卷积神经网络评估孤立性肺结节的诊断准确性与 PET/CT 成像及使用平扫和增强 CT 成像的动态对比增强 CT 成像的比较。
Diagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With PET With CT Imaging and Dynamic Contrast-Enhanced CT Imaging Using Unenhanced and Contrast-Enhanced CT Imaging.
机构信息
Department of Radiology, University of Cambridge School of Clinical Medicine, Biomedical Research Centre, University of Cambridge; Department of Radiology, Royal Papworth Hospital, Cambridge.
College of Medicine, University of Illinois at Chicago, Chicago, IL.
出版信息
Chest. 2023 Feb;163(2):444-454. doi: 10.1016/j.chest.2022.08.2227. Epub 2022 Sep 8.
BACKGROUND
Solitary pulmonary nodules (SPNs) measuring 8 to 30 mm in diameter require further workup to determine the likelihood of malignancy.
RESEARCH QUESTION
What is the diagnostic performance of a lung cancer prediction convolutional neural network (LCP-CNN) in SPNs using unenhanced and contrast-enhanced CT imaging compared with the current clinical workup?
STUDY DESIGN AND METHODS
This was a post hoc analysis of the Single Pulmonary Nodule Investigation: Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules trial, a prospective multicenter study comparing the diagnostic accuracy of dynamic contrast-enhanced (DCE) CT imaging with PET imaging in SPNs. The LCP-CNN was designed and validated in an external cohort. LCP-CNN-generated risk scores were created from the noncontrast and contrast-enhanced CT scan images from the DCE CT imaging. The gold standard was histologic analysis or 2 years of follow-up. The area under the receiver operating characteristic curves (AUC) were calculated using LCP-CNN score, maximum standardized uptake value, and DCE CT scan maximum enhancement and were compared using the DeLong test.
RESULTS
Two hundred seventy participants (mean ± SD age, 68.3 ± 8.8 years; 49% women) underwent PET with CT scan imaging and DCE CT imaging with CT scan data available centrally for LCP-CNN analysis. The accuracy of the LCP-CNN on the noncontrast images (AUC, 0.83; 95% CI, 0.79-0.88) was superior to that of DCE CT imaging (AUC, 0.76; 95% CI, 0.69-0.82; P = .03) and equal to that of PET with CT scan imaging (AUC, 0.86; 95% CI, 0.81-0.90; P = .35). The presence of contrast resulted in a small reduction in diagnostic accuracy, with the AUC falling from 0.83 (95% CI, 0.79-0.88) on the noncontrast images to 0.80 to 0.83 after contrast (P < .05 for 240 s after contrast only).
INTERPRETATION
An LCP-CNN algorithm provides an AUC equivalent to PET with CT scan imaging in the diagnosis of solitary pulmonary nodules.
TRIAL REGISTRATION
ClinicalTrials.gov Identifier; No.: NCT02013063.
背景
直径为 8 至 30 毫米的孤立性肺结节需要进一步检查以确定恶性肿瘤的可能性。
研究问题
与当前的临床检查相比，使用未增强和增强 CT 成像的肺癌预测卷积神经网络（LCP-CNN）在 SPN 中的诊断性能如何？
研究设计和方法
这是一项对前瞻性多中心研究的事后分析，该研究比较了动态对比增强（DCE）CT 成像与 SPN 中 PET 成像的诊断准确性，即单肺结节研究：动态对比增强 CT 成像对孤立性肺结节的特征分析的准确性和成本效益，该研究比较了 DCE CT 成像与 PET 成像的诊断准确性。LCP-CNN 是在外部队列中设计和验证的。LCP-CNN 从 DCE CT 成像的非对比和增强 CT 扫描图像中生成风险评分。金标准是组织学分析或 2 年随访。使用 LCP-CNN 评分、最大标准化摄取值和 DCE CT 扫描最大增强来计算受试者工作特征曲线下的面积（AUC），并使用 DeLong 检验进行比较。
结果
270 名参与者（平均年龄 ± 标准差，68.3 ± 8.8 岁；49%女性）接受了 PET 与 CT 扫描成像以及 DCE CT 扫描与 CT 扫描数据的联合检查，这些数据可在中心位置用于 LCP-CNN 分析。LCP-CNN 在非对比图像上的准确性（AUC，0.83；95%CI，0.79-0.88）优于 DCE CT 成像（AUC，0.76；95%CI，0.69-0.82；P =.03），与 PET 与 CT 扫描成像的准确性相当（AUC，0.86；95%CI，0.81-0.90；P =.35）。对比剂的存在导致诊断准确性略有下降，非对比图像上的 AUC 从 0.83（95%CI，0.79-0.88）下降至 0.80 至 0.83（仅在对比后 240 秒时 P<.05）。
解释
LCP-CNN 算法在孤立性肺结节的诊断中提供了与 PET 与 CT 扫描成像相当的 AUC。
试验注册
ClinicalTrials.gov 标识符；编号：NCT02013063。
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