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

doi:10.6040/j.issn.1671-7554.0.2021.0997

摘要/Abstract

摘要： 目的探讨胶质瘤及瘤周水肿(PTE)MRI影像组学在评估肿瘤复发中的价值。方法选取山东大学齐鲁医院2013年1月至2020年12月经术后病理证实的胶质瘤患者120例,包括55例复发和65例无复发患者,根据术前T₂WI和T₁WI增强图像对肿瘤和PTE进行三维容积感兴趣区勾画,并按照8∶2的比例分为训练组和验证组,分析两者及联合的组学特征与肿瘤复发的关系。使用受试者工作特征(ROC)曲线下面积(AUC)与准确性矩阵,比较和评价不同影像组学模型的训练结果。结果对于PTE,K临近法(KNN)分类器预测效能最好:训练组AUC值、敏感度、特异度分别为0.910、0.84、0.88,验证组分别为0.916、0.82、0.93。对于肿瘤,逻辑回归(LR)分类器预测效能最好:训练组AUC值、敏感度和特异度分别为0.777、0.69、0.67,验证组分别为0.758、0.82、0.92。当肿瘤+PTE联合时,逻辑回归(LR)分类器预测效能最好:训练组AUC值、敏感度、特异度为0.977、0.88、0.89,验证组则为0.841、0.73、0.83。结论胶质瘤PTE和肿瘤影像组学特征在预测胶质瘤术后复发方面具有一定的价值,其中PTE的KNN组学模型效能最佳。

关键词: 影像组学, 磁共振成像, 胶质瘤, 瘤周水肿, 术后复发

Abstract: Objective To explore the value of MRI radiomics of glioma and peritumoral edema(PTE)in evaluating the postoperative recurrence. Methods A total of 120 patients with glioma confirmed by postoperative pathology during Jan. 2013 and Dec. 2020 were retrospectively selected, including 55 cases with recurrence and 65 cases without recurrence. The tumor and PTE were delineated by three-dimensional volumetric regions of interest based on the preoperative T₂WI and contrast enhanced T1WI images, which were divided into training group and validation group according to the ratio 8∶2. The relationship between tumor recurrence and the radiomic characteristics was analyzed. The receiver operating characteristic(ROC)curve was drawn, and the area under curve(AUC)and accuracy matrix were used to compare and evaluate the results of different radiomic models. Results For PTE, KNN classifier had the best prediction performance(AUC=0.910, sensitivity=0.84, specificity=0.88), while in the validation group, the AUC, sensitivity, and specificity were 0.916, 0.82 and 0.93, respectively. For tumor, LR classifier had the best prediction performance: the AUC, sensitivity and specificity of the training group were 0.777, 0.69 and 0.67, respectively, while in the validation group, they were 0.758, 0.82 and 0.92, respectively. In the model of tumor connected with PTE, LR classifier had the best prediction performance: the AUC, sensitivity and specificity of the training group were 0.977, 0.88 and 0.89, respectively, while in the validation group, they were 0.841, 0.73 and 0.83, respectively. Conclusion The MRI radiomic features of PTE and glioma are valuable to predict postoperative recurrence, and the KNN model of PTE has the best diagnostic efficacy.

Key words: Radiomics, Magnetic resonance imaging, Glioma, Peripheral edema, Postoperative recurrence

中图分类号:

R455.2

宋珍珍,孙小玲,李海鸥,王芳,张冉,于德新. 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, WANG Fang, ZHANG Ran, YU Dexin. 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.

