基于MRI和解剖学测量的宫颈癌精准T分期诊断预测研究

doi:10.13418/j.issn.1001-165x.2024.4.05

Abstract

Abstract: Objective To determine whether the measurement of three-dimensional morphological parameters based on magnetic resonance imaging (MRI) can accurately predict the T-stage diagnosis of cervical cancer (CC) tumors or not after three-dimensional reconstruction. Methods Preoperative MRI images from 108 patients with pathologically confirmed cervical cancer were retrospectively collected and divided into four groups: T1, T2, T3 and T4. T1 and T2 were further divided into four subgroups: T1a, T1b, T2a and T2b. The Amira2019 software was used to segment and 3D reconstruct the tumor, uterus, vagina, bladder, urethra, and rectum. The surface area, volume, longitudinal diameter, anterior posterior diameter, transverse diameter, longest diameter, roughness, texture uniformity and degree of vaginal invasion of tumor were measured. The statistical differences were evaluated by using Kruskal-Wallis test, [χ2]-test, receiver operating characteristic (ROC) curve, etc., and the cut-off values for different T-stages were calculated based on Youden's index. Results There were statistical differences in the surface area, volume, longitudinal diameter, anterior posterior diameter, transverse diameter, longest diameter and degree of vaginal invasion of tumor between T1-T4 and T1a-T2b (P<0.05), while there was no statistical difference in roughness and texture uniformity between T1-T4 and T1a-T2b (P<0.001). Among them, the longitudinal average diameters of T1-T4 tumors were 2.82 cm, 3.78 cm, 4.82 cm and 6.61 cm (P<0.001), respectively, with cut-off values of 3.17 cm , 4.24 cm and 6.57 cm (AUC=0.699, 0.73, 0.708). The longitudinal average diameters of T1a-T2b tumors were 2.31 cm, 2.84 cm, 3.63 cm and 4.09 cm (P=0.008), respectively, with cut-off values of 2.32 cm, 3.12 cm and 3.94 cm (AUC=0.597, 0.689, 0.561). Conclusions The morphological parameters of cervical cancer tumors on MRI include surface area, volume, longitudinal diameter, anterior posterior diameter, transverse diameter, longest diameter, and degree of vaginal invasion of tumor, which are valuable diagnostic factors for predicting T-stage in cervical cancer patients. This study contributes to the accurate diagnosis, prognostic evaluation, and treatment decision-making of cervical cancer.

Key words: Cervical cancer; , , Magnetic resonance imaging (MRI); , , T-stage diagnosis; , , Three-dimensional reconstruction

CLC Number:

Zou Yuxin, Wang Ruiwei, Wu Zhe, Hou Wenjing, Xu Shanshan, Fan Rutao, Wang Yanzhou, Wu Yi. Study on the accurate T-stage diagnosis and prediction of cervical cancer based on MRI and anatomical measurement[J]. Chinese Journal of Clinical Anatomy, 2024, 42(4): 382-392.

