基于磁共振扩散加权图像的诺模图在前列腺癌与前列腺增生鉴别诊断中的应用及其诊断PI-RADS 4分中前列腺癌的可行性

doi:DOI:10.12117/jccmi.2019.12.010

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摘要目的：探讨基于前列腺磁共振扩散加权图像（Diffusion-weighted imaging，DWI）的诺模图鉴别前列腺癌（Prostate cancer，PCa）与前列腺增生（Benign prostatic hyperplasia，BPH）的临床价值，及诊断临床可疑病变PI-RADS评分4分中PCa的可行性。方法：回顾性收集前列腺疾病患者260例，根据病理结果分为PCa（2010年7月—2017年10月，130例）和BPH组（2010年7月—2016年7月，130例）。所有患者均经3.0T MR扫描仪扫描，扫描T1WI、T2WI、DCE-MRI及DWI（b=0、1 000 s/mm2）序列。由2名具有5年MR诊断经验的影像医生协商对所有入组病例进行阅片，记录病灶在DWI图像上的信号特点，根据PI-RADS V2评分标准进行评分。基于DWI的影像组学方法主要分为5个步骤：①图像分割：由2名影像医生在不知道病理结果的情况下通过协商取得一致意见，选取病灶显示最大层面，沿前列腺边缘勾画出包含整个前列腺的感兴趣区域；对于DWI信号未升高的病例，结合T2WI、DCE-MRI序列确定病灶层面。②特征提取：由AK软件自动提取出397个纹理特征，包括大小、形状、直方图、灰度共生矩阵以及灰度运行长度矩阵等。③特征选择：按照7∶3的比例将样本随机划分为训练组和验证组，在训练组样本中，采用最大相关最小冗余算法（MRMR）和LASSO算法选择并保留鲁棒性最好的特征用于建模。④模型构建：基于上述选择的组学特征，建立Logistic回归模型，得到组学模型；基于临床因素包括年龄、病灶位置、DWI信号特点以及TPSA指标水平，构建临床模型；再联合组学模型和临床模型两者得到联合模型，并绘制诺模图。⑤模型验证：将其余30%的数据代入上述模型中，绘制ROC曲线评价3个模型的诊断效能，并对临床可疑病变，即被2名观察者评估为PI-RADS 4分的病变，进行分层分析。绘制校准曲线和决策曲线评估诺模图的校准效能和临床应用价值。结果：本研究构建的诺模图鉴别PCa与BPH的曲线下面积（AUC）在训练组和验证组分别为0.95和0.92；校准曲线及决策曲线分析结果亦显示诺模图具有良好的临床应用价值。在PI-RADS 4分的可疑病变中，诺模图、组学模型和临床模型的AUC分别为0.73、0.81和0.54。结论：基于DWI的诺模图，能够很好地鉴别PCa与BPH；对于PI-RADS 4分的可疑病变，临床模型效能远不如组学模型，联合模型即诺模图诊断效能也低于组学模型，应进一步扩充样本量，深入探究影像组学在临床可疑病变中的鉴别诊断价值。

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	陈丽华1
	刘爱连1
	郭妍2
	李昕2
	郭丹1
	宋清伟1
	魏强1

关键词 ：前列腺肿瘤, 前列腺增生, 磁共振成像

Abstract：Objective: To evaluate radiomics as a tool to distinguish prostate cancer(PCa) frombenign prostatic hyperplasia(BPH) based on diffusion-weighted imaging(DWI) sequence without subjective factors, and diagnosedclinical suspicious lesions (PI-RADS score 4 points) in the feasibility of PCa. Methods: This retrospective study was approved by local IRB, and written informed consent was waived. Two hundred and sixty patients with PCa or BPH who underwent MRI exams between January 2010 and October 2017 were enrolled in this study. Among them, 130 were PCa(between July 2010 and October 2017) and 130(between July 2010 and July 2016) were BPH, confirmed by pathologically. All MRI scans were performed on a 3.0T scanner with an eight-channel phased-array body-coil. T1WI, T2WI, DCE-MRI and DWI(b=0，1 000 s/mm2) were scanned. All the images were read by two radiologists with 5 years’ experience according to the PI-RADS V2. High-throughput extraction and analysis of the radiomic features based on DWI mainly included five key procedures: ①data pre-processing and segmentation were performed by two radiologists who were blinded to pathology, 2D region of interest(ROI) was sketched along the edge of the whole prostate gland at the slice with the maximum diameter of the lesion. ②Three hundred and ninety-seven radiomics features, including size and shape based-features, histogram and GLCM(Gray-Level Co-occurrence Matrix) as well as GLRLM(Gray-Level Run Length Matrix) texture features were generated automatically using Analysis-Kinetics software(GE Healthcare, China). ③Feature selection: according to the ratio of 7∶3, the samples were randomly divided into training and validation set, the training samples, the maximum correlation minimum redundancy algorithm(MRMR) and LASSO select and retain the best robustness characteristics used in modeling. ④Model construction: based on the choice of radiomic features, we established a Logistic regression model, and got the radiomic model. Based on clinical factors, including age, DWI routine diagnostic signal characteristics and TPSA index level, we built clinical model. Combined with radiomics features and clinical data, we got the joint model and nomogram. ⑤Model validation: The rest 30% data were used to validate the models. ROC curves were used to evaluate the diagnostic efficacy of the three models. The clinical suspicious lesions, evaluated as PI-RADS 4 by two radiologists, were underwent hierarchical analysis. Then we used calibration curve and decision curve to evaluate the calibration efficiency and clinical application value of nomogram. Results: The AUC of training group and the validation group were 0.95 and 0.92 respectively in this study to identify the PCa and BPH. The analysis results of calibration curve and decision curve also showed that nomogram had good clinical application value. The AUC of nomogram, radiomics model and clinical model were 0.73, 0.81 and 0.54 respectively in suspicious lesions evaluated as PI-RADS 4. Conclusion: The nomogram based on DWI can identify PCa and BPH well. For the lesions evaluated as PI-RADS 4, the clinical model’s diagnostic efficiency is lower than the radiomics model. The nomogram’s diagnosis efficiency is also lower than the radiomics model. We will make a deep exploration by increasing the sample in the future.

Key words： Prostatic neoplasms Prostatic hyperplasia Magnetic resonance imaging

收稿日期: 2019-10-09

PACS:	Ｒ737.25
	Ｒ697.3
	Ｒ445.2

基金资助:首都科技领军人才培养工程（Z181100006318003）。

通讯作者: 刘爱连，大连医科大学附属第一医院放射科，116011。

作者简介: 陈丽华（1987-），女，辽宁抚顺人，医师。

引用本文:

陈丽华1，刘爱连1，郭妍2，李昕2，郭丹1，宋清伟1，魏强1. 基于磁共振扩散加权图像的诺模图在前列腺癌与前列腺增生鉴别诊断中的应用及其诊断PI-RADS 4分中前列腺癌的可行性[J]. 中国临床医学影像杂志, 2019, 30(12): 881-887.
CEHN Li-hua1, LIU Ai-lian1, GUO Yan2, LI Xin2, GUO Dan1, SONG Qing-wei1, WEI Qiang1. Application of magnetic resonance diffusion-weighted image based nomogram in the differential diagnosis of prostate cancer and prostatic hyperplasia and its feasibility in the diagnosis of prostate cancer with PI-RADS 4. JOURNAL OF CHINA MEDICAL IMAGING, 2019, 30(12): 881-887.

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