参考文献

[1] Ostrom QT, Patil N, Cioffi G, et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2013-2017[J]. Neuro Oncol, 2020, 22(12 suppl 2): 1-96.
[2] 中国医师协会神经外科医师分会脑胶质瘤专业委员会.胶质瘤多学科诊治(MDT)中国专家共识[J]. 中华神经外科杂志, 2018, 34(2): 113-118.
[3] Kim CS, Jung S, Jung TY, et al. Characterization of invading glioma cells using molecular analysis of leading-edge tissue[J]. J Korean Neurosurg Soc, 2011, 50(3):157-165. doi:10.3340/jkns.2011.50.3.157.
[4] Weller M, Cloughesy T, Perry JR, et al. Standards of care for treatment of recurrent glioblastoma: are we there yet? [J]. Neuro Oncol, 2013, 15(1): 4-27.doi:10.1093/neuonc/nos273.
[5] 景辉,张辉,秦丹蕾. 脑胶质瘤复发及治疗的影像学研究进展 [J].中国药物与临床杂志,2021,21(2):247-248.
[6] 潮布告, 高宇. PET/CT在脑胶质瘤术后放射治疗中的应用进展[J]. 中国微侵袭神经外科杂志, 2021, 26(1): 43-45. doi:10.11850/j.issn.1009-122X.2021.01.013.
[7] Shboul ZA, Alam M, Vidyaratne L, et al. Feature-guided deep radiomics for glioblastoma patient survival prediction[J]. Front Neurosci, 2019, 13: 966. doi:10.3389/fnins.2019.00966.
[8] Lin ZX. Glioma-related edema: new insight into molecular mechanisms and their clinical implications[J]. Chin J Cancer, 2013, 32(1): 49-52.
[9] 陈科宇, 江普查.瘤周水肿与人脑高级别胶质瘤预后的相关性[J].中国临床神经外科杂志,2021(5):352-353, 359. CHEN Keyu, JIANG Pucha.Correlation between peritumoral edema of high-grade glioma and patients prognoses[J]. Chinese Journal of Clinical Neurosurgery, 2021(5):352-353, 359.
[10] 郭翌, 周世崇, 余锦华, 等. 影像组学的前沿研究与未来挑战[J]. 肿瘤影像学, 2017, 26(2): 81-90. GUO Yi, ZHOU Shichong, YU Jinhua, et al. Advanced researches and future challenges of radiomics[J]. Oncoradiology, 2017, 26(2): 81-90.
[11] Leao DJ, Craig PG, Godoy LF, et al. Response assessment in neuro-oncology criteria for gliomas: practical approach using conventional and advanced techniques[J]. AJNR Am J Neuroradiol, 2020, 41(1): 10-20.
[12] 陈绪珠, 马军. 影像组学在脑胶质瘤中的研究进展[J]. 磁共振成像, 2018, 9(10): 721-724. CHEN Xuzhu, MA Jun. Status of radiomics in cerebral giomas[J]. Chinese Journal of Magnetic Resonance Imaging, 2018, 9(10): 721-724.
[13] Sun MZ, Oh T, Ivan ME, et al. Survival impact of time to initiation of chemoradiotherapy after resection of newly diagnosed glioblastoma[J]. Journal of neurosurgery, 2015, 122: 1144-1150. doi:10.3171/2014.9.JNS14193.
[14] 国家卫生健康委员会医政医管局. 脑胶质瘤诊疗规范(2018年版)[J]. 中华神经外科杂志, 2019, 35(3): 217-239.
[15] 王强, 王荣福. PET/CT多模态显像技术特点及临床应用进展[J]. 中国医学装备, 2013, 10(1): 60-62. WANG Qiang, WANG Rongfu. Technical features and clinical application of PET/CT multi-mode imaging[J]. China Medical Equipment, 2013, 10(1): 60-62.
[16] Lin Y, Li J, Zhang Z, et al. Comparison of intravoxel incoherent motion diffusion-weighted MR imaging and arterial spin labeling MR imaging in gliomas[J]. Biomed Res Int, 2015, 2015: 234-245. doi:10.1155/2015/234245.
[17] Cho H, Park H. Classification of low-grade and high-grade glioma using multi-modal image radiomics features[J]. Annu Int Conf IEEE Eng Med Biol Soc,2017,2017: 3081-3084. doi:10.1109/EMBC.2017.8037508.
[18] Yu JH, Shi ZF, Lian YX, et al. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade Ⅱ glioma[J]. Eur Radiol, 2017, 27(8): 3509-3522.
[19] Kickingereder P, Burth S, Wick A, et al. Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models[J]. Radiology, 2016, 280(3): 880-889.
[20] Qian Z, Li Y, Sun Z, et al. Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction[J]. Aging, 2018, 10(10): 2884-2899. doi:10.18632/aging.101594.
[21] Choi YS, Ahn SS, Chang JH, et al. Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction[J]. Eur Radiol, 2020, 30(7): 3834-3842.
[22] Lemee JM, Clavreul A, Menei P. Intratumoral heterogeneity in glioblastoma: dont forget the peritumoral brain zone[J]. Neuro Oncol, 2015, 17(10): 1322-1332.
[23] Wang XF, Liu XY, Chen YP, et al. Histopathological findings in the peritumoral edema area of human glioma[J]. Histol Histopathol, 2015, 30(9): 1101-1109.
[24] Wang Q, Zhang J, Xu X, et al. Diagnostic performance of apparent diffusion coefficient parameters for glioma grading[J]. J Neurooncol, 2018, 139(1): 61-68.
[25] De Vleeschouwer S, Ardon H, Van Calenbergh F, et al. Stratification according to HGG-IMMUNO RPA model predicts outcome in a large group of patients with relapsed malignant glioma treated by adjuvant postoperative dendritic cell vaccination[J]. Cancer Immunol Immunother, 2012, 61(11): 2105-2112.
[26] 杜谋选, 袁军. 影响人脑胶质瘤的预后因素[J]. 中华神经医学杂志, 2005, 4(2): 145-148. DU Mouxuan, YUAN Jun. Prognostic factors for patients with glioma[J]. Chinese Journal of Neuromedicine, 2005, 4(2): 145-148.

相关文章 15

[1]	王琳琳孙美丽孙玉萍张楠刘传勇. 中心体α-微管蛋白、γ-微管蛋白在脑胶质瘤中的表达及其与Survivin表达的相关性研究[J]. 山东大学学报(医学版), 2209, 47(6): 103-.
[2]	金晨曦,沈薇,李娜,孙建锋,杨驰,郭泾. ADDWoR关节镜下盘复位术盘-髁运动的定量分析[J]. 山东大学学报 (医学版), 2025, 63(7): 54-61.
[3]	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.
[4]	刘晶晶,庞婧,赵晓丹,林昕,付敏,陈静静. 基于乳腺X线摄影及DCE-MRI机器学习模型预测乳腺癌新辅助治疗后病理完全缓解:双中心研究[J]. 山东大学学报 (医学版), 2025, 63(1): 60-72.
[5]	孙婧,杨瑞敏,王聪,张月,罗兵. 基于术前超声、炎症指标及超声影像组学联合模型预测乳腺癌腋窝淋巴结转移[J]. 山东大学学报 (医学版), 2025, 63(1): 73-80.
[6]	李永,崔书君,杨飞,张凡,殷晓霞. 基于增强MRI的亚区域影像组学模型可预测乳腺癌患者新辅助化疗后的病理完全反应[J]. 山东大学学报 (医学版), 2025, 63(1): 81-89.
[7]	卢晓颂,杨瑞敏,王义成,周海丰,罗兵,李晓宇,李娜娜. 含瘤周组织的超声影像组学在鉴别乳腺结节良恶性中的价值[J]. 山东大学学报 (医学版), 2025, 63(1): 90-98.
[8]	郭姝画,樊扬,田风,王传新,杜鲁涛,李培龙,郭兴,徐硕. 微原纤维相关蛋白3在调控胶质瘤干细胞间充质表型转化中的作用[J]. 山东大学学报 (医学版), 2024, 62(6): 9-16.
[9]	刘小文,曹永泉,侯明源,于德新. EOB-MRI多定量参数对肝胆期乏血供低信号结节进展风险的评估价值[J]. 山东大学学报 (医学版), 2024, 62(4): 31-39.
[10]	吴思雨,沈业隆,王锡明. 影像组学预测原发性中枢神经系统淋巴瘤的Ki-67标记指数[J]. 山东大学学报 (医学版), 2024, 62(11): 67-72.
[11]	李夕凤,李红梅. 脑脊液CXCL10:抗NMDAR脑炎潜在的生物学标志物[J]. 山东大学学报 (医学版), 2023, 61(6): 47-52.
[12]	靳新娟,左立平,邓展昊,李安宁,于德新. MRI影像组学对135例肝癌耐药蛋白PFKFB3的预测价值[J]. 山东大学学报 (医学版), 2023, 61(6): 79-86.
[13]	刘艳,冷珊珊,夏晓娜,董昊,黄陈翠,孟祥水. 基于影像组学参数评估376例幕上自发性脑出血患者的功能状态[J]. 山东大学学报 (医学版), 2023, 61(5): 59-67.
[14]	王磊,张帅,刘钢,由胜男,王植,朱珊,陈超,马信龙,杨强. MRI诊断140例腰椎智能网络自动检测分型MCs方法的比较[J]. 山东大学学报 (医学版), 2023, 61(3): 71-79.
[15]	杨咏青,赵鹏,汪玉,马文静,田迷迷,程亚旎,祖璐,林祥涛. 细胞外容积分数对62例不同病理类型肺癌的诊断价值[J]. 山东大学学报 (医学版), 2023, 61(2): 88-94.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed