Prognostic Analysis Method for Non-small Cell Lung Cancer Based on CT Radiomics

doi:10.3969/j.issn.0372-2112.2020.04.003

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Prognostic Analysis Method for Non-small Cell Lung Cancer Based on CT Radiomics

更新时间：2025-07-08

- Prognostic Analysis Method for Non-small Cell Lung Cancer Based on CT Radiomics
- Acta Electronica Sinica Vol. 48, Issue 4, Pages: 637-642(2020)
- 作者机构：
  
  上海理工大学医学影像工程研究所,上海,200093
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2020.04.003
  CLC： TP391
- Published Online：25 April 2020，
  
  Published：2020
- 稿件说明：
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Prognostic Analysis Method for Non-small Cell Lung Cancer Based on CT Radiomics[J]. Acta Electronica Sinica, 2020, 48(4): 637-642.
DOI：

Prognostic Analysis Method for Non-small Cell Lung Cancer Based on CT Radiomics[J]. Acta Electronica Sinica, 2020, 48(4): 637-642. DOI： 10.3969/j.issn.0372-2112.2020.04.003.

摘要

为了辅助医生规划非小细胞肺癌（Non-Small Cell Lung Cancer，NSCLC）患者治疗和复查方案，提出了一种基于CT影像组学的NSCLC预后分析方法.首先，对患者肺部CT影像中的肿瘤区域进行分割；然后，对肿瘤区域进行影像组学特征提取、优化；最后，将优化后的特征数据与患者的预后生存情况作为输入，利用机器学习的方法构建预后分析模型，预测患者的预后生存时间范围.选用124例NSCLC患者数据进行实验，以具有临床意义的3年生存期为预测界限，对患者预后生存时间范围进行预测.实验结果表明，预后分析模型的预测准确率达到91.9%，可以有效地辅助医生对非小细胞肺癌患者的预后情况进行更加精准的评估，制定出更具个性化的治疗与复查方案.

Abstract

In order to assist doctors in planning treatment and review programs for non-small cell lung cancer (NSCLC) patients

a prognostic survival analysis method based on CT radiomics was proposed. First

we segmented the tumor areas in the lung CT images. Then

we extracted and optimized the radiomics features. Finally

the optimized features and the patients’ prognosis survival were taken as input

and the prognostic analysis model was constructed by using machine learning method to predict the prognosis survival time range of the patients. The data of 124 NSCLC patients were selected and the clinical significance of 3-year survival was used as the predictive limit to predict the prognosis survival time range. The experimental results showed the prediction accuracy of the model reached 91.9%

which could effectively assist doctors to carry out more accurate assessment and develop more personalized treatment and review programs for NSCLC patients.

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SOSNet: An Asymmetric Encoder-Decoder Structure Model for Automatic Segmenting Non-Small Cell Lung Cancer CT Images

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School of Computer Science， Shaanxi Normal University

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