影像组学在宫颈癌中的研究进展

doi:10.3760/cma.j.cn371439-20240304-00089

摘要/Abstract

摘要：

宫颈癌是女性生殖系统最常见的恶性肿瘤，早期诊断和准确的疗效预测对于治疗方案的制定具有重要的临床价值。传统的影像学在宫颈癌的诊断及疗效评估中发挥了不可替代的作用，但有一定主观性，而宫颈活检为有创检查，且仅能评价局部肿瘤组织的病理组织学特征。因此亟需一种能在治疗前和治疗中准确预测肿瘤特征的无创且可连续检测的生物标志物。影像组学通过高通量提取CT、MRI及PET-CT等影像数据，深度挖掘图像中的数据信息，对疾病进行诊断、疗效评估及预后预测。深入探讨影像组学在宫颈癌中的最新应用及临床研究进展，可为宫颈癌的治疗提供决策参考。

关键词: 宫颈肿瘤, 影像组学, 疗效预测

Abstract:

Cervical cancer is the most common malignant tumor of the female reproductive system，early diagnosis and accurate efficacy prediction have important clinical value for the development of treatment plans. Traditional imaging inspection plays an irreplaceable role in the diagnosis and efficacy assessment of cervical cancer，however，it is somewhat subjective，while cervical biopsy is an invasive examination and can only evaluate the pathological histological characteristics of local tumor tissue. Therefore，there is a strong demand to develop a non-invasive，continuously detectable biomarker that can accurately predict tumor characteristics before and during treatment. Radiomics is used to make disease diagnosis，efficacy evaluation and prognosis prediction by means of high-throughput extraction of CT，MRI and PET-CT image data as well as deep excavation of the data information in the images. The aim of this discussion is to explore the latest application and clinical research progress of imaging omics for cervical cancer，which can provide reference for decision-making in the treatment of cervical cancer.

Key words: Uterine cervical neoplasms, Radiomics, Efficacy prediction

彭丹, 吕璐, 孙鹏飞. 影像组学在宫颈癌中的研究进展[J]. 国际肿瘤学杂志, 2024, 51(8): 532-537.

Peng Dan, Lyu Lu, Sun Pengfei. Research progress of radiomics in cervical cancer[J]. Journal of International Oncology, 2024, 51(8): 532-537.

