瘤内及瘤周DCE-MRI影像组学对宫颈癌患者无进展生存期的预测价值

doi:10.6040/j.issn.1671-7554.0.2024.0958

摘要/Abstract

摘要： 目的探讨瘤内及不同范围瘤周影像组学对行同步放化疗(concurrent chemoradiothrapy, CCRT)局部中晚期宫颈癌患者无进展生存期(progression-free survival, PFS)的预测价值。方法回顾性选取135例经病理证实的宫颈癌患者,包括32例进展和103例无进展患者,以7∶3的比例分为训练集和验证集。基于动态对比增强磁共振成像(dynanic contrast-enhanced magnetic resonance imaging, DCE-MRI)第二期图像在瘤内和3、5、7 mm瘤周区域进行三维容积感兴趣区(volume of interest, VOI)勾画,分别提取影像组学特征并降维,将筛选出的特征构建瘤内、瘤周、瘤内联合瘤周影像组学模型,比较预测效能。保留有统计学意义的临床特征构建临床模型。基于AUC最佳影像组学特征与筛选出的临床特征共同建立综合模型。利用AUC及一致性指数(consistency index, C-index)评估模型预测能力。AUC及C-index值最高的模型继续进行校准曲线、决策曲线分析(decision curve analysis, DCA)及Kaplan-Meier生存曲线后续评估。结果瘤内+5 mm瘤周模型较其他范围影像组学模型显示出较好的预测效能,AUC为0.852。相较于临床和影像组学模型,综合模型显示出最优的预测效能,AUC分别为0.766、0.852、0.872。经校准曲线和DCA分析,综合模型校准度较高,临床净收益较大,Kaplan-Meier生存曲线可区分出发生疾病进展的高风险患者和低风险患者。结论基于DCE-MRI的瘤内联合瘤周影像组学特征可作为评估行CCRT局部中晚期宫颈癌患者PFS的有效指标,其中瘤内+5 mm瘤周模型显示出较高的预测能力,且纳入临床参数的综合模型效能更佳。

关键词: 宫颈癌, 核磁共振, 影像组学, 瘤周微环境, 生存预后

Abstract: Objective To explore the predictive value of intratumoral and peritumoral radiomics in different ranges for progression-free survival(PFS)in patients with locally advanced cervical cancer undergoing concurrent chemotherapy(CCRT). Methods A total of 135 patients with cervical cancer were retrospectively selected, including 32 patients with progression and 103 patients without progression. They were divided into the training set and the validation set in a 7∶ 3 ratio. On the basis of the second phase images of dynanic contrast-enhanced magnetic resonance imaging(DCE-MRI), three-dimensional volume of interest(VOI)delimitations were performed in the 3, 5, and 7 mm areas within and around the tumor. Radiomics features were extracted and dimensionally reduced, respectively. The selected characteristics were used to construct combined intratumoral, peritumoral, and intratumoral-peritumoral radiomic models to compare predictive efficacy. Clinical models were constructed by retaining statistically significant clinical characteristics. A comprehensive model was jointly established based on the best radiomic features of area under curve(AUC)and the screened clinical features. The predictive ability of the model was evaluated using AUC and the consistency index(C-index). The models with the highest AUC and C-index values were used to evaluate the calibration curve, decision curve analysis(DCA)and Kaplan-Meier survival curve. Results The intratumoral + 5 mm peritumoral model showed better predictive efficacy than other radiomic models, with an AUC of 0.852, Compared to the clinical and radiomic models, the comprehensive model showed the best predictive efficacy, with AUCs of 0.766, 0.852, and 0.872, respectively. Through the calibration curve and DCA analysis, the comprehensive model had a high degree of calibration and a large clinical net benefit. The Kaplan-Meier survival curve could distinguish between high-risk patients and low-risk patients with disease progression. Conclusion The combined intratumoral and peritumoral radiomic characteristics based on DCE-MRI can be used as an effective indicator for evaluating PFS in patients with locally advanced cervical cancer undergoing CCRT. Among them, the intratumoral + 5 mm peritumoral model shows higher predictive ability, and the comprehensive model incorporating clinical parameters has better efficacy.

