基于CT影像组学构建下咽癌及头颈肿瘤预测模型的研究进展

王寅, 雷大鹏. 基于CT影像组学构建下咽癌及头颈肿瘤预测模型的研究进展[J]. 临床耳鼻咽喉头颈外科杂志, 2022, 36(2): 158-162. doi: 10.13201/j.issn.2096-7993.2022.02.018
引用本文: 王寅, 雷大鹏. 基于CT影像组学构建下咽癌及头颈肿瘤预测模型的研究进展[J]. 临床耳鼻咽喉头颈外科杂志, 2022, 36(2): 158-162. doi: 10.13201/j.issn.2096-7993.2022.02.018
WANG Yin, LEI Dapeng. Research progress in CT-based radiomics constructing hypopharyngeal cancer and multisystem tumor prediction model[J]. J Clin Otorhinolaryngol Head Neck Surg, 2022, 36(2): 158-162. doi: 10.13201/j.issn.2096-7993.2022.02.018
Citation: WANG Yin, LEI Dapeng. Research progress in CT-based radiomics constructing hypopharyngeal cancer and multisystem tumor prediction model[J]. J Clin Otorhinolaryngol Head Neck Surg, 2022, 36(2): 158-162. doi: 10.13201/j.issn.2096-7993.2022.02.018

基于CT影像组学构建下咽癌及头颈肿瘤预测模型的研究进展

  • 基金项目:
    国家自然科学基金项目(No:82071918)
详细信息

Research progress in CT-based radiomics constructing hypopharyngeal cancer and multisystem tumor prediction model

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  • [1]

    Lambin P, Leijenaar R, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762. doi: 10.1038/nrclinonc.2017.141

    [2]

    Haider SP, Burtness B, Yarbrough WG, et al. Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas[J]. Cancers Head Neck, 2020, 5: 6. doi: 10.1186/s41199-020-00053-7

    [3]

    Petersen JF, Timmermans AJ, van Dijk B, et al. Trends in treatment, incidence and survival of hypopharynx cancer: a 20-year population-based study in the Netherlands[J]. Eur Arch Otorhinolaryngol, 2018, 275(1): 181-189. doi: 10.1007/s00405-017-4766-6

    [4]

    Kılıç S, Kılıç SS, Hsueh WD, et al. Radiotherapy modality as a predictor of survival in hypopharyngeal cancer[J]. Head Neck, 2018, 40(11): 2441-2448. doi: 10.1002/hed.25360

    [5]

    Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. doi: 10.3322/caac.21660

    [6]

    Chow L. Head and Neck Cancer[J]. N Engl J Med, 2020, 382(1): 60-72. doi: 10.1056/NEJMra1715715

    [7]

    Wen Q, Yang Z, Dai H, et al. Radiomics Study for Predicting the Expression of PD-L1 and Tumor Mutation Burden in Non-Small Cell Lung Cancer Based on CT Images and Clinicopathological Features[J]. Front Oncol, 2021, 11: 620246. doi: 10.3389/fonc.2021.620246

    [8]

    Yang B, Zhou L, Zhong J, et al. Combination of computed tomography imaging-based radiomics and clinicopathological characteristics for predicting the clinical benefits of immune checkpoint inhibitors in lung cancer[J]. Respir Res, 2021, 22(1): 189. doi: 10.1186/s12931-021-01780-2

    [9]

    Weng Q, Hui J, Wang H, et al. Radiomic Feature-Based Nomogram: A Novel Technique to Predict EGFR-Activating Mutations for EGFR Tyrosin Kinase Inhibitor Therapy[J]. Front Oncol, 2021, 11: 590937. doi: 10.3389/fonc.2021.590937

    [10]

    Zhang T, Xu Z, Liu G, et al. Simultaneous Identification of EGFR, KRAS, ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics[J]. Cancers(Basel), 2021, 13(8).

    [11]

    Chen Y, Wei K, Liu D, et al. A Machine Learning Model for Predicting a Major Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer[J]. Front Oncol, 2021, 11: 675458. doi: 10.3389/fonc.2021.675458

    [12]

    Li Y, Cheng Z, Gevaert O, et al. A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer[J]. Chin J Cancer Res, 2020, 32(1): 62-71. doi: 10.21147/j.issn.1000-9604.2020.01.08

    [13]

    Sellami S, Bourbonne V, Hatt M, et al. Predicting response to radiotherapy of head and neck squamous cell carcinoma using radiomics from cone-beam CT images[J]. Acta Oncol, 2021: 1-8.

    [14]

    Wang CY, Ginat DT. Preliminary Computed Tomography Radiomics Model for Predicting Pretreatment CD8+ T-Cell Infiltration Status for Primary Head and Neck Squamous Cell Carcinoma[J]. J Comput Assist Tomogr, 2021, 45(4): 629-636. doi: 10.1097/RCT.0000000000001149

    [15]

    Cohen N, Fedewa S, Chen AY. Epidemiology and Demographics of the Head and Neck Cancer Population[J]. Oral Maxillofac Surg Clin North Am, 2018, 30(4): 381-395. doi: 10.1016/j.coms.2018.06.001

    [16]

    吴钟凯, 刘业海, 张亮, 等. 术前颈胸增强CT和CTA在颈根部不同肿瘤手术方式选择中的意义[J]. 临床耳鼻咽喉头颈外科杂志, 2021, 35(1): 24-28. https://www.cnki.com.cn/Article/CJFDTOTAL-LCEH202101006.htm

    [17]

    Liu X, Maleki F, Muthukrishnan N, et al. Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models[J]. Cancers(Basel), 2021, 13(15).

    [18]

    董研博, 张奥博, 张金刚, 等. 基于影像组学的头颈部鳞状细胞癌研究进展[J]. 临床耳鼻咽喉头颈外科杂志, 2021, 35(2): 181-184. https://www.cnki.com.cn/Article/CJFDTOTAL-LCEH202102025.htm

    [19]

    Torre LA, Trabert B, DeSantis CE, et al. Ovarian cancer statistics, 2018[J]. CA Cancer J Clin, 2018, 68(4): 284-296. doi: 10.3322/caac.21456

    [20]

    Lydiatt WM, Patel SG, O'Sullivan B, et al. Head and Neck cancers-major changes in the American Joint Committee on cancer eighth edition cancer staging manual[J]. CA Cancer J Clin, 2017, 67(2): 122-137. doi: 10.3322/caac.21389

    [21]

    Gu J, Zhu J, Qiu Q, et al. Prediction of Immunohistochemistry of Suspected Thyroid Nodules by Use of Machine Learning-Based Radiomics[J]. AJR Am J Roentgenol, 2019, 213(6): 1348-1357. doi: 10.2214/AJR.19.21626

    [22]

    Lu W, Zhong L, Dong D, et al. Radiomic analysis for preoperative prediction of cervical lymph node metastasis in patients with papillary thyroid carcinoma[J]. Eur J Radiol, 2019, 118: 231-238. doi: 10.1016/j.ejrad.2019.07.018

    [23]

    Zhou Y, Su GY, Hu H, et al. Radiomics analysis of dual-energy CT-derived iodine maps for diagnosing metastatic cervical lymph nodes in patients with papillary thyroid cancer[J]. Eur Radiol, 2020, 30(11): 6251-6262. doi: 10.1007/s00330-020-06866-x

    [24]

    Chen YP, Chan A, Le QT, et al. Nasopharyngeal carcinoma[J]. Lancet, 2019, 394(10192): 64-80. doi: 10.1016/S0140-6736(19)30956-0

    [25]

    Liang ZG, Tan HQ, Zhang F, et al. Comparison of radiomics tools for image analyses and clinical prediction in nasopharyngeal carcinoma[J]. Br J Radiol, 2019, 92(1102): 20190271. doi: 10.1259/bjr.20190271

    [26]

    Zhu C, Huang H, Liu X, et al. A Clinical-Radiomics Nomogram Based on Computed Tomography for Predicting Risk of Local Recurrence After Radiotherapy in Nasopharyngeal Carcinoma[J]. Front Oncol, 2021, 11: 637687. doi: 10.3389/fonc.2021.637687

    [27]

    Blanchard P, Lee A, Marguet S, et al. Chemotherapy and radiotherapy in nasopharyngeal carcinoma: an update of the MAC-NPC meta-analysis[J]. Lancet Oncol, 2015, 16(6): 645-655. doi: 10.1016/S1470-2045(15)70126-9

    [28]

    Peng H, Dong D, Fang MJ, et al. Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma[J]. Clin Cancer Res, 2019, 25(14): 4271-4279. doi: 10.1158/1078-0432.CCR-18-3065

    [29]

    Yang Y, Wang M, Qiu K, et al. Computed tomography-based deep-learning prediction of induction chemotherapy treatment response in locally advanced nasopharyngeal carcinoma[J]. Strahlenther Onkol, 2021.

    [30]

    Yan C, Shen DS, Chen XB, et al. CT-Based Radiomics Nomogram for Prediction of Progression-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma[J]. Cancer Manag Res, 2021, 13: 6911-6923. doi: 10.2147/CMAR.S325373

    [31]

    Xu H, Lv W, Feng H, et al. Subregional Radiomics Analysis of PET/CT Imaging with Intratumor Partitioning: Application to Prognosis for Nasopharyngeal Carcinoma[J]. Mol Imaging Biol, 2020, 22(5): 1414-1426. doi: 10.1007/s11307-019-01439-x

    [32]

    Peng L, Hong X, Yuan Q, et al. Prediction of local recurrence and distant metastasis using radiomics analysis of pretreatment nasopharyngeal[18F]FDG PET/CT images[J]. Ann Nucl Med, 2021, 35(4): 458-468. doi: 10.1007/s12149-021-01585-9

    [33]

    Du D, Feng H, Lv W, et al. Machine Learning Methodsfor Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images[J]. Mol Imaging Biol, 2020, 22(3): 730-738. doi: 10.1007/s11307-019-01411-9

    [34]

    Moro J, Maroneze MC, Ardenghi TM, et al. Oral and oropharyngeal cancer: epidemiology and survival analysis[J]. Einstein(Sao Paulo), 2018, 16(2): eAO4248.

    [35]

    Kędzierawski P, Huruk-Kuchinka A, Radowicz-Chil A, et al. Human papillomavirus infection predicts a better survival rate in patients with oropharyngeal cancer[J]. Arch Med Sci, 2021, 17(5): 1308-1316. doi: 10.5114/aoms.2019.83658

    [36]

    Bagher-Ebadian H, Lu M, Siddiqui F, et al. Application of radiomics for the prediction of HPV status for patients with head and neck cancers[J]. Med Phys, 2020, 47(2): 563-575. doi: 10.1002/mp.13977

    [37]

    Reiazi R, Arrowsmith C, Welch M, et al. Prediction of Human Papillomavirus(HPV)Association of Oropharyngeal Cancer(OPC)Using Radiomics: The Impact of the Variation of CT Scanner[J]. Cancers(Basel), 2021, 13(9).

    [38]

    Rich B, Huang J, Yang Y, et al. Radiomics Predicts for Distant Metastasis in Locally Advanced Human Papillomavirus-Positive Oropharyngeal Squamous Cell Carcinoma[J]. Cancers(Basel), 2021, 13(22).

    [39]

    Song B, Yang K, Garneau J, et al. Radiomic Features Associated With HPV Status on Pretreatment Computed Tomography in Oropharyngeal Squamous Cell Carcinoma Inform Clinical Prognosis[J]. Front Oncol, 2021, 11: 744250. doi: 10.3389/fonc.2021.744250

    [40]

    Choi Y, Nam Y, Jang J, et al. Prediction of Human Papillomavirus Status and Overall Survival in Patients with Untreated Oropharyngeal Squamous Cell Carcinoma: Development and Validation of CT-Based Radiomics[J]. AJNR Am J Neuroradiol, 2020, 41(10): 1897-1904. doi: 10.3174/ajnr.A6756

    [41]

    Li W, Wei D, Wushouer A, et al. Discovery and Validation of a CT-Based Radiomic Signature for Preoperative Prediction of Early Recurrence in Hypopharyngeal Carcinoma[J]. Biomed Res Int, 2020, 2020: 4340521.

    [42]

    Guo R, Guo J, Zhang L, et al. CT-based radiomics features in the prediction of thyroid cartilage invasion from laryngeal and hypopharyngeal squamous cell carcinoma[J]. Cancer Imaging, 2020, 20(1): 81. doi: 10.1186/s40644-020-00359-2

    [43]

    Mo X, Wu X, Dong D, et al. Prognostic value of the radiomics-based model in progression-free survival of hypopharyngeal cancer treated with chemoradiation[J]. Eur Radiol, 2020, 30(2): 833-843. doi: 10.1007/s00330-019-06452-w

    [44]

    Bahig H, Lapointe A, Bedwani S, et al. Dual-energy computed tomography for prediction of loco-regional recurrence after radiotherapy in larynx and hypopharynx squamous cell carcinoma[J]. Eur J Radiol, 2019, 110: 1-6. doi: 10.1016/j.ejrad.2018.11.005

    [45]

    Chen RY, Lin YC, Shen WC, et al. Associations of Tumor PD-1 Ligands, Immunohistochemical Studies, and Textural Features in 18F-FDG PET in Squamous Cell Carcinoma of the Head and Neck[J]. Sci Rep, 2018, 8(1): 105. doi: 10.1038/s41598-017-18489-2

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出版历程
收稿日期:  2021-10-28
刊出日期:  2022-02-03

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