ISSN 1004-4140
CN 11-3017/P
ZHOU J W, FENG F. Research progress on CT radiomics of esophageal cancer[J]. CT Theory and Applications, 2022, 31(5): 687-696. DOI: 10.15953/j.ctta.2021.006. (in Chinese).
Citation: ZHOU J W, FENG F. Research progress on CT radiomics of esophageal cancer[J]. CT Theory and Applications, 2022, 31(5): 687-696. DOI: 10.15953/j.ctta.2021.006. (in Chinese).

Research Progress on CT Radiomics of Esophageal Cancer

More Information
  • Received Date: September 25, 2021
  • Accepted Date: January 11, 2022
  • Available Online: January 26, 2022
  • Published Date: September 30, 2022
  • Esophageal cancer which results in a relatively higher death rate is one of the most common cancers throughout the world. CT radiomics is a study to extract radiomics characteristics which are generalized from a large amount of CT images. These characteristics then underwent high-throughput quantitative analysis so that more heterogeneity information of the caner can be obtained. CT radiomics has been gradually used in forecast clinical stages and pathological differentiation of esophageal cancer in recent years, and applied in assessment of treatment effect and prognosis evaluation as well. This paper focuses on the application of CT radiomics in esophageal cancer and its progress.
  • [1]
    UHLENHOPP D J, THEN E O, SUNKARA T, et al. Epidemiology of esophageal cancer: Update in global trends, etiology and risk factors[J]. Clinical Journal of Gastroenterol, 2020, 13(6): 1010−1021. doi: 10.1007/s12328-020-01237-x
    [2]
    SHAO D, VOGTMANN E, LIU A, et al. Microbial characterization of esophageal squamous cell carcinoma and gastric cardia adenocarcinoma from a high-risk region of China[J]. Cancer, 2019, 125(22): 3993−4002. doi: 10.1002/cncr.32403
    [3]
    OU J, LI R, ZENG R, et al. CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: A case control study[J]. Cancer Imaging, 2019, 19(1): 66. doi: 10.1186/s40644-019-0254-0
    [4]
    HE H, CHEN N, HOU Y, et al. Trends in the incidence and survival of patients with esophageal cancer: A SEER database analysis[J]. Thoracic Cancer, 2020, 11(5): 1121−1128. doi: 10.1111/1759-7714.13311
    [5]
    SCHLOTTMANN F, GABER C, STRASSLE P D, et al. Disparities in esophageal cancer: Less treatment, less surgical resection, and poorer survival in disadvantaged patients[J]. Diseases of the Esophagus, 2020, 33(2): 1−9.
    [6]
    Van TIMMEREN J E, CESTER D, TANADINI-LANG S, et al. Radiomics in medical imaging—“how-to”guide and critical reflection[J]. Insights into Imaging, 2020, 11(1): 91. doi: 10.1186/s13244-020-00887-2
    [7]
    DING H, WU C, LIAO N, et al. Radiomics in oncology: A 10-year bibliometric analysis[J]. Frontiers in Oncology, 2021, 11: 689802. doi: 10.3389/fonc.2021.689802
    [8]
    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
    [9]
    MAYERHOEFER M E, MATERKA A, LANGS G, et al. Introduction to radiomics[J]. The Journal of Nuclear Medicine, 2020, 61(4): 488−495. doi: 10.2967/jnumed.118.222893
    [10]
    ORLHAC F, FROUIN F, NIOCHE C, et al. Validation of a method to compensate multicenter effects affecting CT radiomics[J]. Radiology, 2019, 291(1): 53−59. doi: 10.1148/radiol.2019182023
    [11]
    BIBAULT J E, XING L, GIRAUD P, et al. Radiomics: A primer for the radiation oncologist[J]. Cancer Radiother, 2020, 24(5): 403−410. doi: 10.1016/j.canrad.2020.01.011
    [12]
    BERENGUER R, PASTOR-JUAN M D R, CANALES-VÁZQUEZ J, et al. Radiomics of CT features maybe nonreproducible and redundant: Influence of CT acquisition parameters[J]. Radiology, 2018, 288(2): 407−415. doi: 10.1148/radiol.2018172361
    [13]
    AERTS H J W L. Data science in radiology: A path forward[J]. Clinical Cancer Research, 2018, 24(3): 532−534. doi: 10.1158/1078-0432.CCR-17-2804
    [14]
    JAYAPRAKASAM V S, YEH R, KU G Y, et al. Role of imaging in esophageal cancer management in 2020: Update for radiologists[J]. American Journal of Roentgenology, 2020, 215(5): 1072−1084. doi: 10.2214/AJR.20.22791
    [15]
    HUANG F L, YU S J. Esophageal cancer: Risk factors, genetic association, and treatment[J]. Asian Journal of Surgery, 2018, 41(3): 210−215. doi: 10.1016/j.asjsur.2016.10.005
    [16]
    WU L, WANG C, TAN X, et al. Radiomics approach for preoperative identification of stages Ⅰ-Ⅱ and Ⅲ-Ⅳ of esophageal cancer[J]. Chinese Journal of Cancer Research, 2018, 30(4): 396−405. doi: 10.21147/j.issn.1000-9604.2018.04.02
    [17]
    朱宗明, 冯银波, 陶广宇, 等. 基于CT图像纹理分析方法对胸段食管癌术前T分期的研究价值[J]. 临床放射学杂志, 2019,38(3): 72−76. DOI: 10.13437/j.cnki.jcr.2019.01.016.

