ISSN 1004-4140
CN 11-3017/P
ZHOU J, ZHENG Y T, JIANG S Q, et al. Machine Learning Prediction Models for Staging of Non-small Cell Lung Cancer Patients Using Radiomics[J]. CT Theory and Applications, xxxx, x(x): 1-9. DOI: 10.15953/j.ctta.2024.186. (in Chinese).
Citation: ZHOU J, ZHENG Y T, JIANG S Q, et al. Machine Learning Prediction Models for Staging of Non-small Cell Lung Cancer Patients Using Radiomics[J]. CT Theory and Applications, xxxx, x(x): 1-9. DOI: 10.15953/j.ctta.2024.186. (in Chinese).

Machine Learning Prediction Models for Staging of Non-small Cell Lung Cancer Patients Using Radiomics

More Information
  • Received Date: August 25, 2024
  • Revised Date: October 19, 2024
  • Accepted Date: October 24, 2024
  • Available Online: November 11, 2024
  • Objective: To explore the value of computed tomography (CT) radiomics machine learning classification models for predicting the staging of non-small cell lung cancer (NSCLC). Methods: We downloaded the Lung 1 dataset from the Cancer Imaging Database (TCIA), selected 291 eligible cell lung cancer patients, and divided them into two groups: Group 1 (Stage I and II) and Group 2 (Stage III and IV). We extracted 1037 radiomics features from each lesion and used the t-test and least absolute shrinkage and selection operator (LASSO) algorithm for feature selection. The CT signs of the lesions were screened using t-tests and chi-squared tests. The model was trained, and a prediction model was established using five machine learning classifiers: Logistic regression, random forest, Gaussian NB, support vector machine, and AdaBoost. The performance of the five prediction models was evaluated using receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, external validation was conducted using data acquired from 91 patients at our hospital. Results: After feature screening, 13 radiomics features with high diagnostic value were obtained for the subsequent establishment of an NSCLC patient staging prediction model. Among the five machine-learning classification models, the Random Forest classification prediction model was the best. The validation set AUC value using this model was the highest at 0.740. After external verification, the model exhibited an AUC value of 1.000, sensitivity of 1.000, and specificity of 1.000 in the training set. The AUC value of the test set was 0.698, with a sensitivity of 0.873 and a specificity of 0.500. In the CT morphological features of the case, except for the size of the lesion, there were no statistically significant differences in other features among the patients at different stages. Conclusion: The CT radiomics machine learning classification model can predict the staging of patients with NSCLC.

  • [1]
    SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA: A Cancer Journal for Clinicians, 2021, 71(3): 209‐249.
    [2]
    BRAY F, FERLAY J, SOERJOMATARAM I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA: A Cancer Journal for Clinicians, 2018, 68(6): 394-424. DOI: 10.3322/caac.21492.
    [3]
    GUO Y, SONG Q, JIANG M, et al. Histological subtypes classification of lung cancers on CT images using 3D deep learning and radiomics[J]. Academic Radiology, 2021, 28(9): e258-e266. DOI: 10.1016/j.acra.2020.06.010.
    [4]
    ALLEMANI C, MATSUDA T, DI CARLO V, et al. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population‐based registries in 71 countries[J]. Lancet, 2018, 391(10125): 1023-1075. DOI: 10.1016/S0140-6736(17)33326-3.
    [5]
    卢孔尧, 黄钢, 左艳. 非小细胞肺癌淋巴结转移预测模型研究[J]. 中国医学物理学杂志, 2022, 39(2): 182-187.

    LU K R, HUANG G, ZUO Y. Prediction model for lymph node metastasis in non-small cell lung cancer[J]. Chinese Journal of Medical Physics, 2022, 39(2): 182-187. (in Chinese).
    [6]
    周洁, 郑燕婷, 江舒琪, 等. CT影像组学联合形态学特征模型评估非小细胞肺癌患者预后生存期的价值[J]. 中国医学物理学杂志, 2024, 41(1): 18-26.

