Deep Learning-based Prediction of Percutaneous Recanalization in Chronic Total Occlusion Using Coronary CT
Zhen Zhou1, Yifeng Gao1, Weiwei Zhang1
1From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Rd, Chaoyang District, Beijing 100029, China (Z.Z., Y.G., N.Z., H.W., R.W., L.X.); School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China (W.Z., Z.G., H.Z.); Keya Medical Company, Shenzhen, China (X.H.); Department of Cardiology, Chinese PLA General Hospital, Beijing, China (S.Z.); Department of Radiology, The First Hospital of China Medical University, Shenyang, China (X.D.); Cardiovascular Research Centre, Royal Brompton Hospital, London, UK (G.Y.); National Heart and Lung Institute, Imperial College London, London, UK (G.Y.); and Department of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, Calif (K.N.).
Abstract:
Background CT is helpful in guiding the revascularization of chronic total occlusion (CTO), but manual prediction scores of percutaneous coronary intervention (PCI) success have challenges. Deep learning (DL) is expected to predict success of PCI for CTO lesions more efficiently. Purpose To develop a DL model to predict guidewire crossing and PCI outcomes for CTO using coronary CT angiography (CCTA) and evaluate its performance compared with manual prediction scores.
Materials And Methods:
Participants with CTO lesions were prospectively identified from one tertiary hospital between January 2018 and December 2021 as the training set to develop the DL prediction model for PCI of CTO, with fivefold cross validation. The algorithm was tested using an external test set prospectively enrolled from three tertiary hospitals between January 2021 and June 2022 with the same eligibility criteria. All participants underwent preprocedural CCTA within 1 month before PCI. The end points were guidewire crossing within 30 minutes and PCI success of CTO.
Abstract:
Results A total of 534 participants (mean age, 57.7 years ± 10.8 [SD]; 417 [78.1%] men) with 565 CTO lesions were included. In the external test set (186 participants with 189 CTOs), the DL model saved 85.0% of the reconstruction and analysis time of manual scores (mean, 73.7 seconds vs 418.2-466.9 seconds) and had higher accuracy than manual scores in predicting guidewire crossing within 30 minutes (DL, 91.0%; CT Registry of Chronic Total Occlusion Revascularization, 61.9%; Korean Multicenter CTO CT Registry [KCCT], 68.3%; CCTA-derived Multicenter CTO Registry of Japan (J-CTO), 68.8%; P < .05) and PCI success (DL, 93.7%; KCCT, 74.6%; J-CTO, 75.1%; P < .05). For DL, the area under the receiver operating characteristic curve was 0.97 (95% CI: 0.89, 0.99) for the training test set and 0.96 (95% CI: 0.90, 0.98) for the external test set. Conclusion The DL prediction model accurately predicted the percutaneous recanalization outcomes of CTO lesions and increased the efficiency of noninvasively grading the difficulty of PCI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Pundziute-do Prado in this issue.
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