Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lun
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COMPUTED TOMOGRAPHY
Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas Bao Feng 1,2 & XiangMeng Chen 1 & YeHang Chen 2 & SenLiang Lu 2 & KunFeng Liu 3 & KunWei Li 3 & ZhuangSheng Liu 1 & YiXiu Hao 1,4 & Zhi Li 2 & ZhiBin Zhu 5 & Nan Yao 1 & GuangYuan Liang 1 & JiaYu Zhang 1 & WanSheng Long 1 & XueGuo Liu 3 Received: 29 February 2020 / Revised: 21 April 2020 / Accepted: 9 June 2020 # European Society of Radiology 2020
Abstract Objectives To evaluate the differential diagnostic performance of a computed tomography (CT)-based deep learning nomogram (DLN) in identifying tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) presenting as solitary solid pulmonary nodules (SSPNs). Methods Routine CT images of 550 patients with SSPNs were retrospectively obtained from two centers. A convolutional neural network was used to extract deep learning features from all lesions. The training set consisted of data for 218 patients. The least absolute shrinkage and selection operator logistic regression was used to create a deep learning signature (DLS). Clinical factors and CT-based subjective findings were combined in a clinical model. An individualized DLN incorporating DLS, clinical factors, and CT-based subjective findings was constructed to validate the diagnostic ability. The performance of the DLN was assessed by discrimination and calibration using internal (n = 140) and external validation cohorts (n = 192). Results DLS, gender, age, and lobulated shape were found to be independent predictors and were used to build the DLN. The combination showed better diagnostic accuracy than any single model evaluated using the net reclassification improvement method (p < 0.05). The areas under the curve in the training, internal validation, and external validation cohorts were 0.889 (95% confidence interval [CI], 0.839–0.927), 0.879 (95% CI, 0.813–0.928), and 0.809 (95% CI, 0.746–0.862), respectively. Decision curve analysis and stratification analysis showed that the DLN has potential generalization ability. Conclusions The CT-based DLN can preoperatively distinguish between LAC and TBG in patients presenting with SSPNs. Key Points • The deep learning nomogram was developed to preoperatively differentiate TBG from LAC in patients with SSPNs. • The performance of the deep learning feature was superior to that of the radiomics feature. • The deep learning nomogram achieved superior performance compared to the deep learning signature, the radiomics signature, or the clinical model alone. Keywords Tuberculosis . Lung adenocarcinoma . Solitary pulmonary nodule . Deep learning Bao Feng and XiangMeng Chen contributed equally to this work. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-07024-z) contains supplementary material, which is available to authorized users. * WanSheng Long [email protected]
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The Department of Radiology, The Fifth Affiliated Hospital Sun Yat-Sen University, NO.52 Meihua
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