    ZHOU J, ZHENG Y T, JIANG S Q, et al. Value of CT radiomics combined with morphological features in predicting the prognosis of patients with non-small cell lung cancer[J]. Chinese Journal of Medical Physics, 2024, 41(1): 18-26. (in Chinese).
    [7]
    COROLLER T P, AGRAWAL V, NARAYAN V, et al. Radiomic phenotype features predict pathological response in non-small cell lung cancer[J]. Radiotherapy and Oncology, 2016, 119(3): 480-486. DOI: 10.1016/j.radonc.2016.04.004.
    [8]
    许昌, 刘玉霞, 郭兰田, 等. 基于薄层CT的影像组学和形态学特征联合模型在预测磨玻璃样肺腺癌浸润程度的价值[J]. 临床放射学杂志, 2022, 41(1): 64-69.

    XU C, LIU Y X, GUO L T, et al. Radiomics features combined with morphological signs based on thin-slice CT for predicting the invasive of pulmonary adenocarcinoma appearing as ground-glass nodules[J]. Journal of Clinical Radiology, 2022, 41(1): 64-69. (in Chinese).
    [9]
    郑慧, 李建玉, 王珊, 等. 基于肺磨玻璃结节CT征象的诊断模型列线图评估肺癌浸润性[J]. 放射学实践, 2021, 36(4): 470-474.

    ZHENG H, LI J Y, WANG S, et al. Evaluation on the invasion of lung cancer by diagnostic model nomogram based on the CT characteristics of pulmonary ground glass nodules[J]. Radiologic Practice, 2021, 36(4): 470-474. (in Chinese).
    [10]
    代平, 何其舟, 王思凯, 等. CT定量分析预测肺部肿瘤性磨玻璃结节病理侵袭性的价值[J]. 放射学实践, 2019, 34(10): 1108-1112.

    DIA P, HE Q Z, WANG S K, et al. Quantitative CT analysis of pulmonary ground-glass nodule predicts histological invasiveness[J]. Radiologic Practice, 2019, 34(10): 1108-1112. (in Chinese).
    [11]
    贾守勤, 张旭, 王武章, 等. 肺结核合并肺癌的临床特点及MSCT影像学分析[J]. 医学影像学杂志, 2021, 31(10): 1682-1685.

    JIA S Q, ZHANG X, WANG W Z, et al. Clinical characteristis of the pulmonary tuberculosis with lung cancer and analysis of MSCT imaging[J]. Journal of Medical Imaging, 2021, 31(10): 1682-1685. (in Chinese).
    [12]
    MENG X, XIA W, XIE P, et al. Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer[J]. European Radiology, 2019, 29: 3200-3209. DOI: 10.1007/s00330-018-5763-x. (in Chinese).
    [13]
    中华医学会肿瘤学分会, 中华医学杂志社. 中华医学会肺癌临床诊疗指南(2023版)[J]. 中华医学杂志, 2023, 103(27): 2037-2074.

    Oncology Society of Chinese Medical Association, Chinese Medical Association Publishing House. Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2023 edition)[J]. National Medical Journal of China, 2023, 103(27): 2037-2074. (in Chinese).
    [14]
    崔婷婷. 多层螺旋CT在肺癌临床诊断中的应用价值及征象特征研究[J]. 影像研究与医学应用, 2021, 5(20): 8-9.

    CUI T T. Study on the application value of multilayered spiral CT in the clinical diagnosis of lung cancer[J]. Journal of Imaging Research and Medical, 2021, 5(20): 8-9. (in Chinese).
    [15]
    钱琴, 陆云霞, 乔正博, 等. Cys-C、Hcy、CYFRA21-1在肺癌分期及预后中的价值[J]. 分子诊断与治疗杂志, 2023, 15(8): 1397-1401.

    QIAN Q, LU Y X, QIAO Z B, et al. Value of Cys-C, Hcy and CYFRA21-1 in staging and prognosis of lung cancer[J]. Journal of Molecular Diagnostics and Therapy, 2023, 15(8): 1397-1401. (in Chinese).
    [16]
    陈少武. 能谱CT定量参数结合血清NSE和HOMA-IR与肺癌分期的相关性[J]. 中国卫生工程学, 2023, 22(2): 251-252,255.

    CHEN S W. The correlation between quantitative parameters of spectral CT combined with serum NSE and HOMA-IR and lung cancer staging[J]. Chinese Journal of Public Health Engineering, 2023, 22(2): 251-252,255. (in Chinese).
    [17]
    陈宝华, 段强军. 血清CEA、NSE联合影像学特征在肺癌分期及预后中的价值[J]. 分子诊断与治疗杂志, 2022, 14(9): 1615-1619.

    CHEN B H, DUAN Q J. The value of serum CEA and NSE combined with imaging features in lung cancer staging and prognosis[J]. Journal of Molecular Diagnostics and Therapy, 2022, 14(9): 1615-1619. (in Chinese).
    [18]
    欧阳锦, 罗亭, 余石群, 等. 基于GEO数据库结合CT影像预测肺癌临床分期的分子标志物及其诊断预测模型的建立[J]. 南昌大学学报(医学版), 2021, 61(5): 1-7.

    OUYANG J, LUO T, YU S Q, et al. Prediction of molecular markers for lung cancer staging based on GEO database combined with CT images and establishment of diagnosis and prediction model[J]. Journal of Nanchang University (Medical Sciences), 2021, 61(5): 1-7. (in Chinese).
    [19]
    BINCZYK F, PRAZUCH W, BOZEK P, et al. Radiomics and artificial intelligence in lung cancer screening[J]. Translational Lung Cancer Research, 2021, 10(2): 1186-1199. DOI: 10.21037/tlcr-20-708.
    [20]
    Imbriaco M, Cuocolo R. Does Texture Analysis of MR Images of Breast Tumors Help Predict Response to Treatment?[J]. Radiology, 2018, 286(2): 421-423. DOI: 10.1148/radiol.2017172454.
    [21]
    陈晓, 杨斌. 影像组学技术在非小细胞肺癌预后中的应用研究[J]. CT理论与应用研究(中英文), 2024, 33(3): 385-390.

    CHEN X, YANG B. The application of radiomics in the prognosis of non-small cell lung cancer[J]. Computerized Tomography Theory and Applications, 2024, 33(3): 385-390. (in Chinese).
    [22]
    FENG B, CHEN X, CHEN Y, et al. Solitary solid pulmonary nodules: A CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas[J]. European Radiology, 2020, 30: 6497-6507. DOI: 10.1007/s00330-020-07024-z.
    [23]
    XIA X, GONG J, HAO W, et al. Comparison and fusion of deep learn-ing and radiomics features of ground-glass nodules to predict the invasiveness risk of stage-i lung adenocarcinomas in CT scan[J]. Frontiers in Oncology, 2020, 10: 418. DOI: 10.3389/fonc.2020.00418.
    [24]
    LUBNER M G, SMITH A D, SANDRASEGARAN K, et al. CT texture analysis: Definitions, applications, biologic correlates, and challenges[J]. RadioGraphics, 2017, 37(5): 1483-1503. DOI: 10.1148/rg.2017170056.
    [25]
    周洁, 郑燕婷, 江舒琪, 等. 非小细胞肺癌患者预后生存时间范围的机器学习预测模型研究[J]. 放射学实践, 2024, 39(5): 622-628.

    ZHOU J, ZHENG Y T, JIANG S Q, et al. Study on machine learning predictive model for prognostic survival time range of non-small cell lung cancer patients[J]. Radiologic Practice, 2024, 39(5): 622-628. (in Chinese).
    [26]
    SU X, XU Y, TAN Z, et al. Prediction for cardiovascular diseases based on laboratory data: An analysis of random forest model[J]. Journal of Clinical Laboratory Analysis, 2020, 34(9): e23421. DOI: 10.1002/jcla.23421.

Catalog

    Article views (95) PDF downloads (17) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return