参考文献 41

[1]	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.
[2]	Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036. pmid: 22257792
[3]	Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169. pmid: 26579733
[4]	Galic V, Herzog TJ, Lewin SN, et al. Prognostic significance of adenocarcinoma histology in women with cervical cancer[J]. Gynecol Oncol, 2012, 125(2): 287-291. DOI: 10.1016/j.ygyno.2012.01.012. pmid: 22266551
[5]	Fujiwara K, Monk B, Devouassoux-Shisheboran M. Adenocarcinoma of the uterine cervix: why is it different?[J]. Curr Oncol Rep, 2014, 16(12): 416. DOI: 10.1007/s11912-014-0416-y.
[6]	Wang W, Jiao Y, Zhang L, et al. Multiparametric MRI-based radiomics analysis: differentiation of subtypes of cervical cancer in the early stage[J]. Acta Radiol, 2022, 63(6): 847-856. DOI: 10.1177/02841851211014188.
[7]	Shen WC, Chen SW, Liang JA, et al. [18]fluorodeoxyglucose positron emission tomography for the textural features of cervical cancer associated with lymph node metastasis and histological type[J]. Eur J Nucl Med Mol Imaging, 2017, 44(10): 1721-1731. DOI: 10.1007/s00259-017-3697-1.
[8]	Matsuo K, Mandelbaum RS, Machida H, et al. Association of tumor differentiation grade and survival of women with squamous cell carcinoma of the uterine cervix[J]. J Gynecol Oncol, 2018, 29(6): e91. DOI: 10.3802/jgo.2018.29.e91.
[9]	Liu Y, Zhang YW, Cheng RF, et al. Radiomics analysis of apparent diffusion coefficient in cervical cancer: a preliminary study on histological grade evaluation[J]. J Magn Reson Imaging, 2019, 49(1): 280-290. DOI: 10.1002/jmri.26192. pmid: 29761595
[10]	Wang S, Jiang T, Hu X, et al. Can the combination of DWI and T2WI radiomics improve the diagnostic efficiency of cervical squamous cell carcinoma?[J]. Magn Reson Imaging, 2022, 92: 197-202. DOI: 10.1016/j.mri.2022.07.005. pmid: 35842193
[11]	Mu W, Chen Z, Liang Y, et al. Staging of cervical cancer based on tumor heterogeneity characterized by texture features on (18)F-FDG PET images[J]. Phys Med Biol, 2015, 60(13): 5123-5139. DOI: 10.1088/0031-9155/60/13/5123. pmid: 26083460
[12]	Wang M, Perucho JAU, Tse KY, et al. MRI texture features differen-tiate clinicopathological characteristics of cervical carcinoma[J]. Eur Radiol, 2020, 30(10): 5384-5391. DOI: 10.1007/s00330-020-06913-7.
[13]	Wang M, Perucho JAU, Vardhanabhuti V, et al. Radiomic features of T2-weighted imaging and diffusion kurtosis imaging in differentia-ting clinicopathological characteristics of cervical carcinoma[J]. Acad Radiol, 2022, 29(8): 1133-1140. DOI: 10.1016/j.acra.2021.08.018.
[14]	Du W, Wang Y, Li DD, et al. Preoperative prediction of lymphovascular space invasion in cervical cancer with radiomics-based nomogram[J]. Front Oncol, 2021, 11: 637794. DOI: 10.3389/fonc.2021.637794.
[15]	Huang G, Cui YQ, Wang P, et al. Multi-parametric magnetic resonance imaging-based radiomics analysis of cervical cancer for preoperative prediction of lymphovascular space invasion[J]. Front Oncol, 2021, 11: 663370. DOI: 10.3389/fonc.2021.663370.
[16]	Wu Y, Wang S, Chen Y, et al. A multicenter study on preoperative assessment of lymphovascular space invasion in early‐stage cervical cancer based on multimodal MR radiomics[J]. J Magn Reson Imaging, 2023, 58(5): 1638-1648. DOI: 10.1002/jmri.28676.Epub 2023 Mar 16.
[17]	Li X, Xu C, Yu Y, et al. Prediction of lymphovascular space invasion using a combination of tenascin-C, cox-2, and PET/CT radiomics in patients with early-stage cervical squamous cell carcinoma[J]. BMC Cancer, 2021, 21(1): 866. DOI: 10.1186/s12885-021-08596-9.
[18]	Cui L, Yu T, Kan Y, et al. Multi-parametric MRI-based peritumoral radiomics on prediction of lymph-vascular space invasion in early-stage cervical cancer[J]. Diagn Interv Radiol, 2022, 28(4): 312-321. DOI: 10.5152/dir.2022.20657.
[19]	Hua W, Xiao T, Jiang X, et al. Lymph-vascular space invasion prediction in cervical cancer: exploring radiomics and deep learning multilevel features of tumor and peritumor tissue on multiparametric MRI[J]. Biomed Signal Process Control, 2020, 58: 101869. DOI: 10.1016/j.bspc.2020.101869.
[20]	袁晨阳, 周菊英, 杜霄, 等. 宫颈癌2018与2009 FIGO分期的比较及预后因素分析[J]. 国际肿瘤学杂志, 2022, 49(3): 151-163. DOI: 10.3760/cma.j.cn371439-20211015-00026.
[21]	Liu Y, Fan H, Dong D, et al. Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer[J]. Transl Oncol, 2021, 14(8): 101113. DOI: 10.1016/j.tranon.2021.101113.
[22]	Song J, Hu Q, Ma Z, et al. Feasibility of T2WI-MRI-based radiomics nomogram for predicting normal-sized pelvic lymph node metastasis in cervical cancer patients[J]. Eur Radiol, 2021, 31(9): 6938-6948. DOI: 10.1007/s00330-021-07735-x.
