PURPOSE The high false positive rate (FPR) of 18F-FDG PET/CT in lung cancer screening represents a severe challenge for clinical decision-making. This study aimed to develop a clinical-translatable radiomics nomogram for reducing the FPR of PET/CT in lung cancer diagnosis, and to determine the impact of integrating manual diagnosis to the performance of the radiomics nomogram. METHODS Among 3,947 18F-FDG PET/CT-screened patients with lung lesion, 157 malignant and 111 benign patients were retrospectively enrolled and divided into training and test cohorts. The data of manual diagnosis were recorded. A total of 4,338 features were extracted from CT, thin-section CT, PET and PET/CT, and the four radiomics signatures (RS) were then generated by LASSO method. Radiomics prediction nomogram integrating imaging-based RS and manual diagnosis was developed using multivariable logistic regression. The performances of RS and prediction nomograms were independently validated through key discrimination index and clinical benefit. RESULTS The FPR of manual diagnosis was found to be 30.6%. Among the four RS, PET/CT RS exhibited the best performance. By integrating manual diagnosis, the hybrid nomogram integrating PET/CT RS and manual diagnosis demonstrated lowest FPR and highest area under curve (AUC) and Youden index (YI) in both training and test cohorts (FPR: 5.4% and 9.1%, AUC: 0.98 and 0.92, YI: 85.8% and 75.5%, respectively). This hybrid nomogram respectively corrected 78.6% and 37.5% among FPR cases produced by PET/CT RS, without significantly sacrificing its sensitivity. The net benefit of hybrid nomogram appeared highest at <85% threshold probability. CONCLUSION The established hybrid nomogram integrating PET/CT RS and manual diagnosis can significantly reduce FPR, improve diagnostic accuracy and enhance clinical benefit compared to manual diagnosis. By integrating manual diagnosis, the performance of this hybrid nomogram is superior to PET/CT RS, indicating the importance of clinicians' judgement as an essential information source for improving radiomics diagnostic approaches.

中文翻译：

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将手动诊断集成到放射学中，以减少可疑肺癌患者中18F-FDG PET / CT诊断的假阳性率。

目的18F-FDG PET / CT在肺癌筛查中的高假阳性率（FPR）对临床决策提出了严峻挑战。这项研究旨在开发可降低PET / CT FPR在肺癌诊断中的临床可转换放射线影像图，并确定整合手动诊断对放射线影像图性能的影响。方法对3947例经18F-FDG PET / CT筛查的肺部病变患者中的157例恶性和111例良性患者进行回顾性研究，分为训练组和测试组。记录手动诊断的数据。从CT，薄层CT，PET和PET / CT中总共提取了4338个特征，然后通过LASSO方法生成了四个放射学特征（RS）。使用多变量逻辑回归技术开发了结合基于成像的RS和手动诊断的Radiomics预测列线图。RS和预测列线图的性能分别通过关键的辨别指数和临床获益进行了独立验证。结果人工诊断的FPR为30.6％。在这四种RS中，PET / CT RS表现出最好的性能。通过集成手动诊断，将PET / CT RS和手动诊断相结合的混合列线图显示，在训练和测试队列中，FPR最低，曲线下面积（AUC）和Youden指数（YI）最高（FPR：5.4％和9.1％，AUC： 0.98和0.92，YI分别为85.8％和75.5％）。在PET / CT RS产生的FPR病例中，该混合列线图分别校正了78.6％和37.5％，而没有显着降低其灵敏度。混合列线图的净收益在阈值概率<85％时最高。结论建立的结合PET / CT RS和手动诊断的混合列线图可以显着降低FPR，提高诊断准确性，并提高临床收益。通过集成手动诊断，此混合列线图的性能优于PET / CT RS，表明临床医生的判断作为改进放射线学诊断方法必不可少的信息源的重要性。