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CHEN X, YANG B. The Application of Radiomics in the Prognosis of Non-small Cell Lung Cancer[J]. CT Theory and Applications, 2024, 33(3): 385-390. DOI: 10.15953/j.ctta.2023.071. (in Chinese).
Citation: CHEN X, YANG B. The Application of Radiomics in the Prognosis of Non-small Cell Lung Cancer[J]. CT Theory and Applications, 2024, 33(3): 385-390. DOI: 10.15953/j.ctta.2023.071. (in Chinese).
shu

The Application of Radiomics in the Prognosis of Non-small Cell Lung Cancer

  • 1.

    Department of Medical Imaging, Jining First People’s Hospital, Jining 272002, China

  • 2.

    Department of Medical Imaging, Calmette Hospital, The First Hospital of Kunming, Kunming 650051, China

More Information
  • Received Date: March 18, 2023
  • Revised Date: August 12, 2023
  • Accepted Date: September 11, 2023
  • Available Online: October 30, 2023
    Graphical Abstract
    • Abstract

  • Lung cancer remains a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) as the most prevalent type. Accurately predicting NSCLC prognosis is crucial for optimizing patient survival. However, traditional assessment tools often lack the comprehensive and precise capability to effectively stratify patient risk. Recent research has focused on exploring the potential of imaging histology technology for NSCLC prognosis. This article delves into the core principles of imaging histology and reviews the current state of research on its application in predicting NSCLC outcomes.

