小细胞肺癌（SCLC）是一种具有高度恶性和转移的癌症，具有极差的预后。虽然联合化疗免疫疗法改善了广泛期（ES）-SCLC的预后，但生存益处仍然有限。此外，迄今为止无可靠的生物标志物可预测化疗免疫疗法的治疗结果。本回顾性研究纳入了中国五家医疗中心于2019年1月1日至2022年10月1日期间接受第一线联合阿特珠单抗或杜瓦伦单抗与标准化疗治疗的广泛期小细胞肺癌（ES-SCLC）患者作为联合化疗免疫疗法组。患者被分为一个训练队列和两个独立的外部验证队列。此外，我们还创建了一个接受第一线标准化疗治疗的广泛期小细胞肺癌对照组。基于治疗前获得的胸部CT上的感兴趣区域提取的放射组学特征，使用机器学习算法获得了Radiomics得分。进行了Cox比例风险回归分析以确定与治疗效果相关的临床特征。采用log-rank检验，时间相关的接收者操作特征曲线，和Concordance Index（C-index）评估模型的有效性。我们的研究纳入了341名患者（平均年龄为62±8.7岁）。经过中位随访时间为12.1个月后，中位无进展生存期（mPFS）为7.1（95% CI 6.6至7.7）个月，而中位总生存期（mOS）尚未达到。TNM分期、Eastern Cooperative Oncology Group生存状况和肺部免疫预后指数与无进展生存期呈显著相关。我们提出了一个基于八项放射组学特征的预测模型，用于确定小细胞肺癌患者联合化疗免疫疗法耐药风险（验证队列1：mPFS，12个月 vs 5个月，C-index=0.634；验证队列2：mPFS，10.8个月 vs 6.1个月，C-index=0.617）。将与无进展生存期相关的临床特征纳入放射组学模型后，预测效果显著提高。因此，与高进展风险组相比，低进展风险组在验证队列1（mPFS，12.8个月 vs 4.5个月，HR=0.40，p=0.028）和验证队列2（mPFS，9.2个月 vs 4.6个月，HR=0.30，p=0.012）中显示出明显较长的中位无进展生存期。外部验证队列1和队列2分别获得了最高的6个月曲线下面积和C-index值，分别为0.852和0.820。重要的是，综合预测模型也在生存结果方面表现出较大的区分能力。所有患者中低进展风险组和高进展风险组的总生存期比为0.28（95% CI 0.17至0.48），验证队列中比为0.20（95% CI 0.08至0.54）。相比之下，在联合化疗免疫疗法和化疗队列之间未观察到无进展生存期和总生存期的显著差异（mPFS，5.5个月 vs 5.9个月，HR=0.90，p=0.547；mOS，14.5个月 vs 13.7个月，HR=0.97，p=0.910）。集成的临床和放射组学模型可以预测接受联合化疗免疫疗法的广泛期小细胞肺癌患者的治疗结果，为患者管理的决策提供了一种便捷且低成本的预后模型。© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Small cell lung cancer (SCLC) is a highly malignant cancer characterized by metastasis and an extremely poor prognosis. Although combined chemoimmunotherapy improves the prognosis of extensive-stage (ES)-SCLC, the survival benefits remain limited. Furthermore, no reliable biomarker is available so far to predict the treatment outcomes for chemoimmunotherapy.This retrospective study included patients with ES-SCLC treated with first-line combined atezolizumab or durvalumab with standard chemotherapy between Janauray 1, 2019 and October 1, 2022 at five medical centers in China as the chemoimmunotherapy group. The patients were divided into one training cohort and two independent external validation cohorts. Additionally, we created a control group of ES-SCLC who was treated with first-line standard chemotherapy alone. The Radiomics Score was derived using machine learning algorithms based on the radiomics features extracted in the regions of interest delineated on the chest CT obtained before treatment. Cox proportional hazards regression analysis was performed to identify clinical features associated with therapeutic efficacy. The log-rank test, time-dependent receiver operating characteristic curve, and Concordance Index (C-index) were used to assess the effectiveness of the models.A total of 341 patients (mean age, 62±8.7 years) were included in our study. After a median follow-up time of 12.1 months, the median progression-free survival (mPFS) was 7.1 (95% CI 6.6 to 7.7) months, whereas the median overall survival (mOS) was not reached. The TNM stage, Eastern Cooperative Oncology Group performance status, and Lung Immune Prognostic Index showed significant correlations with PFS. We proposed a predictive model based on eight radiomics features to determine the risk of chemoimmunotherapy resistance among patients with SCLC (validation set 1: mPFS, 12.0 m vs 5.0 m, C-index=0.634; validation set 2: mPFS, 10.8 m vs 6.1 m, C-index=0.617). By incorporating the clinical features associated with PFS into the radiomics model, the predictive efficacy was substantially improved. Consequently, the low-progression-risk group exhibited a significantly longer mPFS than the high-progression-risk group in both validation set 1 (mPFS, 12.8 m vs 4.5 m, HR=0.40, p=0.028) and validation set 2 (mPFS, 9.2 m vs 4.6 m, HR=0.30, p=0.012). External validation set 1 and set 2 yielded the highest 6-month area under the curve and C-index of 0.852 and 0.820, respectively. Importantly, the integrated prediction model also exhibited considerable differentiation power for survival outcomes. The HR for OS derived from the low-progression-risk and high-progression-risk groups was 0.28 (95% CI 0.17 to 0.48) in all patients and 0.20 (95% CI 0.08 to 0.54) in validation set. By contrast, no significant differences were observed in PFS and OS, between high-progression-risk patients receiving chemoimmunotherapy and the chemotherapy cohort (mPFS, 5.5 m vs 5.9 m, HR=0.90, p=0.547; mOS, 14.5 m vs 13.7 m, HR=0.97, p=0.910).The integrated clinical and radiomics model can predict the treatment outcomes in patients with ES-SCLC receiving chemoimmunotherapy, rendering a convenient and low-cost prognostic model for decision-making regarding patient management.© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.