基于高分辨T2WI联合表观扩散系数图像影像组学对前列腺癌的鉴别诊断价值

doi:10.7517/issn.1674-0475.230604

影像科学与光化学 ›› 2023, Vol. 41 ›› Issue (5): 247-252.DOI: 10.7517/issn.1674-0475.230604

基于高分辨T2WI联合表观扩散系数图像影像组学对前列腺癌的鉴别诊断价值

吴山¹, 崔静静², 李雪婷³, 李瑞¹, 任瑞民⁴, 闫俊荣¹

1. 山西白求恩医院放射科, 山西太原 030032;
2. 慧影医疗科技有限公司, 北京 100192;
3. 山西白求恩医院病理科, 山西太原 030032;
4. 山西白求恩医院泌尿外科, 山西太原 030032

收稿日期:2023-06-20 出版日期:2023-09-23 发布日期:2023-10-28
通讯作者: 闫俊荣

Diagnosis Value of Prostate Cancer Based on High-Resolution T2WI Combined with Apparent Diffusion Coefficient Imaging Radiomics

WU Shan¹, CUI Jingjing², LI Xueting³, LI Rui¹, REN Ruimin⁴, YAN Junrong¹

1. Department of Radiology, Shanxi Bethune Hospital, Taiyuan 030032, Shanxi, P.R. China;
2. Huiying Medical Technology Co., Beijing, 100192, P.R. China;
3. Department of Pathology, Shanxi Bethune Hospital, Taiyuan 030032, Shanxi, P.R. China;
4. Department of Urology, Shanxi Bethune Hospital, Taiyuan 030032, Shanxi, P.R. China

Received:2023-06-20 Online:2023-09-23 Published:2023-10-28

摘要/Abstract

摘要： 评价基于高分辨T2WI联合表观扩散系数图像(ADC)影像组学对前列腺癌的鉴别诊断价值。回顾性分析120例前列腺疾病患者,包括71例前列腺癌患者和49例非前列腺癌患者。将这些患者的病变分别分为外周带(PZ)和中央区(CZ)组。利用Radcloud平台对MRI图像进行数据分析,通过受试者操作特征曲线(ROC)计算曲线下面积(AUC)、敏感性、特异性,比较T2WI、ADC、T2WI+ADC影像组学诊断模型及前列腺影像报告和数据系统第二版(PI-RADS v2)对前列腺癌的鉴别诊断价值。结果显示,在测试集中,外周带区,T2WI+ADC模型对前列腺良恶性病灶的鉴别诊断价值优于T2WI模型及PI-RADS v2(P＜0.05);在中央区,ADC模型的诊断价值优于T2WI模型及PI-RADS v2 (P＜0.05);对于PI-RADS评分为3分的病灶,T2WI+ADC模型的AUC在外周带及中央区分别为0.89和0.94。基于高分辨T2WI联合ADC影像组学模型对前列腺癌的鉴别诊断价值优于PI-RADS v2,同时对于前列腺PI-RADS 评分为3分的病灶也有一定的鉴别诊断价值。

关键词: 影像组学, 前列腺癌, 磁共振成像, PI-RADS v2

Abstract: To evaluate the diagnostic value of radiomics based on high-resolution T2WI combined with apparent diffusion coefficient imaging (ADC) for prostate cancer. Data from 120 patients who underwent MRI and Confirmed by pathology were reviewed by a retrospective study, including 71 prostate cancer and 49 no-prostate cancer. The lesions of these patients were divided into peripheral zone (PZ) and central zone (CZ) groups, respectively. Radcloud platform was used for data analysis of MRI images, and the area under the curve (AUC) was calculated by receiver operating characteristic curve (ROC). T2WI, ADC, T2WI+ADC computer aided diagnostic model and Prostate Imaging Report and Data System version 2 (PI-RADS v2) were compared in 247distinguishing benign and malignant prostate lesion. The results show in the test set, the performance of Model T2WI+ADC was significantly higher than Model T2WI and PI-RADS in the peripheral zone (P<0.05), Model ADC was significantly higher than Model T2WI and PI-RADS in the central zone (P<0.05). For PI-RADS 3 lesion, the area under curve of the Model T2WI+ADC were 0.89 and 0.94 in the peripheral zone and central zone, respectively. Radiomics based on high-resolution T2WI combined with apparent diffusion coefficient imaging achieved a higher diagnostic efficiency than PI-RADS v2, including PI-RADS 3 lesion.

