging-based biomarker offers a non-invasive approach to select patients with NSCLC who are sensitive to targeted therapies or immunotherapy, which is promising for developing personalized treatment strategies during therapy.
摘要：
背景：为癌症患者选择治疗策略通常基于在癌症发病中起重要作用的关键靶分子生物标志物，programming,和预后。因此，迫切需要可以纵向使用的新型生物标志物来指导治疗选择。
方法：使用来自三个机构的508位非小细胞肺癌（NSCLC）患者的数据，我们开发并验证了可区分6种基因型和浸润性免疫表型的综合预测性生物标志物.分析这些特征以建立放射学表型与肿瘤基因型/免疫表型之间的关联，并建立分子特征的放射学解释。此外,我们通过在五个不同的体素间隔下评估模型的性能来评估模型的灵敏度，从而提高了所提出方法的泛化性。
结果：我们开发的影像组学模型，整合了临床因素和多区域特征，优于仅使用临床和肿瘤内特征的常规模型。我们的组合模型显示了EGFR的显着性能，KRAS,ALK,TP53,PIK3CA,和ROS1突变状态，AUC分别为0.866、0.874、0.902、0.850、0.860和0.900。此外,PD-1/PD-L1的预测性能为0.852.尽管在五个不同的体素空间分辨率下，所有模型的性能都有不同程度的下降，组合模型的性能优势没有改变。
结论：我们在多机构队列中验证了肿瘤基因型和免疫表型的多尺度放射组学特征。这种基于成像的生物标志物提供了一种非侵入性方法来选择对靶向治疗或免疫疗法敏感的NSCLC患者。这对于在治疗期间开发个性化治疗策略是有希望的。
方法：使用来自三个机构的508位非小细胞肺癌（NSCLC）患者的数据，我们开发并验证了可区分6种基因型和浸润性免疫表型的综合预测性生物标志物.分析这些特征以建立放射学表型与肿瘤基因型/免疫表型之间的关联，并建立分子特征的放射学解释。此外,我们通过在五个不同的体素间隔下评估模型的性能来评估模型的灵敏度，从而提高了所提出方法的泛化性。
结果：我们开发的影像组学模型，整合了临床因素和多区域特征，优于仅使用临床和肿瘤内特征的常规模型。我们的组合模型显示了EGFR的显着性能，KRAS,ALK,TP53,PIK3CA,和ROS1突变状态，AUC分别为0.866、0.874、0.902、0.850、0.860和0.900。此外,PD-1/PD-L1的预测性能为0.852.尽管在五个不同的体素空间分辨率下，所有模型的性能都有不同程度的下降，组合模型的性能优势没有改变。
结论：我们在多机构队列中验证了肿瘤基因型和免疫表型的多尺度放射组学特征。这种基于成像的生物标志物提供了一种非侵入性方法来选择对靶向治疗或免疫疗法敏感的NSCLC患者。这对于在治疗期间开发个性化治疗策略是有希望的。