    ZHU Z M, FEN Y B, TAO G Y, et al. The value of CT image texture analysis for preoperative T staging of thoracic esophageal cancer[J]. Journal of Clinical Radiology, 2019, 38(3): 72−76. DOI: 10.13437/j.cnki.jcr.2019.01.016. (in Chinese).
    [18]
    杨耀华, 樊斌. 基于增强CT扫描成像的灰度共生矩阵纹理分析不同T分期食管鳞状细胞癌[J]. 临床放射学杂志, 2017,36(9): 1275−1278. DOI: 10.13437/j.cnki.jcr.2017.09.018.

    YANG Y H, FAN B. Assessment of T staging of esophageal squamous cell carcinoma by using GLCM texture analysis: Contrast-enhanced CT texture[J]. Journal of Clinical Radiology, 2017, 36(9): 1275−1278. DOI: 10.13437/j.cnki.jcr.2017.09.018. (in Chinese).
    [19]
    LIU J, WANG Z, SHAO H, et al. Improving CT detection sensitivity for nodal metastases in oesophageal cancer with combination of smaller size and lymph node axial ratio[J]. European Radiology, 2018, 28(1): 188−195. doi: 10.1007/s00330-017-4935-4
    [20]
    YUKAWA N, AOYAMA T, TAMAGAWA H, et al. The lymph node ratio is an independent prognostic factor in esophageal cancer patients who receive curative surgery[J]. In Vivo, 2020, 34(4): 2087−2093. doi: 10.21873/invivo.12012
    [21]
    TAN X, MA Z, YAN L, et al. Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma[J]. European Radiology, 2019, 9(1): 392−400.
    [22]
    SHEN C, LIU Z, WANG Z, et al. Building CT radiomics based nomogram for preoperative esophageal cancer patients lymph node metastasis prediction[J]. Translational Oncology, 2018, 11(3): 815−824. doi: 10.1016/j.tranon.2018.04.005
    [23]
    OU J, WU, L, LI R, et al. CT radiomics features to predict lymph node metastasis in advanced esophageal squamous cell carcinoma and to discriminate between regional and non-regional lymph node metastasis: A case control study[J]. Quantitative Imaging in Medicine and Surgery, 2021, 11(2): 628−640. doi: 10.21037/qims-20-241
    [24]
    WU L, YANG X, CAO W, et al. Multiple level CT radiomics features preoperatively predict lymph node metastasis in esophageal cancer: A multicentre retrospective study[J]. Frontiers in Oncology, 2019, 9: 1548.
    [25]
    程蕾舒, 吴磊, 陈舒婷, 等. 基于CT影像组学对食管鳞状细胞癌病理分化程度的预测[J]. 中南大学学报(医学版), 2019,44(3): 251−256. DOI: 10.11817/j.issn.1672-7347.2019.03.004.

