Literature DB >> 34698568

Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non-Small Cell Lung Cancer.

Yifan Zhong1, Yunlang She1, Jiajun Deng1, Shouyu Chen1, Tingting Wang1, Minglei Yang1, Minjie Ma1, Yongxiang Song1, Haoyu Qi1, Yin Wang1, Jingyun Shi1, Chunyan Wu1, Dong Xie1, Chang Chen1.   

Abstract

Background Preoperative mediastinal staging is crucial for the optimal management of clinical stage I non-small cell lung cancer (NSCLC). Purpose To develop a deep learning signature for N2 metastasis prediction and prognosis stratification in clinical stage I NSCLC. Materials and Methods In this retrospective study conducted from May 2020 to October 2020 in a population with clinical stage I NSCLC, an internal cohort was adopted to establish a deep learning signature. Subsequently, the predictive efficacy and biologic basis of the proposed signature were investigated in an external cohort. A multicenter diagnostic trial (registration number: ChiCTR2000041310) was also performed to evaluate its clinical utility. Finally, on the basis of the N2 risk scores, the instructive significance of the signature in prognostic stratification was explored. The diagnostic efficiency was quantified with the area under the receiver operating characteristic curve (AUC), and the survival outcomes were assessed using the Cox proportional hazards model. Results A total of 3096 patients (mean age ± standard deviation, 60 years ± 9; 1703 men) were included in the study. The proposed signature achieved AUCs of 0.82, 0.81, and 0.81 in an internal test set (n = 266), external test cohort (n = 133), and prospective test cohort (n = 300), respectively. In addition, higher deep learning scores were associated with a lower frequency of EGFR mutation (P = .04), higher rate of ALK fusion (P = .02), and more activation of pathways of tumor proliferation (P < .001). Furthermore, in the internal test set and external cohort, higher deep learning scores were predictive of poorer overall survival (adjusted hazard ratio, 2.9; 95% CI: 1.2, 6.9; P = .02) and recurrence-free survival (adjusted hazard ratio, 3.2; 95% CI: 1.4, 7.4; P = .007). Conclusion The deep learning signature could accurately predict N2 disease and stratify prognosis in clinical stage I non-small cell lung cancer. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Park and Lee in this issue.

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Year:  2021        PMID: 34698568     DOI: 10.1148/radiol.2021210902

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  5 in total

1.  [Exosomal FZD10 derived from non-small cell lung cancer cells promotes angiogenesis of human umbilical venous endothelial cells in vitro].

Authors:  X Wu; R Zhan; D Cheng; L Chen; T Wang; X Tang
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2022-09-20

2.  Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model.

Authors:  Xiaoling Ma; Liming Xia; Jun Chen; Weijia Wan; Wen Zhou
Journal:  Eur Radiol       Date:  2022-09-28       Impact factor: 7.034

Review 3.  A narrative review of deep learning applications in lung cancer research: from screening to prognostication.

Authors:  Jong Hyuk Lee; Eui Jin Hwang; Hyungjin Kim; Chang Min Park
Journal:  Transl Lung Cancer Res       Date:  2022-06

4.  Deep Learning Analysis Using 18F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma.

Authors:  Ming-Li Ouyang; Rui-Xuan Zheng; Yi-Ran Wang; Zi-Yi Zuo; Liu-Dan Gu; Yu-Qian Tian; Yu-Guo Wei; Xiao-Ying Huang; Kun Tang; Liang-Xing Wang
Journal:  Front Oncol       Date:  2022-07-07       Impact factor: 5.738

5.  Neglected Foreign Body Aspiration Mimicking Lung Cancer Recurrence.

Authors:  Lei Li; Meng-Jie Li; Liu Sun; Yuan-Liang Jiang; Jian Zhu
Journal:  Risk Manag Healthc Policy       Date:  2022-03-16
  5 in total

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