旨在评估基于 CT 的 delta 放射组学特征在预测小细胞肺癌 (SCLC) 患者化疗后总生存 (OS) 和局部复发 (LR) 方面的有效性。回顾性纳入 136 名 SCLC 患者，分为训练组和测试组。在第二个和第四个化疗周期之前、之后从 CT 图像中提取放射组学特征。 Delta放射组学特征是通过计算特征的净变化获得的。开发了三个放射组学特征（R1、R2 和 R3）和三个 delta 放射组学特征（R21、R31 和 R32）。最佳特征被定义为放射组学风险特征（RRS）。应用显着的临床放射学因素和 OS 或 LR 的 RRS 来构建组合模型。还分别根据分期和治疗方案对亚组中的 RRS 进行了研究。Delta 放射组学模型表现出了改进的性能。 R32 签名在训练和测试队列中表现出最高的 C 指数，OS 组的 C 指数分别为 0.850 和 0.834，LR 组的 C 指数分别为 0.723 和 0.737。将临床放射学特征纳入 RRSOS 后观察到性能增量，C 指数分别为 0.857 和 0.836。此外，分层分析还证实了 RRS 的能力，分别基于 OS 和 LR 组的分期和治疗方案亚组。Delta 放射组学特征可以改善 SCLC 患者化疗早期 OS 和 LR 的个性化预测。 R32 签名表现出最高的性能。版权所有 © 2023 大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

To evaluate the validity of CT-based delta radiomics signatures in predicting overall survival (OS) and local recurrence (LR) in small cell lung cancer (SCLC) patients after chemotherapy.Retrospectively enrolled 136 SCLC patients were split into training and testing cohorts. Radiomics features were extracted from CT images before, after the second, and the fourth cycle of chemotherapy. Delta radiomics features were obtained by calculating the net changes of features. Three radiomics signatures (R1, R2, and R3) and three delta radiomics signatures (R21, R31, and R32) were developed. The best signature was defined as the radiomics risk signature (RRS). The significant clinicoradiological factors and RRS of OS or LR were applied to build the combined model. RRS was also investigated in the subgroups based on stage and treatment regimens, respectively.Delta radiomics models presented improved performance. R32 signature demonstrated the highest C-indices in the training and testing cohorts, with C-indices of 0.850 and 0.834 in the OS arm, and 0.723 and 0.737 in the LR arm, respectively. The incremental performance was observed after the clinicoradiological characteristics integrated into the RRSOS, with C-indexes of 0.857 and 0.836, respectively. Furthermore, the stratified analysis also confirmed the ability of RRS based on the stage and treatment regimen subgroups in the OS and LR arms, respectively.Delta radiomics signatures could improve the personalized prediction of OS and LR at the early stage of chemotherapy in SCLC patients. R32 signature performed the highest performance.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.