    CHENG L S, WU L, CHEN S T, et al. CT-based radiomics analysis for evaluating the differentiation degree of esophageal squamous carcinoma[J]. Journal of Central South University (Medical Science), 2019, 44(3): 251−256. DOI: 10.11817/j.issn.1672-7347.2019.03.004. (in Chinese).
    [26]
    WANG Y F, YANG Y L, SUN J W, et al. Development and validation of the predictive model for esophageal squamous cell carcinoma differentiation degree[J]. Frontiers in Genetics, 2020, 11: 595638. doi: 10.3389/fgene.2020.595638
    [27]
    KAWAHARA D, MURAKAMI Y, TANI S, et al. A prediction model for degree of differentiation for resectable locally advanced esophageal squamous cell carcinoma based on CT images using radiomics andmachine-learning[J]. British Journal of Radiology, 2021, 94: 20210525. doi: 10.1259/bjr.20210525
    [28]
    RUAN R, CHEN S, TAO Y, et al. A nomogram for predicting lymphovascular invasion in superficial esophageal squamous cell carcinoma[J]. Frontiers in Oncology, 2021, 11: 663802. doi: 10.3389/fonc.2021.663802
    [29]
    LI Y, YU M, WANG G, et al. Contrast-enhanced CT-based radiomics analysis in predicting lymphovascular invasion in esophageal squamous cell carcinoma[J]. Frontiers in Oncology, 2021, 11: 644165. doi: 10.3389/fonc.2021.644165
    [30]
    LENG X F, DAIKO H, HAN Y T, et al. Optimal preoperative neoadjuvant therapy for resectable locally advanced esophageal squamous cell carcinoma[J]. Annals of the New York Academy of Sciences, 2020, 1482(1): 213−224. doi: 10.1111/nyas.14508
    [31]
    YANG Z, HE B, ZHUANG X, et al. CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy[J]. Journal Radiation Research, 2019, 60(4): 538−545. doi: 10.1093/jrr/rrz027
    [32]
    HU Y, XIE C, YANG H, et al. Assessment of intratumoral and peritumoral computed tomography radiomics for predicting pathological complete response to neoadjuvant chemoradiation in patients with esophageal squamous cell carcinoma[J]. JAMA Network Open, 2020, 3(9): e2015927. DOI: 10.1001/jamanetworkopen.2020.15927.
    [33]
    HU Y, XIE C, YANG H, et al. Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma[J]. Radiotherapy and Oncology, 2021, 154: 6−13. doi: 10.1016/j.radonc.2020.09.014
    [34]
    LI N, LUO P, LI C, et al. Analysis of related factors of radiation pneumonia caused by precise radiotherapy of esophageal cancer based on random forest algorithm[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 4477−4490. doi: 10.3934/mbe.2021227
    [35]
    DU F, LIU H, WANG W, et al. Correlation between lung density changes under different dose gradients and radiation pneumonitis−Based on an analysis of computed tomography scans during esophageal cancer radiotherapy[J]. Frontiers in Oncology, 2021, 11: 650764. doi: 10.3389/fonc.2021.650764
    [36]
    DU F, TANG N, CUI Y, et al. A novel nomogram model based on cone-beam CT radiomics analysis technology for predicting radiation pneumonitis in esophageal cancer patients undergoing radiotherapy[J]. Frontiers in Oncology, 2020, 10: 596013. doi: 10.3389/fonc.2020.596013
    [37]
    WANG L, GAO Z, LI C, et al. Computed tomography-based delta-radiomics analysis for discriminating radiation pneumonitis in patients with esophageal cancer after radiation therapy[J]. International Journal Radiation Oncology Biology Physics, 2021, 111(2): 443−455. doi: 10.1016/j.ijrobp.2021.04.047
    [38]
    HAN L, GAO Q L, ZHOU X M, et al. Characterization of CD103+CD8+tissue‑resident T cells in esophageal squamous cell carcinoma: May be tumor reactive and resurrected by anti-PD-1 lockade[J]. Cancer Immunology Immunotherapy, 2020, 69(8): 1493−1504. doi: 10.1007/s00262-020-02562-3
    [39]
    KUO H Y, GUO J C, HSU C H. Anti-PD-1 immunotherapy in advanced esophageal squamous cell carcinoma: A longawaited breakthrough finally arrives[J]. Journal Formosan Medical Association, 2020, 119(2): 565−568. doi: 10.1016/j.jfma.2019.10.010
    [40]
    WEN Q, YANG Z, ZHU J, et al. Pretreatment CT-based radiomics signature as a potential imaging biomarker for predicting the expression of PD-L1 and CD8+TILs in ESCC[J]. Onco Targets and Therapy, 2020, 13: 12003−12013. doi: 10.2147/OTT.S261068
    [41]
    GULLO R L, DAIMIEL I, MORRIS E A, et al. Combining molecular and imaging metrics in cancer: Radiogenomics[J]. Insights into Imaging, 2020, 11(1): 1. doi: 10.1186/s13244-019-0795-6
    [42]
    XIE C Y, HU Y H, HO J W, et al. Using genomics feature selection method in radiomics pipelineimproves prognostication performance in locally advanced esophageal squamous cell carcinoma−A pilot study[J]. Cancers (Basel), 2021, 13(9): 2145. doi: 10.3390/cancers13092145
    [43]
    LINDENMANN J, FEDIUK M, FINK-NEUBOECK N, et al. Hazard curves for tumor recurrence and tumor related death following esophagectomy for esophageal cancer[J]. Cancers (Basel), 2020, 12: 2066. doi: 10.3390/cancers12082066
    [44]
    QIU Q, DUAN J, DENG H, et al. Development and validation of a radiomics nomogram model for predicting postoperative recurrence in patients with esophageal squamous cell cancer who achieved pCR after neoadjuvant chemoradiotherapy followed by surgery[J]. Frontiers in Oncology, 2020, 10: 1398. doi: 10.3389/fonc.2020.01398
    [45]
    王大伟, 董婷宇, 霍志云, 等. CT增强图像纹理分析对食管癌术后早期复发转移的预测价值[J]. 实用医学杂志, 2020,36(11): 1525−1529. DOI: 10.3969/j.issn.1006-5725.2020.11.023.

    WANG D W, DONG T Y, HUO Z Y, et al. Predictive value of texture analysis based on enhanced CT images for early recurrence and metastasis of esophageal squamous cell carcinoma after surgery[J]. The Journal of Practical Medicine, 2020, 36(11): 1525−1529. DOI: 10.3969/j.issn.1006-5725.2020.11.023. (in Chinese).
    [46]
    FORNACON-WOOD I, MISTRY H, ACKERMANN C J, et al. Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform[J]. European Radiology, 2020, 30(11): 6241−6250. doi: 10.1007/s00330-020-06957-9

Catalog

    Article views (513) PDF downloads (56) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return