Abstract：Objective To analyze the prognostic value of nomogram model for cervical cancer based on the imaging features of diffusion kurtosis imaging (DKI) histogram. Methods The DKI and clinical data of 272 patients with cervical cancer who were admitted to Affiliated Hospital of Guangdong Medical University from March 2015 to February 2022 were collected and retrospectively analyzed. All patients were randomly divided into the training group (n=190) and validation group (n=82) at a ratio of 7 vs. 3. The parameters of DKI histogram were obtained by GE AW 4.2 MRI software. The best prognostic imaging features were screened by LASSO regression. The DKI radiomics score was calculated by linear combination. The independent risk factors of prognosis were identified by univariate and multivariate regression analyses, and a nomogram model was constructed. The model discrimination was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). The internal consistency of the model was evaluated by the calibration map. Results Adenocarcinoma (HR=2.496, 95%CI=1.312-4.749, P=0.005), DKI score (HR=24.087, 95%CI=6.062-95.711, P<0.001), depth of invasion ≥ 1/2 muscular layer (HR=2.277, 95%CI=1.156-4.487, P=0.017) and neutrophil to lymphocyte ratio (NLR) (HR=1.800, 95%CI=1.313-2.468, P<0.001) were the independent risk factors for prognosis of cervical cancer. The AUC of the nomogram model in the training and validation groups were 0.860 and 0.757, respectively. The calibration curve was well fitted with the 45° diagonal. The prediction results of long-term prognosis of this model were in good agreement with the actual situation. Conclusions Adenocarcinoma, NLR, DKI score and depth of invasion ≥ 1/2 muscular layer are the independent risk factors for the prognosis of patients with cervical cancer. The constructed nomogram model could reliably predict the 3-year survival rate of patients with cervical cancer.
He Bin,Chen Wubiao,Wu Yongjun et al. Prognostic value of diffusion kurtosis imaging histogram based nomogram model for cervical cancer[J]. Chinese Journal of Radiation Oncology, 2023, 32(7): 606-611.
[1] Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics2020: 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] Female Genital Tumours.WHO Classification of Tumors[M]. 5th ed. Volume 4. Geneva, Switzerland: WHO Press, 2020. [3] Ferlay J, Colombet M, Soerjomataram I, et al. Cancer statistics for the year2020: an overview[J]. Int J Cancer, 2021,149:778-789.DOI: 10.1002/ijc.33588. [4] 崔芳芳, 鲍俊哲, 王琳琳, 等. 1990-2019年中国女性"两癌"疾病负担变化趋势及预测分析[J].中国卫生统计,2022,39(5):647-652. DOI: 10.3969/j.issn.1002-3674.2022. 05.002. Cui FF, Bao JZ, Wang LL, et al. Analysis on the trends and projections of disease burden of breast cancer and cervical cancer among women in China, from1990 to 2019[J]. Chinese Journal of Health Statistics,2022,39(5):647-652. DOI: 10.3969/j.issn.1002-3674.2022.05.002. [5] Mauricio D, Zeybek B, Tymon-Rosario J, et al. Immunotherapy in cervical cancer[J]. Curr Oncol Rep, 2021,23(6):61. [6] 孙轶群, 刘宗霖, 付彩霞, 等. DKI直方图预测局部进展期直肠癌患者预后的临床价值[J].肿瘤影像学,2022,31(2):105-112. DOI: 10.19732/j.cnki.2096-6210.2022.02.002. Sun YQ, Liu ZL, Fu CX, et al. The clinical value of DKI histogram parameter metrics predicting survival in patients with locally advanced rectal cancer[J]. Oncoradiology,2022,31(2):105-112. DOI: 10.19732/j.cnki.2096-6210.2022.02.002. [7] 闫坤, 胡莎莎, 杨品, 等. 扩散峰度成像与宫颈癌病理学特征相关性的初步探索[J].磁共振成像,2016,7(9):683-688. DOI: 10.12015/issn.1674-8034.2016.09.009. Yan K, Hu SS, Yang P, et al. The relationship between DKI and pathological features of cervical cancer:a primary study[J].Chinese Journal of Magnetic Resonance Imaging,2016,7(9):683-688. DOI: 10.12015/issn.1674-8034.2016. 09.009. [8] 周晖, 彭永排, 俞进, 等. «2014年NCCN宫颈癌临床实践指南»解读[J].实用妇产科杂志,2014,30(6):422-425. Zhou H, Peng YP, Yu J, et al. Interpretation of NCCN Clinical Practice Guidelines for Cervical Cancer 2014[J]. Journal of Practical Obstetrics and Gynecology,2014,30(6):422-425. [9] 张凯悦, 钱立庭, 潘博. 