Key words: Cervical cancer, Nuclear magnetic resonance, Imaging omics, Peritumor microenvironment, Survival prognosis

中图分类号:

R737.33

王磊,常霄,王梓萌,李娇娇,崔书君,杨飞,朱月香. 瘤内及瘤周DCE-MRI影像组学对宫颈癌患者无进展生存期的预测价值[J]. 山东大学学报 (医学版), 2025, 63(6): 45-54.

WANG Lei, CHANG Xiao, WANG Zimeng, LI Jiaojiao, CUI Shujun, YANG Fei, ZHU Yuexiang. Predictive value of intratumoral and peritumoral DCE-MRI imaging histology for progression-free survival in patients with cervical cancer[J]. Journal of Shandong University (Health Sciences), 2025, 63(6): 45-54.

参考文献

[1] Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023[J]. CA A Cancer J Clinicians, 2023, 73(1): 17-48.
[2] Gopu P, Antony F, Cyriac S, et al. Updates on systemic therapy for cervical cancer[J]. Indian J Med Res, 2021, 154(2): 293-302.
[3] Li HD, Zhu MC, 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
[4] 张钦和, 刘爱连. MRI功能成像及定量成像技术在宫颈癌诊疗中的应用述评[J]. 磁共振成像, 2024, 15(8): 1-11. ZHANG Qinhe, LIU Ailian. Review on the application of MRI functional and quantitative imaging techniques in the diagnosis and treatment of cervical cancer[J]. Chinese Journal of Magnetic Resonance Imaging, 2024, 15(8): 1-11.
[5] Liu ZY, Wang S, Dong D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges[J]. Theranostics, 2019, 9(5): 1303-1322. doi:10.7150/thno.30309
[6] Marsilla J, Weiss J, Ye XY, et al. A T2-weighted MRI-based radiomic signature for disease-free survival in locally advanced cervical cancer following chemoradiation: an international, multicentre study[J]. Radiother Oncol, 2024, 199: 110463. doi:10.1016/j.radonc.2024.110463
[7] 季中, 王高波, 项万青. 基于临床-影像组学列线图模型对局部晚期宫颈癌同步放化疗后复发和无病生存率的预测价值[J]. 现代实用医学, 2023, 35(11): 1433-1437. JI Zhong, WANG Gaobo, XIANG Wanqing. Prognostic value of clinical-imaging nomogram model in predicting recurrence and disease-free survival of locally advanced cervical cancer after concurrent chemoradiotherapy[J]. Modern Practical Medicine, 2023, 35(11): 1433-1437.
[8] 宋佳成, 蒋晓婷, 张爱宁, 等. 影像组学联合临床病理特征预测早期宫颈癌无病生存期的效能[J]. 南京医科大学学报(自然科学版), 2024, 44(1): 52-59. SONG Jiacheng, JIANG Xiaoting, ZHANG Aining, et al. Value of optimal MRI radiomics parameters and clinicopathological factors in predicting disease-free survival of early-stage cervical cancer patients[J]. Journal of Nanjing Medical University(Natural Sciences), 2024, 44(1): 52-59.
[9] 陈薇, 祝江红, 张新龙, 等. 基于宫颈癌术前T2WI影像组学特征预测近期预后的研究[J]. 宜春学院学报, 2024, 46(3): 49-52. CHEN Wei, ZHU Jianghong, ZHANG Xinlong, et al. Prediction of short-term prognosis based on preoperative T2WI imaging features of cervical cance[J]. Journal of Yichun University, 2024, 46(3): 49-52.
[10] 宋珍珍, 孙小玲, 李海鸥, 等. 120例胶质瘤及瘤周水肿MRI影像组学在评估肿瘤复发中的价值[J]. 山东大学学报(医学版), 2021, 59(11): 53-60. SONG Zhenzhen, SUN Xiaoling, LI Haiou, et al. Value of MRI radiomics of glioma and peritumoral edema in evaluating tumor recurrence[J]. Journal of Shandong University(Health Sciences), 2021, 59(11): 53-60.