Key words: radiomics, prostate cancer, magnetic resonance imaging, PI-RADS v2

吴山, 崔静静, 李雪婷, 李瑞, 任瑞民, 闫俊荣. 基于高分辨T2WI联合表观扩散系数图像影像组学对前列腺癌的鉴别诊断价值[J]. 影像科学与光化学, 2023, 41(5): 247-252.

WU Shan, CUI Jingjing, LI Xueting, LI Rui, REN Ruimin, YAN Junrong. Diagnosis Value of Prostate Cancer Based on High-Resolution T2WI Combined with Apparent Diffusion Coefficient Imaging Radiomics[J]. IMAGING SCIENCE AND PHOTOCHEMISTRY, 2023, 41(5): 247-252.

参考文献

[1] Chen W Q, Zheng R S, Baade P D, et al. Cancer statistics in China, 2015[J]. CA-A Cancer Journal for Clinicians, 2016, 66(2): 115-132.
[2] Haider M A, van der Kwast T H, Tanguay J, et al. Combined T2-weighted and diffusion-weighted MRI for localization of prostate cancer[J]. American Journal of Roentgenology, 2007, 189(2): 323-328.
[3] Kumar V, Bora G S, Kumar R, et al. Multiparametric (mp) MRI of prostate cancer[J]. Progress in Nuclear Magnetic Resonance Spectroscopy, 2018, 105: 23-40.
[4] Kozlowski P, Chang S D, Jones E C, et al. Combined diffusion-weighted and dynamic contrast-enhanced MRI for prostate cancer diagnosis-correlation with biopsy and histopathology[J]. Journal of Magnetic Resonance Imaging, 2006, 24(1): 108-113.
[5] Auer T, Edlinger M, Bektic J, et al. Performance of PI-RADS version 1 versus version 2 regarding the relation with histopathological results[J]. World Journal of Urology, 2017, 35(5): 687-693.
[6] Khalvati F, Wong A, Haider M A. Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models[J]. BMC Medical Imaging, 2015, 15: 27.
[7] Ishioka J, Matsuoka Y, Uehara S, et al. Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm[J]. BJU International, 2018, 122(3): 411-417.
[8] Vignati A, Mazzetti S, Giannini V, et al. Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness[J]. Physics in Medicine and Biology, 2015, 60(7): 2685-2701.
[9] Patel N, Henry A, Scarsbrook A. The value of MR textural analysis in prostate cancer[J]. Clinical Radiology, 2019, 74(11): 876-885.
[10] Viswanath S E, Bloch N B, Chappelow J C, et al. Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 Tesla endorectal, in vivo T2-weighted MR imagery[J]. Journal of Magnetic Resonance Imaging, 2012, 36(1): 213-224.
[11] Wibmer A, Hricak H, Gondo T, et al. Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores[J]. European Radiology, 2015, 25(10): 2840-2850.
[12] Weinreb J C, Barentsz J O, Choyke P L, et al. PI-RADS prostate imaging-reporting and data system: 2015, version 2[J]. European Urology, 2016, 69(1): 16-40.
[13] Chen T, Li M J, Gu Y F, et al. Prostate cancer differentiation and aggressiveness: Assessment with a radiomic-based model vs. PI-RADS v2[J]. Journal of Magnetic Resonance Imaging, 2019, 49(3): 875-884.
[14] 居敏昊, 魏超刚, 陆志华, 等. 不同版本PI-RADS(v1与v2)对前列腺癌诊断的一致性分析[J]. 放射学实践, 2019, 34(10): 1132-1136.
[15] 姬健智, 张倩, 牛猛, 等. 联合临床、MR T2WI及表观弥散系数图影像组学特征列线图预测初发前列腺癌骨转移[J]. 中国医学影像技术, 2022, 38(7): 1050-1055.
[16] Giambelluca D, Cannella R, Vernuccio F, et al. PI-RADS 3 lesions: Role of prostate MRI texture analysis in the identification of prostate cancer[J]. Current Problems in Diagnostic Radiology, 2021, 50(2): 175-185.

