Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non-Small Cell Lung Cancer
| Autor: | Yin Wang, Jingyun Shi, Haoyu Qi, Dong Xie, Jiajun Deng, Shouyu Chen, Chunyan Wu, Yifan Zhong, Minglei Yang, Tingting Wang, Yunlang She, Yongxiang Song, Chang Chen, Minjie Ma |
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| Rok vydání: | 2021 |
| Předmět: |
Oncology
Male medicine.medical_specialty Stage I Non-Small Cell Lung Cancer Lung Neoplasms N2 disease Risk Assessment Metastasis Cohort Studies Deep Learning Predictive Value of Tests Internal medicine Carcinoma Non-Small-Cell Lung medicine Biomarkers Tumor Humans Radiology Nuclear Medicine and imaging In patient Prospective Studies Neoplasm Staging Retrospective Studies business.industry Reproducibility of Results Neoplasms Second Primary Middle Aged medicine.disease Prognosis Survival Analysis Female Non small cell business |
| Zdroj: | Radiology. 302(1) |
| ISSN: | 1527-1315 2000-0413 |
| Popis: | 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 ( |
| Databáze: | OpenAIRE |
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