References

[1] 金鸽,李魁秀,郭云峰,等.宫颈癌近距离放疗的研究进展[J].国际妇产科学杂志, 2022, 49(2):202-206. DOI:10.12280/gjfckx.20210574.
[2] Buskwofie A, David-West G, Clare CA. A Review of Cervical Cancer: Incidence and Disparities[J]. J Natl Med Assoc, 2020, 112(2):229-232. DOI:10.1016/j.jnma.2020.03.002.
[3] 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.
[4] Zou Z, Fairley CK, Ong JJ, et al. Domestic HPV vaccine price and economic returns for cervical cancer prevention in China: a cost-effectiveness analysis[J]. Lancet Glob Health, 2020, 8(10):e1335-e1344. DOI: 10.1016/S2214-109X(20)30277-1.
[5] Singh D, Vignat J, Lorenzoni V, et al. Global estimates of incidence and mortality of cervical cancer in 2020: a baseline analysis of the WHO Global Cervical Cancer Elimination Initiative[J]. Lancet Glob Health, 2023, 11(2):e197-e206. DOI: 10.1016/S2214-109X(22)00501-0.
[6] Ramli Z, Karim MKA, Effendy N, et al. Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI[J]. Diagnostics (Basel), 2022, 12(12):3125. DOI: 10.3390/diagnostics12123125.
[7] Devine C, Viswanathan C, Faria S, et al. Imaging and Staging of Cervical Cancer[J]. Semin Ultrasound CT MR, 2019, 40(4):280-286. DOI:10.1053/j.sult.2019.03.001.
[8] Song J, Hu Q, Huang J, et al. Combining tumor size and diffusion-weighted imaging to diagnose normal-sized metastatic pelvic lymph nodes in cervical cancers[J]. Acta Radiol, 2019, 60(3):388-395. DOI: 10.1177/0284185118780903.
[9] 王新婧, 施磊, 李兵, 等. DWI联合MRI动态增强扫描在诊断宫颈癌分期中的应用[J].中国中西医结合影像学杂志, 2024, 22(2):207-211. DOI:10.3969/j.issn.1672-0512.2024.02.017.
[10]李淑健, 程敬亮, 张勇, 等. T2 mapping成像评估宫颈癌病理学特征[J].中国医学影像技术, 2019, 35(9):1365-1369. DOI:10.13929/j.1003-3289.201903113.
[11]Cai H, Wang R, Li Y, et al. Role of 3D reconstruction in the evaluation of patients with lower segment oesophageal cancer[J]. J Thorac Dis,2018, 10(7):3940-3947. DOI: 10.21037/jtd.2018.06.119.
[12]Robb H, Scrimgeour G, Boshier P, et al. The current and possible future role of 3D modelling within oesophagogastric surgery: a scoping review[J]. Surg Endosc, 2022, 36(8):5907-5920. DOI: 10.1007/s00464-022-09176-z.
[13]Wu QY, Liu SL, Sun P, et al. Establishment and clinical application value of an automatic diagnosis platform for rectal cancer T-staging based on a deep neural network[J]. Chin Med J (Engl), 2021, 134(7):821-828. DOI: 10.1097/CM9.0000000000001401.
[14]Yang Q, Guo Y, Ou X, et al. Automatic T staging using weakly supervised deep learning for nasopharyngeal carcinoma on MR images[J]. J Magn Reson Imaging, 2020, 52(4):1074- 1082. DOI:10.1002/jmri.27202.
[15]Zhong Y, She Y, Deng J, et al. Deep learning for prediction of N2 metastasis and survival for clinical stage I non-small cell lung cancer[J]. Radiology, 2021,302(1):200-211.DOI: 10.1148/radiol. 2021210902.
[16]Wang W, Zhou Y, Wang D, et al. Prophylactic Extended-Field Irradiation in Patients With Cervical Cancer: A Literature Review[J]. Front Oncol, 2020, 10:579410. DOI: 10.3389/fonc.2020.579410.
[17]Bianchi L, Schiavina R, Bortolani B, et al. Novel Volumetric and Morphological Parameters Derived from Three-dimensional Virtual Modeling to Improve Comprehension of Tumor's Anatomy in Patients with Renal Cancer[J]. Eur Urol Focus, 2022, 8(5):1300-1308. DOI: 10.1016/j.euf.2021.08.002.
[18]Shen S, Zhang S, Liu P, et al. Potential role of microRNAs in the treatment and diagnosis of cervical cancer[J]. Cancer Genet, 2020, 248-249:25-30. DOI:10.1016/j.cancergen.2020.09.003.
[19]Li J, Gu C, Zheng H, et al. Ultrasonographic diagnosis in rare primary cervical cancer[J]. Int J Gynecol Cancer, 2021, 31(12):1535-1540. DOI: 10.1136/ijgc-2021-002860.
[20]Tu S, Zhang H, Yang X, et al. Screening of cervical cancer-related hub genes based on comprehensive bioinformatics analysis[J]. Cancer Biomark, 2021, 32(3):303-315. DOI:10.3233/CBM-203262.
[21]Xu L, Cai F, Fu Y, et al. Cervical cell classification with deep-learning algorithms[J]. Med Biol Eng Comput, 2023,61(3):821-833. DOI: 10.1007/s11517-022-02745-3.
[22]程贤文, 邹晓凤, 赖伟建. MRI常规序列及DWI序列用于宫颈癌分期诊断中的应用[J].医学理论与实践, 2024, 37(8):1369-1371. DOI:10.19381/j.issn.1001-7585.2024.08.037.
[23]Zhang J, Tian W, Bu X, et al. Diagnostic significance of magnetic resonance imaging in patients with cervical cancer after brachytherapy: a meta-analysis[J]. Acta Radiol, 2019, 60(5):670-676. DOI: 10.1177/0284185118791199.

Study on the accurate T-stage diagnosis and prediction of cervical cancer based on MRI and anatomical measurement

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