基于高分辨T2WI联合表观扩散系数图像影像组学对前列腺癌的鉴别诊断价值

Diagnosis Value of Prostate Cancer Based on High-Resolution T2WI Combined with Apparent Diffusion Coefficient Imaging Radiomics

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	朱桐, 吴艳云, 陈宇鲜. 基于HR-MRI颅内血管斑块参数与老年初发和再发脑梗死患者预后的相关性分析[J]. 影像科学与光化学, 2023, 41(6): 307-313.
[2]	袁艳, 胡萍香, 林燕秋, 丘远婵, 曾卫姗, 吴姗. 腹部超声联合DCE-MRI在卵巢瘤样病变中的诊断灵敏性分析[J]. 影像科学与光化学, 2023, 41(6): 314-319.
[3]	杨巧飞, 杨普, 毕孝杨, 李正亮, 杨璐帆, 唐艳隆. 基于MRI列线图模型对乳腺良恶性结节的预测价值研究[J]. 影像科学与光化学, 2023, 41(6): 338-344.
[4]	张晓盼, 杨帆, 金学民, 程敬亮, 文宝红. 联合低频振幅和度中心性的高度近视患者脑功能研究[J]. 影像科学与光化学, 2023, 41(5): 230-237.
[5]	牛犇, 曹辉. MRI联合经直肠超声靶向穿刺在前列腺癌中的应用[J]. 影像科学与光化学, 2023, 41(1): 6-10.
[6]	张倩倩, 李岩, 任虹, 肖爱伟, 刘珊珊, 于桂泳. DE-ICER在无先兆偏头痛平衡针刺后血脑屏障改变中的评估作用及与镇痛效果的关系[J]. 影像科学与光化学, 2023, 41(1): 11-16.
[7]	邹丽雯, 汪月, 潘毓玲, 曾鸿君. MRI、TEG参数诊断ACI溶栓后发生出血性转化的研究[J]. 影像科学与光化学, 2023, 41(1): 23-27.
[8]	周悦, 齐恩林, 刘树荣, 姜立杰. 磁共振成像中DKI模型诊断软组织肉瘤的价值及对手术治疗的指导意义[J]. 影像科学与光化学, 2023, 41(1): 43-47.
[9]	王艳军, 赵德彩, 孙政, 张鹏, 路磊. MRI诊断侵袭性垂体腺瘤的特征分析及内镜下双鼻孔入路的手术效果观察[J]. 影像科学与光化学, 2023, 41(1): 90-95.
[10]	袁永睿, 李争民, 王长平. 脑脊液CD64、血清炎性因子联合MRI对术后继发性神经系统感染的诊断分析[J]. 影像科学与光化学, 2023, 41(1): 119-123.
[11]	郑奕, 江迎春, 杨豪, 董战军, 陈艳, 李思强. 药物治疗前后RA患者DCE-MRI参数变化及对骨侵蚀情况的影响[J]. 影像科学与光化学, 2023, 41(1): 136-141.
[12]	赵燕, 孙勇. 多参数磁共振成像在诊断前列腺癌及疾病危险程度中的应用[J]. 影像科学与光化学, 2023, 41(1): 177-181.
[13]	郑大军, 汪牛, 邱雷. MRI参数对篮球运动员剥脱性骨软骨炎的诊断及术后康复效果评价[J]. 影像科学与光化学, 2023, 41(1): 191-195.
[14]	张宏光, 秦绪沛, 王康妮. MSCT结合MRI对成人腕关节扭伤的影像分析[J]. 影像科学与光化学, 2022, 40(6): 1323-1327.
[15]	焦悦, 姚水平, 郭丽, 国春蕾. MRI联合血清AFP在诊断CSP及胎盘植入中的应用研究[J]. 影像科学与光化学, 2022, 40(6): 1381-1385.