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    • References (28)
  • [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. DOI: 10.3322/caac.21660.
    [2]
    YOSHIDA Y, MURAYAMA T, SATO Y, et al. Gender differences in long-term survival after surgery for non-small cell lung cancer[J]. The Thoracic and Cardiovascular Surgeon, 2016, 64(6): 507−514. DOI:10.1055/ s-0035-1558995.
    [3]
    ETTINGER D S, WOOD D E, AISNER D L, et al. Non-small cell lung cancer, version 5.2017, NCCN clinical practice guidelines in oncology[J]. Journal of the National Comprehensive Cancer Network, 2017, 15(4): 504−535. DOI: 10.6004/jnccn.2017.0050.
    [4]
    PARK S, LEE S M, KIM S, et al. Performance of radiomics models for survival prediction in non-small-cell lung cancer: Influence of CT slice thickness[J]. European Radiology, 2021, 31(5): 2856−2865. DOI: 10.1007/s00330-020-07423-2.
    [5]
    ZHAO J, SUN L, SUN K, et al. Development and validation of a radiomics nomogram for differentiating pulmonary cryptococcosis and lung adenocarcinoma in solitary pulmonary solid nodule[J]. Frontiers in Oncology, 2021, 11: 759840. DOI: 10.3389/fonc.2021.759840.
    [6]
    LAMBIN P, LEIJENAAR R T H, DEIST T M, et al. Radiomics: The bridge between medical imaging and personalized medicine[J]. Nature Reviews. Clinical Oncology, 2017, 14(12): 749−762. DOI: 10.1038/nrclinonc.2017.141.
    [7]
    LAMBIN P, RIOS-VELAZQUEZ E, LEIJENAAR R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis[J]. European Journal of Cancer, 2012, 48(4): 441−446. DOI: 10.1016/j.ejca.2011.11.036.
    [8]
    MAYERHOEFER M E, MATERKA A, LANGS G, et al. Introduction to radiomics[J]. Journal of Nuclear Medicine, 2020, 61(4): 488−495. DOI: 10.2967/jnumed.118.222893.
    [9]
    GILLIES R J, KINAHAN P E, HRICAK H. Radiomics: Images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563−577. DOI: 10.1148/radiol.2015151169.
    [10]
    YIP S S, AERTS H J. Applications and limitations of radiomics[J]. Physics in Medicine and Biology, 2016, 61(13): R150−R166. DOI: 10.1088/0031-9155/61/13/R150.
    [11]
    LIMKIN E J, SUN R, DERCLE L, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology[J]. Annals of Oncology, 2017, 28(6): 1191−1206. DOI: 10.1093/annonc/mdx034.
    [12]
    DHARA A K, MUKHOPADHYAY S, DUTTA A, et al. Content-based image retrieval system for pulmonary nodules: Assisting radiologists in self-learning and diagnosis of lung cancer[J]. Journal of Digital Imaging, 2017, 30(1): 63−77. DOI: 10.1007/s10278-016-9904-y.
    [13]
    MIZUNO T, ARIMURA T, KURODA H, et al. Current outcomes of postrecurrence survival in patients after resection of non-small cell lung cancer[J]. Journal of Thoracic Disease, 2018, 10(3): 1788−1796. DOI: 10.21037/jtd.2018.01.148.
    [14]
    YANG L, YANG J, ZHOU X, et al. Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients[J]. European Radiology, 2019, 29(5): 2196−2206. DOI: 10.1007/s00330-018-5770-y.
    [15]
    WANG L, DONG T, XIN B, et al. Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer[J]. European Radiology, 2019, 29(6): 2958−2967. DOI: 10.1007/s00330-018-5949-2.
    [16]
    YANG B, ZHONG J, ZHONG J, et al. Development and validation of a radiomics nomogram based on 18F-fluorodeoxyglucose positron emission tomography/computed tomography and clinicopathological factors to predict the survival outcomes of patients with non-small cell lung cancer[J]. Frontiers in Oncology, 2020, 10: 1042. DOI: 10.3389/fonc.2020.01042.
    [17]
    YOO J, LEE J, CHEON M, et al. Radiomics analysis of 18F-FDG PET/CT for prognosis prediction in patients with stage III non-small cell lung cancer undergoing neoadjuvant chemoradiation therapy followed by surgery[J]. Cancers, 2023, 15(7): 2012. DOI: 10.3390/cancers15072012.
    [18]
    CHOE J, LEE S M, DO K H, et al. Outcome prediction in resectable lung adenocarcinoma patients: Value of CT radiomics[J]. European Radiology, 2020, 30(9): 4952−4963. DOI: 10.1007/s00330-020-06872-z.
    [19]
    LIU K, LI K, WU T, et al. Improving the accuracy of prognosis for clinical stage I solid lung adenocarcinoma by radiomics models covering tumor per se and peritumoral changes on CT[J]. European Radiology, 2022, 32(2): 1065−1077. DOI: 10.1007/s00330-021-08194-0.
    [20]
    XIU D, MO Y, LIU C, et al. Integrative nomogram of computed tomography radiomics, clinical, and tumor immune features for analysis of disease-free survival of NSCLC patients with surgery[J]. Journal of Oncology, 2023, 2023: 8607062. DOI: 10.1155/2023/8607062.
    [21]
    HE L, LI Z, CHEN X, et al. A radiomics prognostic scoring system for predicting progression-free survival in patients with stage IV non-small cell lung cancer treated with platinum-based chemotherapy[J]. Chinese Journal of Cancer Research, 2021, 33(5): 592−605. DOI: 10.21147/j.issn.1000-9604.2021.05.06.
    [22]
    LI J, ZHANG B, GE S, et al. Prognostic value of 18F-FDG PET/CT radiomic model based on primary tumor in patients with non-small cell lung cancer: A large single-center cohort study[J]. Frontiers in Oncology, 2022, 12: 1047905. DOI: 10.3389/fonc.2022.1047905.
    [23]
    CONG M, FENG H, REN J L, et al. Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer[J]. Lung Cancer, 2020, 139: 73−79. DOI:10. 1016/j.lungcan.2019.11.003.
    [24]
    RAN J, CAO R, CAI J, et al. Development and validation of a nomogram for preoperative prediction of lymph node metastasis in lung adenocarcinoma based on radiomics signature and deep learning signature[J]. Frontiers in Oncology, 2021, 11: 585942. DOI: 10.3389/fonc.2021.585942.
    [25]
    WANG X, ZHAO X, LI Q, et al. Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT?[J]. European Radiology, 2019, 29(11): 6049−6058. DOI: 10.1007/s00330-019-06084-0.
    [26]
    CHEN A, LU L, PU X, et al. CT-based radiomics model for predicting brain metastasis in category T1 lung adenocarcinoma[J]. American Journal of Roentgenology, 2019, 213(1): 134−139. DOI: 10.2214/AJR.18.20591.
    [27]
    DING Z, WANG Y, XIA C, et al. Thoracic CT radiomics analysis for predicting synchronous brain metastasis in patients with lung cancer[J]. Diagnostic and Interventional Radiology, 2022, 28(1): 39−49. DOI: 10.5152/dir.2021.21677.
    [28]
    CHEN L, YU L, LI X, et al. Value of CT radiomics and clinical features in predicting bone metastases in patients with NSCLC[J]. Contrast Media & Molecular Imaging, 2022, 2022: 7642511. DOI: 10.1155/2022/7642511.
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