宫颈癌同步放化疗预后评估中18F-FDG PET-CT的应用[J].中华肿瘤防治杂志,2021,28(18):1416-1422. DOI: 10.16073/j.cnki.cjcpt.2021.18.10. Zhang KY, Qian LT, Pan B. Application of 18F-FDG PET-CT in cervical cancer patients treated with concurrent chemoradiotherapy[J]. Chinese Journal of Cancer Prevention and Treatment,2021,28(18):1416-1422. DOI: 10.16073/j.cnki.cjcpt.2021.18.10. [10] Matsuo K, Purushotham S, Jiang B, et al. Survival outcome prediction in cervical cancer: Cox models vs deep-learning model[J]. Am J Obstet Gynecol, 2019,220(4):381.e1-381.e14. DOI: 10.1016/j.ajog.2018.12.030. [11] He B, Chen W, Liu L, et al.Prediction models for prognosis of cervical cancer: systematic review and critical appraisal[J]. Front Public Health, 2021,9:654454. DOI: 10.3389/fpubh.2021.654454. [12] 陈静, 曹雷, 顾红梅. 扩散峰度成像在评估宫颈癌病理类型及分化程度中的价值[J].放射学实践,2021,36(5):628-632. DOI: 10.13609/j.cnki.1000-0313.2021.05.011. Chen J, Cao L, Gu HM. The value of DKI-MRI in evaluating pathological types and differentiation degree of cervical cancer[J].Radiologic Practice,2021,36(5):628-632. DOI: 10.13609/j.cnki.1000-0313.2021.05.011. [13] 李悦, 刘岿然. 63例特殊类型宫颈腺癌的临床特征分析[J].现代妇产科进展,2022,31(6):401-406. DOI: 10.13283/j.cnki.xdfckjz.2022.06.031. Li Y, Liu KR. Clinical analysis of 63 cases of special types of cervical adenocarcinoma[J]. Progress in Obstetrics and Gynecology,2022,31(6):401-406. DOI: 10.13283/j.cnki.xdfckjz.2022.06.031. [14] 张芳芳, 黄磊, 李雪娇. RDW与NLR对老年局部晚期宫颈癌患者预后的预测价值[J].中国老年学杂志,2022,42(16):3936-3939. DOI: 10.3969/j.issn.1005-9202.2022.16.018. Zhang FF, Huang L, Li XJ. The prognostic value of RDW and NLR in elderly patients with locally advanced cervical cancer [J]. Chinese Journal of Geriatrics, 2022,42(16): 3936-3939. DOI: 10.3969/j.issn.1005-9202.2022.16.018. [15] Li YX, Chang JY, He MY, et al.Neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR) predict clinical outcome in patients with stage IIB cervical cancer[J]. J Oncol, 2021,2021:2939162. DOI: 10.1155/2021/2939162. [16] 黄丹, 崔树娜, 李士华. 中性粒细胞与淋巴细胞比值影响妇科恶性肿瘤预后的研究进展[J].现代免疫学,2019,39(6):500-504. Huang D, Cui SN, Li SH. Research progress of neutrophil to lymphocyte ratio affecting the prognosis of gynecological malignant tumors[J]. Modern immunology, 2019,39(06): 500-504. [17] 韦苑, 饶群仙, 冯小燕, 等. COX回归联合决策树建立宫颈癌预后的预测模型[J].现代预防医学,2022,49(2):206-212. Wei Y, Rao QX, Feng XY, et al. Prediction model of prognosis of cervical cancer based on COX regression and decision tree[J].Modern Preventive Medicine,2022,49(2):206-212. [18] 周晓爽. 宫颈癌的临床病理特点分析及基于多组学数据的预后预测模型构建[D].北京:北京协和医学院,2020. Zhou XS.Analysis of clinicopathological features of cervical cancer and construction of prognostic prediction model based on multi-omics data [D]. Beijing: Peking Union Medical College, 2020. [19] Malagi AV, Shivaji S, Kandasamy D, et al.Pancreatic mass characterization using IVIM-DKI MRI and machine learning-based multi-parametric texture analysis[J]. Bioengineering (Basel), 2023,10(1):83. DOI: 10.3390/bioengineering10010083. [20] Song JC, Lu SS, Zhang J, et al. Quantitative assessment of diffusion kurtosis imaging depicting deep myometrial invasion: a comparative analysis with diffusion-weighted imaging[J]. Diagn Interv Radiol, 2020,26(2):74-81. DOI: 10.5152/dir.2019.18366. [21] 田士峰, 刘爱连, 郭妍, 等. 基于扩散峰度成像序列平均扩散峰度图的影像组学鉴别宫颈鳞癌与腺癌的应用[J].中国医学影像学杂志,2021,29(7):716-720. DOI: 10.3969/j.issn.1005-5185.2021.07.015. Tian SF, Liu AL, Guo Y, et al. Application of radiomics methods based on DKI sequence MK map for differentiating squamous cell carcinoma from cervix adenocarcinoma[J]. Chinese Journal of Medical Imaging,2021,29(7):716-720. DOI: 10.3969/j.issn.1005-5185. 2021.07.015. [22] 谢刚, 李康. 基于MRI及影像组学的列线图预测脑胶质瘤术后短期复发[J].临床放射学杂志,2022,41(10):1814-1818. Xie G, Li K. A nomogram based on MRI and radiomics for prediction of postoperative recurrence of glioma[J]. Journal of Clinical Radiology,2022,41(10):1814-1818.
2023, Vol. 32 
