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

doi:10.6040/j.issn.1671-7554.0.2024.0504

摘要/Abstract

摘要： 目的研究原发性中枢神经系统淋巴瘤(primary central nervous system lymphomas, PCNSL)中表观扩散系数(apparent diffusion coefficient, ADC)、弥散加权成像(diffusion weighted imaging, DWI)和T1对比增强(T1 contrast enhanced, T1-CE)与Ki-67标记指数(labeling index, LI)的相关性,并评估基于多参数MRI影像组学模型区分低增殖PCNSL和高增殖PCNSL的性能。方法本项回顾性研究纳入83例PCNSL患者的MRI图像及临床信息,并利用Spearman相关性分析检验它们与Ki-67 LI的相关性。分别提取三个序列(ADC、DWI和T1-CE)的影像组学特征,并构建不同的影像组学模型。受试者工作特征(receiver operating characteristic, ROC)曲线用于评估模型性能,Delong检验用于比较模型差异。结果相对平均ADC(relative mean ADC, rADCmean)(ρ=-0.354,P=0.019)、相对平均DWI(relative mean DWI, rDWImean)(b=1 000)(ρ=0.273,P=0.013)和相对平均T1-CE(relative mean T1-CE, rT1-CEmean)(ρ=0.385,P=0.001)与Ki-67显著相关。最佳预测模型是组合模型(ADC+DWI+T1-CE)(AUC=0.869)。结论 rDWImean、 rADCmean和rT1-CEmean与Ki-67 LI相关。基于多参数MRI影像组学模型有望区分低增殖PCNSL和高增殖PCNSL。

关键词: 中枢神经系统淋巴瘤, 影像组学, 多参数, Ki-67 标记指数, 磁共振成像

Abstract: Objective To examine the correlation of apparent diffusion coefficient(ADC), diffusion weighted imaging(DWI), and T1 contrast enhanced(T1-CE)with Ki-67 labeling index(LI)in primary central nervous system lymphomas(PCNSL), and to assess the diagnostic performance of MRI radiomics-based models in differentiating the high-proliferation and low-proliferation groups of PCNSL. Methods MRI images and clinical information of 83 PCNSL patients were included, and their correlation with Ki-67 LI was examined using Spearman correlation analysis. The imaging histological features of three sequences were extracted separately and seven different imaging histological models were constructed. The receiver operating characteristic(ROC)curve was used to evaluate the predictive performance of all models. Delong test was utilised to compare the differences of models. Results Relative mean ADC(rADCmean)(ρ=-0.354, P=0.019), relative mean DWI(rDWImean)(b=1,000)(ρ=0.273, P=0.013)and relative mean T1-CE(rT1-CEmean)(ρ=0.385, P=0.001)were significantly correlated with Ki-67. The best prediction model is ADC+DWI+T1-CE(AUC=0.869). Conclusion rDWImean, rADCmean and rT1-CEmean are correlated with Ki-67 LI. The radiomics model based on MRI sequences combination is promising to distinguish low proliferation PCNSL from high proliferation PCNSL.

Key words: Primary central nervous system lymphomas, Radiomics, Multi-parameter, Ki-67 labeling index, Magnetic resonance imaging

中图分类号:

R739.41

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.

WU Siyu, SHEN Yelong, WANG Ximing. Radiomics predicts Ki-67 labeling index in primary central nervous system lymphomas[J]. Journal of Shandong University (Health Sciences), 2024, 62(11): 67-72.

