AbstractThis study aimed to explore potential radiomics biomarkers in predicting the efficiency of chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC). Eligible patients were prospectively assigned to receive chemo-immunotherapy, and were divided into a primary cohort (n = 138) and an internal validation cohort (n = 58).

摘要本研究旨在探索潜在的放射组学生物标志物，以预测晚期非小细胞肺癌（NSCLC）患者化疗免疫治疗的效率。符合条件的患者被前瞻性分配接受化学免疫治疗，并分为主要队列（n=138）和内部验证队列（n=58）。

Additionally, a separative dataset was used as an external validation cohort (n = 60). Radiomics signatures were extracted and selected from the primary tumor sites from chest CT images. A multivariate logistic regression analysis was conducted to identify the independent clinical predictors. Subsequently, a radiomics nomogram model for predicting the efficiency of chemo-immunotherapy was conducted by integrating the selected radiomics signatures and the independent clinical predictors.

此外，一个独立的数据集被用作外部验证队列（n=60）。从胸部CT图像的原发肿瘤部位提取并选择放射组学特征。进行多因素logistic回归分析以确定独立的临床预测因子。随后，通过整合所选的放射组学特征和独立的临床预测因子，进行了用于预测化学免疫疗法效率的放射组学列线图模型。

The receiver operating characteristic (ROC) curves demonstrated that the radiomics model, the clinical model, and the radiomics nomogram model achieved areas under the curve (AUCs) of 0.85 (95% confidence interval [CI] 0.78–0.92), 0.76 (95% CI 0.68–0.84), and 0.89 (95% CI 0.84–0.94), respectively, in the primary cohort.

受试者工作特征（ROC）曲线表明，在主要队列中，放射组学模型，临床模型和放射组学列线图模型的曲线下面积（AUC）分别为0.85（95%置信区间[CI]0.78-0.92），0.76（95%CI 0.68-0.84）和0.89（95%CI 0.84-0.94）。

In the internal validation cohort, the corresponding AUCs were 0.93 (95% CI 0.86–1.00), 0.79 (95% CI 0.68–0.91), and 0.96 (95% CI 0.90–1.00) respectively. Moreover, in the external validation cohort, the AUCs were 0.84 (95% CI 0.72–0.96), 0.75 (95% CI 0.62–0.87), and 0.86 (95% CI 0.75–0.96), respectively.

在内部验证队列中，相应的AUC分别为0.93（95%CI 0.86-1.00），0.79（95%CI 0.68-0.91）和0.96（95%CI 0.90-1.00）。此外，在外部验证队列中，AUC分别为0.84（95%CI 0.72-0.96），0.75（95%CI 0.62-0.87）和0.86（95%CI 0.75-0.96）。

In conclusion, the radiomics nomogram provides a convenient model for predicting the effect of chemo-immunotherapy in advanced NSCLC patients..

总之，放射组学列线图为预测晚期NSCLC患者化疗免疫治疗的效果提供了一个方便的模型。。

IntroductionNon-small cell lung cancer (NSCLC) accounts for approximately 80–85% of all lung cancers. The treatment of advanced NSCLC depends on tumor histology, pathological subtype, disease stage, and clinical characteristic of the patient1. Chemotherapy and immunotherapy play crucial roles in the treatment of advanced NSCLC2.

引言非小细胞肺癌（NSCLC）约占所有肺癌的80-85%。。化疗和免疫治疗在晚期NSCLC2的治疗中起着至关重要的作用。

However, the prognosis of advanced NSCLC remains poor, with a 5-year survival rate of less than 15%3.Currently, combining chemo-immunotherapy is purposed to improve the survival of advanced NSCLC patients. The mechanism of standard chemotherapy is to induce tumor cell lethality4. Combining standard chemotherapy and immune checkpoint inhibitors could strengthen the immune system against tumor cells5.

然而，晚期NSCLC的预后仍然很差，5年生存率低于15%3。目前，联合化疗免疫治疗旨在提高晚期NSCLC患者的生存率。标准化疗的机制是诱导肿瘤细胞致死4。结合标准化疗和免疫检查点抑制剂可以增强针对肿瘤细胞的免疫系统5。

Clinical trials have revealed that chemo-immunotherapy has the potential benefit in the treatment of advanced NSCLC patients6. However, chemo-immunotherapy may lead to enhanced treatment-related toxicities. Thus, selecting eligible candidates for chemo-immunotherapy should be done carefully. Identifying biomarkers to predict responders of chemo-immunotherapy is one of the major challenges7.Previous studies have demonstrated that image biomarkers of non-invasive CT scans could be used to predict the pathological subtypes of lung tumor8.

临床试验表明，化学免疫疗法在治疗晚期NSCLC患者方面具有潜在的益处6。然而，化学免疫疗法可能导致增强的治疗相关毒性。因此，应仔细选择符合条件的化学免疫治疗候选人。。

Medical imaging biomarkers have also been reported to be feasible for predicting responders to different therapies for advanced NSCLC9. Radiomics is a method used to extract quantitative medical image features, providing imaging biomarkers for diagnostic and prognostic purposes10. Additionally, radiomics features and clinical predictors can be combined to build a radiomics nomogram model, which is useful for individual management11.However, to our knowledge, no study has investigated potential radiomics biomarkers to p.

据报道，医学成像生物标志物可用于预测晚期非小细胞肺癌不同疗法的反应者9。放射组学是一种用于提取定量医学图像特征的方法，为诊断和预后目的提供成像生物标志物10。此外，可以将放射组学特征和临床预测因子结合起来，建立放射组学列线图模型，这对个体管理有用11。然而，据我们所知，没有研究调查p的潜在放射组学生物标志物。

Data availability

数据可用性

All data generated or analysed during this study are included in this published article.

本研究期间生成或分析的所有数据均包含在本文中。

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Download referencesAuthor informationAuthors and AffiliationsDepartment of Respiratory Medicine, Jinshan Hospital, Fudan University, Shanghai, 201508, ChinaHua JinDepartment of Medical Imaging, Third Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150030, ChinaYuchao WangDepartment of Clinical Laboratory, Jinshan Hospital, Fudan University, Shanghai, 201508, ChinaXushuo Li & Ying YangDepartment of Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Shanghai, 201508, ChinaRuixue QiAuthorsHua JinView author publicationsYou can also search for this author in.

下载参考文献作者信息作者和附属机构复旦大学金山医院呼吸内科，上海，201508，中国黑龙江中医药大学附属第三医院金华医学影像科，哈尔滨，150030，中国王玉超复旦大学金山医院临床检验科，上海，201508，中国徐硕李应阳复旦大学金山医院肿瘤诊断与治疗中心，上海，201508，中国瑞雪乔治华金维作者出版物您也可以在中搜索这位作者。

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PubMed Google ScholarContributionsR.Q. and Y.W. designed the research study. X.L., Y.W. and Y.Y. provided help and advice on acquisition of data. Y.W. analyzed the data. H.J. and R.Q. wrote the manuscript. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript.Corresponding authorsCorrespondence to.

PubMed谷歌学术贡献。Q、 Y.W.设计了这项研究。十、 L.，Y.W.和Y.Y.在获取数据方面提供了帮助和建议。Y、 W.分析了数据。H、 J.和R.Q.写了手稿。所有作者都为稿件的编辑更改做出了贡献。所有作者都阅读并批准了最终稿件。通讯作者通讯。

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KeywordsChemo-immunotherapyNon-small cell lung cancerRadiomicsNomogram

关键词小细胞肺癌的免疫治疗

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