在基于放射组学的强化学习中利用连续低剂量 CT 扫描改善基线筛查中的肺癌早期诊断。

IF 3.8 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Radiology. Cardiothoracic imaging Pub Date : 2024-06-01 DOI:10.1148/ryct.230196
Yifan Wang, Chuan Zhou, Lei Ying, Elizabeth Lee, Heang-Ping Chan, Aamer Chughtai, Lubomir M Hadjiiski, Ella A Kazerooni
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A baseline RRL (B-RRL) model was trained with only LDCT scans acquired at baseline screening for comparison. The 547 held-out individuals (150 with lung cancer) were used as an independent test set for performance evaluation. The area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI) were used to assess the performances of the models in the classification of screen-detected nodules. Results Deployment to the held-out baseline scans showed that the S-RRL model achieved a significantly higher test AUC (0.88 [95% CI: 0.85, 0.91]) than both the Brock model (AUC, 0.84 [95% CI: 0.81, 0.88]; <i>P</i> = .02) and the B-RRL model (AUC, 0.86 [95% CI: 0.83, 0.90]; <i>P</i> = .02). Lung cancer risk stratification was significantly improved by the S-RRL model as compared with Lung CT Screening Reporting and Data System (NRI, 0.29; <i>P</i> < .001) and the Brock model (NRI, 0.12; <i>P</i> = .008). 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引用次数: 0

摘要

目的 评估利用连续低剂量 CT(LDCT)扫描开发基于放射组学的强化学习(RRL)模型的可行性,以改善基线筛查中肺癌的早期诊断。材料与方法 在这项回顾性研究中,2002 年 8 月至 2004 年 4 月期间,从全国肺筛查试验中随机抽取了 1951 名参与者(女性患者 822 名;中位年龄 61 岁 [范围 55-74 岁])(男性患者 1129 名;中位年龄 62 岁 [范围 55-74 岁])。利用来自1404名参与者(372名肺癌患者)的数据(包含2525张3年前的序列LDCT扫描),对使用序列LDCT扫描的RRL模型(S-RRL)进行了训练和验证。基线 RRL(B-RRL)模型仅使用基线筛查时获得的 LDCT 扫描数据进行训练和对比。547名被排除在外的患者(150名肺癌患者)被用作独立的测试集进行性能评估。接受者操作特征曲线下面积(AUC)和净再分类指数(NRI)用于评估模型在筛查出的结节分类中的性能。结果 对保留的基线扫描结果显示,S-RRL 模型的测试 AUC(0.88 [95% CI: 0.85, 0.91])明显高于 Brock 模型(AUC, 0.84 [95% CI: 0.81, 0.88]; P = .02)和 B-RRL 模型(AUC, 0.86 [95% CI: 0.83, 0.90]; P = .02)。与肺CT筛查报告和数据系统(NRI,0.29;P < .001)和布洛克模型(NRI,0.12;P = .008)相比,S-RRL模型能显著改善肺癌风险分层。结论 与 B-RRL 模型和临床模型相比,S-RRL 模型显示了在基线筛查时改进肺癌早期诊断和风险分层的潜力。关键词基于放射组学的强化学习 肺癌筛查 低剂量 CT 机器学习 © RSNA, 2024 本文有补充材料。
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Leveraging Serial Low-Dose CT Scans in Radiomics-based Reinforcement Learning to Improve Early Diagnosis of Lung Cancer at Baseline Screening.

Purpose To evaluate the feasibility of leveraging serial low-dose CT (LDCT) scans to develop a radiomics-based reinforcement learning (RRL) model for improving early diagnosis of lung cancer at baseline screening. Materials and Methods In this retrospective study, 1951 participants (female patients, 822; median age, 61 years [range, 55-74 years]) (male patients, 1129; median age, 62 years [range, 55-74 years]) were randomly selected from the National Lung Screening Trial between August 2002 and April 2004. An RRL model using serial LDCT scans (S-RRL) was trained and validated using data from 1404 participants (372 with lung cancer) containing 2525 available serial LDCT scans up to 3 years. A baseline RRL (B-RRL) model was trained with only LDCT scans acquired at baseline screening for comparison. The 547 held-out individuals (150 with lung cancer) were used as an independent test set for performance evaluation. The area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI) were used to assess the performances of the models in the classification of screen-detected nodules. Results Deployment to the held-out baseline scans showed that the S-RRL model achieved a significantly higher test AUC (0.88 [95% CI: 0.85, 0.91]) than both the Brock model (AUC, 0.84 [95% CI: 0.81, 0.88]; P = .02) and the B-RRL model (AUC, 0.86 [95% CI: 0.83, 0.90]; P = .02). Lung cancer risk stratification was significantly improved by the S-RRL model as compared with Lung CT Screening Reporting and Data System (NRI, 0.29; P < .001) and the Brock model (NRI, 0.12; P = .008). Conclusion The S-RRL model demonstrated the potential to improve early diagnosis and risk stratification for lung cancer at baseline screening as compared with the B-RRL model and clinical models. Keywords: Radiomics-based Reinforcement Learning, Lung Cancer Screening, Low-Dose CT, Machine Learning © RSNA, 2024 Supplemental material is available for this article.

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