参考文献

[1] Farrall AL, Smith JR. Changing incidence and survival of primary central nervous system lymphoma in Australia: a 33-year national population-based study[J]. Cancers, 2021, 13(3): 403. doi:10.3390/cancers13030403.
[2] Bi SC, Li J, Wang TY, et al. Multi-parametric MRI-based radiomics signature for preoperative prediction of Ki-67 proliferation status in sinonasal malignancies: a two-centre study[J]. Eur Radiol, 2022, 32(10): 6933-6942.
[3] Küker W, Nägele T, Korfel A, et al. Primary central nervous system lymphomas(PCNSL): MRI features at presentation in 100 patients[J]. J Neurooncol, 2005, 72(2): 169-177.
[4] Wieduwilt MJ, Valles F, Issa S, et al. Immunochemotherapy with intensive consolidation for primary CNS lymphoma: a pilot study and prognostic assessment by diffusion-weighted MRI[J]. Clin Cancer Res, 2012, 18(4): 1146-1155.
[5] Chen T, Liu YB, Wang Y, et al. Evidence-based expert consensus on the management of primary central nervous system lymphoma in China[J]. J Hematol Oncol, 2022, 15(1): 136. doi:10.1186/s13045-022-01356-7.
[6] Lohmann P, Franceschi E, Vollmuth P, et al. Radiomics in neuro-oncological clinical trials[J]. Lancet Digit Health, 2022, 4(11): 841-849.
[7] Dohan A, Gallix B, Guiu B, et al. Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab[J]. Gut, 2020, 69(3): 531-539.
[8] 董春桐, 毛宁, 谢海柱, 等. 影像组学及深度学习在预测乳腺癌新辅助化疗疗效中的研究进展[J]. 医学影像学杂志, 2023, 33(4): 652-656. DONG Chuntong, MAO Ning, XIE Haizhu, et al. Research progress of radiomics and deep learning in predicting the response to neoadjuvant chemotherapy for breast cancer[J]. Journal of Medical Imaging, 2023, 33(4): 652-656.
[9] Cho U, Oh WJ, Hong YK, et al. Prognostic significance of high ki-67 index and histogenetic subclassification in primary central nervous system lymphoma[J]. Appl Immunohistochem Mol Morphol, 2018, 26(4): 254-262.
[10] Schaff LR, Grommes C. Primary central nervous system lymphoma[J]. Blood, 2022, 140(9): 971-979.
[11] Kaulen LD, Baehring JM. Treatment options for recurrent primary CNS lymphoma[J]. Curr Treat Options Oncol, 2022, 23(11): 1548-1565.
[12] Neelakantan S, Kumaran SP, Viswamitra S, et al. Myriad of MR imaging phenotypes of primary central nervous system lymphoma in a cohort of immunocompetent Indian patient population[J]. Indian J Radiol Imaging, 2018, 28(3): 296-304.
[13] Holdhoff M, Mrugala MM, Grommes C, et al. Challenges in the treatment of newly diagnosed and recurrent primary central nervous system lymphoma[J]. J Natl Compr Canc Netw, 2020, 18(11): 1571-1578.
[14] Yang H, Xun Y, Yang AP, et al. Advances and challenges in the treatment of primary central nervous system lymphoma[J]. J Cell Physiol, 2020, 235(12): 9143-9165.
[15] Luchini C, Pantanowitz L, Adsay V, et al. Ki-67 assessment of pancreatic neuroendocrine neoplasms: systematic review and meta-analysis of manual vs. digital pathology scoring[J]. Mod Pathol, 2022, 35(6): 712-720.
[16] Raverot G, Ilie MD, Lasolle H, et al. Aggressive pituitary tumours and pituitary carcinomas[J]. Nat Rev Endocrinol, 2021, 17(11): 671-684.
[17] Lenschow C, Schrägle S, Kircher S, et al. Clinical presentation, treatment, and outcome of parathyroid carcinoma: results of the NEKAR retrospective international multicenter study[J]. Ann Surg, 2022, 275(2): e479-e487.
[18] 边毓尧, 石向明. 常规 MR联合 DWI成像鉴别小脑原发性中枢神经系统淋巴瘤与高级别胶质瘤的价值[J]. 中国实验诊断学, 2024, 28(2): 127-131. BIAN Yuyao, SHI Xiangming. The value of conventional MR combined with DWI imaging in differential diagnosis ofp rimary cerebellar central nervous system lymphoma and advanced glioma [J]. Chinese Journal of Laboratory Diagnosis, 2024, 28(2): 127-131.
[19] Chen H, Li W, Wan C, et al. Correlation of dynamic contrast-enhanced MRI and diffusion-weighted MR imaging with prognostic factors and subtypes of breast cancers[J]. Front Oncol, 2022, 12: 942943. doi:10.3389/fonc.2022.942943.
[20] Hu XX, Yang ZX, Liang HY, et al. Whole-tumor MRI histogram analyses of hepatocellular carcinoma: correlations with Ki-67 labeling index[J]. J Magn Reson Imaging, 2017, 46(2): 383-392.
[21] Schob S, Meyer J, Gawlitza M, et al. Diffusion-weighted MRI reflects proliferative activity in primary CNS lymphoma[J]. PLoS One, 2016, 11(8): e0161386. doi:10.1371/journal.pone.0161386.
[22] Zhang B, Tian J, Dong D, et al. Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma[J]. Clin Cancer Res, 2017, 23(15): 4259-4269.
[23] Chen MY, Cao JS, Hu JH, et al. Clinical-radiomic analysis for pretreatment prediction of objective response to first transarterial chemoembolization in hepatocellular carcinoma[J]. Liver Cancer, 2021, 10(1): 38-51.
[24] Bera K, Braman N, Gupta A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology[J]. Nat Rev Clin Oncol, 2022, 19(2): 132-146.
[25] Sun R, Limkin EJ, Vakalopoulou M, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study[J]. Lancet Oncol, 2018, 19(9): 1180-1191.
[26] Xue KM, Liu L, Liu YX, et al. Radiomics model based on multi-sequence MR images for predicting preoperative immunoscore in rectal cancer[J]. Radiol Med, 2022, 127(7): 702-713.
[27] Huang EP, OConnor JPB, McShane LM, et al. Criteria for the translation of radiomics into clinically useful tests[J]. Nat Rev Clin Oncol, 2023, 20(2): 69-82.
[28] Xia W, Hu B, Li HQ, et al. Multiparametric-MRI-based radiomics model for differentiating primary central nervous system lymphoma from glioblastoma: development and cross-vendor validation[J]. J Magn Reson Imaging, 2021, 53(1): 242-250.
[29] Bathla G, Priya S, Liu YN, et al. Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques[J]. Eur Radiol, 2021, 31(11): 8703-8713.
[30] Kang D, Park JE, Kim YH, et al. Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation[J]. Neuro Oncol, 2018, 20(9): 1251-1261.