[23]	Wu Q, Wang S, Chen X, et al. Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer[J]. Radiother Oncol, 2019, 138: 141-148. DOI: 10.1016/j.radonc.2019.04.035. pmid: 31252296
[24]	Shi J, Dong Y, Jiang W, et al. MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: a multi-center study[J]. Magn Reson Imaging, 2022, 88: 1-8. DOI: 10.1016/j.mri.2021.12.008.
[25]	Song M, Lin J, Song FZ, et al. The value of MR-based radiomics in identifying residual disease in patients with carcinoma in situ after cervical conization[J]. Sci Rep, 2020, 10(1): 19890. DOI: 10.1038/s41598-020-76853-1.
[26]	Fang J, Zhang B, Wang S, et al. Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer[J]. Theranostics, 2020, 10(5): 2284-2292. DOI: 10.7150/thno.37429. pmid: 32089742
[27]	Zhou Y, Gu H, Zhang X, et al. Multiparametric magnetic resonance imaging-derived radiomics for the prediction of disease-free survival in early-stage squamous cervical cancer[J]. Eur Radiol, 2022, 32(4): 2540-2551. DOI: 10.1007/s00330-021-08326-6.
[28]	Liu S, Li R, Liu Q, et al. Radiomics model of ¹⁸F-FDG PET/CT imaging for predicting disease-free survival of early-stage uterine cervical squamous cancer[J]. Cancer Biomark, 2022, 33(2): 249-259. DOI: 10.3233/CBM-210201.
[29]	Lucia F, Visvikis D, Desseroit MC, et al. Prediction of outcome using pretreatment ¹⁸F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy[J]. Eur J Nucl Med Mol Imaging, 2018, 45(5): 768-786. DOI: 10.1007/s00259-017-3898-7.
[30]	Lucia F, Visvikis D, Vallières M, et al. External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy[J]. Eur J Nucl Med Mol Imaging, 2019, 46(4): 864-877. DOI: 10.1007/s00259-018-4231-9.
[31]	Mu W, Liang Y, Hall LO, et al. ¹⁸F-FDG PET/CT habitat radiomics predicts outcome of patients with cervical cancer treated with chemoradiotherapy[J]. Radiol Artif Intell, 2020, 2(6): e190218. DOI: 10.1148/ryai.2020190218.
[32]	Ren K, Shen L, Qiu J, et al. Treatment planning computed tomography radiomics for predicting treatment outcomes and haematological toxicities in locally advanced cervical cancer treated with radiotherapy: a retrospective cohort study[J]. BJOG, 2023, 130(2): 222-230. DOI: 10.1111/1471-0528.17285.
[33]	Li H, Zhu M, Jian L, et al. Radiomic score as a potential imaging biomarker for predicting survival in patients with cervical cancer[J]. Front Oncol, 2021, 11: 706043. DOI: 10.3389/fonc.2021.706043.
[34]	Zhang X, Zhang Q, Guo J, et al. Added-value of texture analysis of ADC in predicting the survival of patients with 2018 FIGO stage ⅢCr cervical cancer treated by concurrent chemoradiotherapy[J]. Eur J Radiol, 2022, 150: 110272. DOI: 10.1016/j.ejrad.2022.110272.
[35]	Liu B, Sun Z, Xu Z, et al. Predicting disease-free survival with multiparametric MRI-derived radiomic signature in cervical cancer patients underwent CCRT[J]. Front Oncol, 2021, 11: 812993. DOI: 10.3389/fonc.2021.812993.
[36]	Park SH, Hahm MH, Bae BK, et al. Magnetic resonance imaging features of tumor and lymph node to predict clinical outcome in node-positive cervical cancer: a retrospective analysis[J]. Radiat Oncol, 2020, 15(1): 86. DOI: 10.1186/s13014-020-01502-w.
[37]	Kim KE, Kim CK. Magnetic resonance imaging-based texture analysis for the prediction of postoperative clinical outcome in uterine cervical cancer[J]. Abdom Radiol (NY), 2022, 47(1): 352-361. DOI: 10.1007/s00261-021-03288-1.
[38]	Wormald BW, Doran SJ, Ind TE, et al. Radiomic features of cervical cancer on T2-and diffusion-weighted MRI: prognostic value in low-volume tumors suitable for trachelectomy[J]. Gynecol Oncol, 2020, 156(1): 107-114. DOI: 10.1016/j.ygyno.2019.10.010. pmid: 31685232
[39]	Yusufaly TI, Zou J, Nelson TJ, et al. Improved prognosis of treatment failure in cervical cancer with nontumor PET/CT radiomics[J]. J Nucl Med, 2022, 63(7): 1087-1093. DOI: 10.2967/jnumed.121.262618.
[40]	Zhang X, Zhang Q, Xie L, et al. The value of whole-tumor texture analysis of ADC in predicting the early recurrence of locally advanced cervical squamous cell cancer treated with concurrent chemoradiotherapy[J]. Front Oncol, 2022, 12: 852308. DOI: 10.3389/fonc.2022.852308.
[41]	Zhang Y, Liu L, Zhang K, et al. Nomograms combining clinical and imaging parameters to predict recurrence and disease-free survival after concurrent chemoradiotherapy in patients with locally advanced cervical cancer[J]. Acad Radiol, 2023, 30(3): 499-508. DOI: 10.1016/j.acra.2022.08.002.