Integrating manual diagnosis into radiomics for reducing the false positive rate of 18F-FDG PET/CT diagnosis in patients with suspected lung cancer
Integrating manual diagnosis into radiomics for reducing the false positive rate of 18F-FDG PET/CT diagnosis in patients with suspected lung cancer
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将手动诊断融入放射组学降低疑似肺癌患者F-18-FDG PET/CT诊断假阳性率
DOI：
10.1007/s00259-019-04418-0
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发表时间：
2019-12-01
影响因子：
9.1
通讯作者：
Wang, Jing
中科院分区：
文献类型：
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作者：
Kang, Fei;Mu, Wei;Wang, Jing
目的18氟脱氧葡萄糖（F-18-FDG）PET/CT在肺癌筛查中的高假阳性率（FPR）给临床决策带来了严峻挑战。本研究旨在开发一种临床可翻译的放射组学诺模图，以降低PET/CT在肺癌诊断中的FPR，并确定整合人工诊断对放射组学诺模图性能的影响。方法在3,947例F-18-FDG PET/CT筛查的肺部病变患者中，157例恶性和111例良性患者回顾性入组，并分为训练和测试队列。记录手法诊断数据。从CT、薄层CT、PET和PET/CT共提取4,338个特征，然后通过LASSO方法生成四个放射组学签名（RS）。放射组学预测诺模图集成成像为基础的RS和人工诊断，使用多变量逻辑回归。结果人工诊断的FPR为30.6%，人工诊断的FPR为30.6%。在四种RS中，PET/CT RS表现出最好的性能。通过整合手动诊断，整合PET/CT RS和手动诊断的混合列线图在训练和测试队列中均显示出最低的FPR和最高的曲线下面积（AUC）和约登指数（YI）（FPR：5.4%和9.1%，AUC：0.98和0.92，YI：85.8%和75.5%）。该混合诺模图对PET/CT RS产生的FPR病例的校正率分别为78.6%和37.5%，而没有显著降低其灵敏度。杂交诺模图的净效益最高，
Purpose The high false positive rate (FPR) of F-18-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 F-18-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