联合TRUS和CEUS建立对前列腺外周带病变良恶性的预测模型

doi:10.16352/j.issn.1001-6325.2023.09.1432

基础医学与临床 ›› 2023, Vol. 43 ›› Issue (9): 1432-1438.doi: 10.16352/j.issn.1001-6325.2023.09.1432

联合TRUS和CEUS建立对前列腺外周带病变良恶性的预测模型

孙亚¹, 石艳萍¹, 李华蓉¹, 王嘉俊¹, 徐井旭², 梁蕾^1*

1.航天中心医院超声医学科,北京 100049;
2.北京深睿博联科技有限责任公司研究协作部研发部,北京 100080

收稿日期:2022-09-30 修回日期:2022-12-27 出版日期:2023-09-05 发布日期:2023-09-01
通讯作者: ^*lianglei_csk@126.com
基金资助:
北京市海淀区卫生健康发展科研培育项目(HP2021-32-50702);航天中心医院科研基金(YN202105)

Establishment of a predictive model for benign and malignant prostatic peripheral lesions by combined TRUS and CEUS

SUN Ya¹, SHI Yanping¹, LI Huarong¹, WANG Jiajun¹, XU Jingxu², LIANG Lei^1*

1. Department of Ultrasound Medicine, Aerospace Center Hospital, Beijing 100049;
2. Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing 100080, China

Received:2022-09-30 Revised:2022-12-27 Online:2023-09-05 Published:2023-09-01
Contact: ^*lianglei_csk@126.com

摘要/Abstract

摘要： 目的建立联合经直肠超声(TRUS)常规二维模型(B型)和超声造影(CEUS)预测前列腺外周带病变良恶性的机器学习模型。方法回顾性分析了109例前列腺外周带病灶,良性45例,恶性64例,均以超声引导下靶向穿刺活检病理作为金标准。使用Sonoliver软件获得病变处造影的时间-强度曲线,得到以下参数:峰值强度(IMAX)、上升时间 (RT)、达峰时间 (TTP)和平均渡越时间 (MTT)。收集患者的相关危险因素,包括年龄、总血清前列腺特异性抗原(tPSA)、游离前列腺特异性抗原(fPSA)、游离/总前列腺特异性抗原(f/tPSA)、前列腺体积和前列腺特异性抗原密度 (PSAD)。分别运用特征相关性分析和多元Logistic回归进行影像组学的特征筛选和建模。结果最终筛选出32个特征,建立了3种影像组学相关诊断模型(B模型、CEUS模型、B-CEUS联合模型),得到3种不同的影像组学评分。其中,年龄、PSAD和RT是预测前列腺外周带病灶良恶性的独立危险因素(P＜0.05)。B模型、CEUS型、B-CEUS联合型、危险因素模型、危险因素-影像组学联合模型的诊断准确性分别为0.75、0.71、0.73、0.70、0.84;诊断的曲线下面积(AUC)分别为0.79、0.75、0.84、0.79、0.91。危险因素-影像组学联合模型在验证集中有显著优势(P＜0.05)。结论联合TRUS和CEUS及相关危险因素的机器学习模型可以较好的预测前列腺外周带病变的良恶性,对临床诊断有一定价值。

关键词: 超声造影(CEUS), 前列腺, 外周带, 影像组学

Abstract: Objective To construct a machine learning model based on radiomics features by combing conventional B-mode transrectal ultrasound (TRUS) and contrast-enhanced ultrasound (CEUS)in order to improve prostate cancer (PCa) detection in the peripheral zone, and to explore its clinical application value. Methods A retrospective study of 109 cases (45 benign, 64 malignant lesions) with targeted biopsy-confirmed pathology who underwent B-mode and CEUS examinations was performed. Time-intensity curves were obtained using Sonoliver software forall lesions in regions of interest. The following parameters were collected: the maximum intensity (IMAX), the rise time (RT), the time to peak (TTP), and the mean transit time (MTT). Clinical risk factors, including age, serum total prostate-specific antigen (tPSA), free PSA(fPSA), f/t PSA, prostate volume and prostate-specific antigen density (PSAD), were collected. Feature selection and model construction were carried out by feature correlation analysis and multivariable Logistic regression analysis, respectively. Results Finally, 32 features were screened out by the radiomics models. Three radiomics models (B model, CEUS model and B-CEUS combined model) were established by logistic regression, and radiomics scores were obtained at the same time. The risk factors like age, PSAD, and RT were significant independent predictors (P＜0.05). The diagnostic accuracy of B model, CEUS model, B-CEUS combined model, risk factor model and risk factor-radiomics combined model were 0.75, 0.71, 0.73, 0.70 and 0.84, respectively, and the area under the curves(AUC) were 0.79, 0.75, 0.84, 0.79 and 0.91, respectively. The risk factor-radiomics combined model had a significant significance in the validation cohort (P＜0.05). Conclusions The machine learning model combined with conventional B-mode TRUS, CEUS, and relevant clinical factors can better predict the benign and malignant prostate peripheral lesions, which may significantly support clinical decision-making.

Key words: contrast-enhanced ultrasound (CEUS), prostate, peripheral zone, radiomics

中图分类号:

R737.25

孙亚, 石艳萍, 李华蓉, 王嘉俊, 徐井旭, 梁蕾. 联合TRUS和CEUS建立对前列腺外周带病变良恶性的预测模型[J]. 基础医学与临床, 2023, 43(9): 1432-1438.

SUN Ya, SHI Yanping, LI Huarong, WANG Jiajun, XU Jingxu, LIANG Lei. Establishment of a predictive model for benign and malignant prostatic peripheral lesions by combined TRUS and CEUS[J]. Basic & Clinical Medicine, 2023, 43(9): 1432-1438.

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联合TRUS和CEUS建立对前列腺外周带病变良恶性的预测模型

Establishment of a predictive model for benign and malignant prostatic peripheral lesions by combined TRUS and CEUS

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