[11] 顾均玉, 林锦仕, 张文, 等. 动态对比DCE-MRI在宫颈癌分期诊断中的应用价值[J]. 中国CT和MRI杂志, 2024, 22(10): 118-120. GU Junyu, LIN Jinshi, ZHANG Wen, et al. Dynamic comparison of DCE-MRI for staging and diagnosis of cervical cancer[J]. Chinese Journal of CT and MRI, 2024, 22(10): 118-120.
[12] 何陈伟, 陈亚君, 邹靖, 等. 动态对比增强磁共振成像在局部晚期宫颈癌同步放化疗效果评估中的价值[J]. 分子影像学杂志, 2021, 44(1): 74-77. HE Chenwei, CHEN Yajun, ZOU Jing, et al. Value of DCE-MRI in evaluation of concurrent chemoradiotherapy for locally advanced cervical cancer [J]. Journal of Molecular Imaging, 2021, 44(1): 74-77.
[13] 苏亚英, 石子馨, 张苗, 等. 基于DCE-MRI影像组学定量预测进展期宫颈鳞癌同步放化疗反应的价值[J]. 河北北方学院学报(自然科学版), 2023, 39(2): 11-17. SU Yaying, SHI Zixin, ZHANG Miao, et al. Value of quantitative prediction of response to concurrent chemotherapy and radiation therapy in advanced cervical squamous cell carcinoma based on DCE-MRI radiomics[J]. Journal of Hebei North University(Natural Science Edition), 2023, 39(2): 11-17.
[14] Sharma S, Deep A, Sharma AK. Current treatment for cervical cancer: an update[J]. Anticancer Agents Med Chem, 2020, 20(15): 1768-1779.
[15] 顾尚尉, 艾丛慧, 王关顺, 等. MRI及影像组学在宫颈癌放化疗疗效及预后评估中的研究进展[J]. 放射学实践, 2024, 39(5): 699-703.
[16] 曲晓丽, 张娜, 李发红. 局部晚期宫颈癌淋巴结转移的危险因素及预后分析[J]. 青岛大学学报(医学版), 2024, 60(4): 607-610. QU Xiaoli, ZHANG Na, LI Fahong. Risk factors for lymph node metastasis in locally advanced cervical cancer and their impact on prognosis[J]. Journal of Qingdao University(Medical Sciences), 2024, 60(4): 607-610.
[17] Yip SSF, Aerts HJWL. Applications and limitations of radiomics[J]. Phys Med Biol, 2016, 61(13): R150-R166.
[18] 王茹, 高阳. 多模态MRI影像组学及深度学习在胶质瘤诊疗中的研究进展[J]. 磁共振成像, 2024, 15(7): 165-172. WANG Ru, GAO Yang. Research progress of multimodal MRI radiomics and deep learning in glioma [J]. Chinese Journal of Magnetic Resonance Imaging, 2024, 15(7): 165-172.
[19] 胡逸凡, 潘佳叶, 宫悦, 等. 基于深度学习影像组学在食管癌的研究进展[J]. 放射学实践, 2024, 39(8): 1099-1103. HU Yifan, PAN Jiaye, GONG Yue, et al. Research progress of deep learning-based imaging in esophageal cancer[J]. Radiologic Practice, 2024, 39(8): 1099-1103.
[20] Yang XY, Gao CF, Sun N, et al. An interpretable clinical ultrasound-radiomics combined model for diagnosis of stage I cervical cancer[J]. Front Oncol, 2024, 14: 1353780. doi:10.3389/fonc.2024.1353780
[21] 林宝金, 吴朝霞, 王石, 等. 瘤内及瘤周MR影像组学联合临床特征预测宫颈癌淋巴脉管间隙浸润[J]. 中国医学物理学杂志, 2024, 41(7): 851-857. LIN Baojin, WU Zhaoxia, WANG Shi, et al. Intratumoral and peritumoral magnetic resonance imaging radiomics combined with clinical characteristics to predict lymphovascular space invasion in cervical cancer[J]. Chinese Journal of Medical Physics, 2024, 41(7): 851-857.
[22] Kang WD, Qiu X, Luo YG, et al. Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis[J]. J Transl Med, 2023, 21(1): 598. doi:10.1186/s12967-023-04437-4
[23] Zhang SE, Regan K, Najera J, et al. The peritumor microenvironment: physics and immunity[J]. Trends Cancer, 2023, 9(8): 609-623.
[24] Kim Y, Stolarska MA, Othmer HG. The role of the microenvironment in tumor growth and invasion[J]. Prog Biophys Mol Biol, 2011, 106(2): 353-379.
[25] 吴佩琪, 刘于宝, 陈祉妍, 等. 基于MRI的瘤周影像组学在肿瘤研究中的应用进展[J]. 分子影像学杂志, 2023, 46(1): 164-169. WU Peiqi, LIU Yubao, CHEN Zhiyan, et al. Application progress of MRI-based peritumoral radiomics in tumor research[J]. Journal of Molecular Imaging, 2023, 46(1): 164-169.
[26] Mohammadi A, Mirza-Aghazadeh-Attari M, Faeghi F, et al. Tumor microenvironment, radiology, and artificial intelligence: should we consider tumor periphery?[J]. J Ultrasound Med, 2022, 41(12): 3079-3090.
[27] Sun XJ, Pang PP, Lou L, et al. Radiomic prediction models for the level of Ki-67 and p53 in glioma[J]. J Int Med Res, 2020, 48(5): 300060520914466. doi:10.1177/0300060520914466
[28] Xiao ML, Fu L, Wei Y, et al. Intratumoral and peritumoral MRI radiomics nomogram for predicting parametrial invasion in patients with early-stage cervical adenocarcinoma and adenosquamous carcinoma[J]. Eur Radiol, 2024, 34(2): 852-862.
[29] 黄静, 马彦云, 武静, 等. 基于MRI瘤内联合最佳瘤周影像组学评估乳腺癌Ki-67表达状态的研究[J]. 临床放射学杂志, 2024, 43(8): 1317-1324. HUANG Jing, MA Yanyun, WU Jing, et al. Assessment of ki-67 expression status in breast cancer based on MRI intratumoral combined with optimal peritumoral radiomics[J]. Journal of Clinical Radiology, 2024, 43(8): 1317-1324.
[30] 徐青, 彭雪艳, 郭长义, 等. 基于矢状位T2WI瘤内瘤周影像组学列线图术前预测ⅠB期和ⅡA期宫颈癌的研究[J]. 磁共振成像, 2024, 15(8): 46-51. XU Qing, PENG Xueyan, GUO Changyi, et al. Intra- and peritumoral sagittal T2WI radiomics nomogram for preoperative prediction of patients with stage ⅠB and stage ⅡA cervical cancer [J]. Chinese Journal of Magnetic Resonance Imaging, 2024, 15(8): 46-51.
[31] 蒋旭, 曹海明, 吴宇, 等. 基于CT影像组学预测感染性肾结石的价值[J]. 安徽医药, 2021, 25(7): 1401-1406. JIANG Xu, CAO Haiming, WU Yu, et al. Value of the prediction of infectious kidney stones based on CT radiomics[J]. Anhui Medical and Pharmaceutical Journal, 2021, 25(7): 1401-1406.
[32] 何建风, 刘磊, 吕晋浩, 等. 颅内前后循环动脉粥样硬化斑块的影像组学特征: 多中心前瞻性研究[J]. 协和医学杂志, 2019, 10(1): 53-58. HE Jianfeng, LIU Lei, LYU Jinhao, et al. Radiomic features of atherosclerotic plaques in the anterior and posterior intracranial circulation: multicenter prospective study[J]. Medical Journal of Peking Union Medical College Hospital, 2019, 10(1): 53-58.
[33] Yip SSF, Liu Y, Parmar C, et al. Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer[J]. Sci Rep, 2017, 7(1): 3519. doi:10.1038/S41598-017-02425-5

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed