Prediction of visceral pleural invasion in lung cancer on CT: deep learning model achieves a radiologist-level performan
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Prediction of visceral pleural invasion in lung cancer on CT: deep learning model achieves a radiologist-level performance with adaptive sensitivity and specificity to clinical needs Hyewon Choi 1 & Hyungjin Kim 1,2 & Wonju Hong 1 & Jongsoo Park 1 & Eui Jin Hwang 1 & Chang Min Park 1,2,3,4 & Young Tae Kim 4,5 & Jin Mo Goo 1,2,3,4 Received: 25 June 2020 / Revised: 30 September 2020 / Accepted: 15 October 2020 # European Society of Radiology 2020
Abstract Objectives To develop and validate a preoperative CT-based deep learning model for the prediction of visceral pleural invasion (VPI) in early-stage lung cancer. Methods In this retrospective study, dataset 1 (for training, tuning, and internal validation) included 676 patients with clinical stage IA lung adenocarcinomas resected between 2009 and 2015. Dataset 2 (for temporal validation) included 141 patients with clinical stage I adenocarcinomas resected between 2017 and 2018. A CT-based deep learning model was developed for the prediction of VPI and validated in terms of discrimination and calibration. An observer performance study and a multivariable regression analysis were performed. Results The area under the receiver operating characteristic curve (AUC) of the model was 0.75 (95% CI, 0.67–0.84), which was comparable to those of board-certified radiologists (AUC, 0.73–0.79; all p > 0.05). The model had a higher standardized partial AUC for a specificity range of 90 to 100% than the radiologists (all p < 0.05). The high sensitivity cutoff (0.245) yielded a sensitivity of 93.8% and a specificity of 31.2%, and the high specificity cutoff (0.448) resulted in a sensitivity of 47.9% and a specificity of 86.0%. Two of the three radiologists provided highly sensitive (93.8% and 97.9%) but not specific (48.4% and 40.9%) diagnoses. The model showed good calibration (p > 0.05), and its output was an independent predictor for VPI (adjusted odds ratio, 1.07; 95% CI, 1.03–1.11; p < 0.001). Conclusions The deep learning model demonstrated a radiologist-level performance. The model could achieve either highly sensitive or highly specific diagnoses depending on clinical needs. Key Points • The preoperative CT-based deep learning model demonstrated an expert-level diagnostic performance for the presence of visceral pleural invasion in early-stage lung cancer. • Radiologists had a tendency toward highly sensitive, but not specific diagnoses for the visceral pleural invasion. Keywords Deep learning . Radiologists . Pleura . Lung neoplasms . Multidetector computed tomography
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-07431-2) contains supplementary material, which is available to authorized users. * Hyungjin Kim [email protected] 1
Department of Radiology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
2
Department of Radiology, Seoul National University College of Medicine, 101, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
3
Institute of Radiation Medicine, Seoul
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