相关文章 15

[1]	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.
[2]	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.
[3]	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.
[4]	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.
[5]	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.
[6]	朱正阳,沈靖菲,陈思璇,叶梅萍,杨惠泉,周佳南,梁雪,张鑫,张冰. 磁敏感加权成像不同影像组学模型预测胶质瘤IDH基因突变[J]. 山东大学学报 (医学版), 2023, 61(12): 44-50.
[7]	靳新娟,蔡大幸,范金蕾,邓展昊,李楠,于德新,李安宁. 显微镜高分辨率MRI鉴别皮肤良、恶性局灶隆起性结节[J]. 山东大学学报 (医学版), 2023, 61(12): 51-61.
[8]	焦光丽,石子馨,陈蓉,宋亚博,杨飞,崔书君. 基于增强CT影像组学预测卵巢癌患者铂类药物敏感性[J]. 山东大学学报 (医学版), 2023, 61(12): 62-69.
[9]	艾江山,高会江,艾仕文,李恒艳,石国栋,魏煜程. CT影像组学对囊腔型肺癌的诊断价值[J]. 山东大学学报 (医学版), 2023, 61(12): 70-77.
[10]	赵恩举,赵硕,郭云亮,王锡明. 282例颈动脉钙化与脑小血管病MRI总负荷评分的关联性[J]. 山东大学学报 (医学版), 2023, 61(1): 38-44.
[11]	陶国伟,王芳,董向毅,徐亚瑄,赵琳丽,胡蓓蓓. 子宫腺肌病的超声与MRI诊断及进展[J]. 山东大学学报 (医学版), 2022, 60(7): 56-65.
[12]	孙浩瑜,姜鑫,陈守臻,曲思凤,史本康. 多参数磁共振联合前列腺健康指数对PSA灰区临床有意义前列腺癌的诊断价值[J]. 山东大学学报 (医学版), 2022, 60(6): 46-50.
[13]	袁宏涛,纪淙山,康冰,秦松楠,于鑫鑫,高琳,王锡明. CT影像组学对肾上腺乏脂腺瘤与结节样增生的诊断价值[J]. 山东大学学报 (医学版), 2022, 60(4): 68-75.
[14]	冯宝民,王舟,徐晗,李佳存,于乔文,修建军. 抗髓鞘少突胶质细胞糖蛋白IgG抗体相关疾病临床及影像特征[J]. 山东大学学报 (医学版), 2022, 60(3): 45-50.
[15]	左立平,蒋丰洋,周斌彬,范金蕾,梁永锋,邓展昊,于德新. 术前MRI在预测169例肝细胞肝癌微血管侵犯及早期复发的价值[J]. 山东大学学报 (医学版), 2022, 60(3): 89-95.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed