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

被引:7
作者
Weir-McCall, Jonathan R. [1 ,3 ]
Debruyn, Elise [6 ]
Harris, Scott [4 ]
Qureshi, Nagmi R. [3 ]
Rintoul, Robert C. [2 ]
Gleeson, Fergus, V [5 ]
Gilbert, Fiona J. [1 ]
机构
[1] Univ Cambridge, Biomed Res Ctr, Dept Radiol, Sch Clin Med, Cambridge, England
[2] Univ Cambridge, Dept Oncol, Dept Thorac Oncol, Cambridge, England
[3] Royal Papworth Hosp, Dept Radiol, Cambridge, Cambridgeshire, England
[4] Univ Southampton, Fac Publ Hlth Sci & Med Stat, Southampton, England
[5] Univ Oxford, Churchill Hosp, Dept Radiol, Oxford, England
[6] Univ Illinois, Coll Med, Chicago, IL USA
关键词
diagnostic test accuracy; machine learning; positron emission tomography computed tomography; solitary pulmonary nodule; tomography; X-ray computed; CANCER; PROBABILITY; BENIGN; TRIAL;
D O I
10.1016/j.chest.2022.08.2227
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
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 con-volutional 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 his-tologic 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
引用
收藏
页码:444 / 454
页数:11
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