A Nomogram Based on CT Deep Learning Signature: A Potential Tool for the Prediction of Overall Survival in Resected Non-Small Cell Lung Cancer Patients
Received 30 December 2020
Accepted for publication 2 March 2021
Published 30 March 2021 Volume 2021:13 Pages 2897—2906
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Seema Singh
Ting Lin,1,* Jinhai Mai,2,3,* Meng Yan,1,* Zhenhui Li,4 Xianyue Quan,1 Xin Chen3,5
1Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, People’s Republic of China; 2School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, People’s Republic of China; 3Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, People’s Republic of China; 4Department of Radiology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, People’s Republic of China; 5Department Of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xin Chen
Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, People’s Republic of China
Email [email protected]
Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, People’s Republic of China
Email [email protected]
Purpose: To develop and further validate a deep learning signature-based nomogram from computed tomography (CT) images for prediction of the overall survival (OS) in resected non-small cell lung cancer (NSCLC) patients.
Patients and Methods: A total of 1792 deep learning features were extracted from non-enhanced and venous-phase CT images for each NSCLC patient in training cohort (n=231). Then, a deep learning signature was built with the least absolute shrinkage and selection operator (LASSO) Cox regression model for OS estimation. At last, a nomogram was constructed with the signature and other independent clinical risk factors. The performance of nomogram was assessed by discrimination, calibration and clinical usefulness. In addition, in order to quantify the improvement in performance added by deep learning signature, the net reclassification improvement (NRI) was calculated. The results were validated in external validation cohort (n=77).
Results: A deep learning signature with 9 selected features was significantly associated with OS in both training cohort (hazard ratio [HR]=5.455, 95% CI: 3.393– 8.769, P< 0.001) and external validation cohort (HR=3.029, 95% CI: 1.673– 5.485, P=0.004). The nomogram combining deep learning signature with clinical risk factors of TNM stage, lymphatic vessel invasion and differentiation grade showed favorable discriminative ability with C-index of 0.800 as well as a good calibration, which was validated in external validation cohort (C-index=0.723). Additional value of deep learning signature to the nomogram was statistically significant (NRI=0.093, P=0.027 for training cohort; NRI=0.106, P=0.040 for validation cohort). Decision curve analysis confirmed the clinical usefulness of this nomogram in predicting OS.
Conclusion: The deep learning signature-based nomogram is a robust tool for prognostic prediction in resected NSCLC patients.
Keywords: deep learning, non-small cell lung cancer, prognosis, nomogram
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