探讨灰阶超声图像和超声造影图像的影像转录组学分析方法在前列腺结节良恶性预测诊断中的应用价值。

分析2020年12月至2022年3月在解放军总医院第一医学中心接受超声引导下可疑前列腺癌进行穿刺活检的68例患者。患者均采用经直肠灰阶超声及超声造影2种显像模式，并对图像进行自动分割及纹理特征分析。活检标本进行RNA测序及前列腺癌相关的基因表达谱、功能富集和通路分析。绘制随机森林、贝叶斯和支持向量机3种数据集模型的受试者操作特征（ROC）曲线和校准曲线评价模型的预测效能。

影像组学分析得到2个关键纹理特征：ca2-GLSZM-LZHGE和GLSZM-ZSV。RNA测序发现120个与前列腺癌相关的差异基因，利用影像转录组学方法得到区分前列腺癌和前列腺增生的生物标志物：ITGB3、CAV1、miR141-3p、let-7a-5p、miR25-5p和miR200c-3p，功能富集和通路分析发现上述生物标志物与雄激素受体状态、耐药性、增殖和凋亡相关的转录组改变有关。随机森林、贝叶斯和支持向量机3种模型联合数据集ROC的曲线下面积（AUC）分别为0.99、0.98和0.99，3种模型影像组学数据集AUC分别为0.99、0.95和0.99，分别优于临床数据集AUC（0.79、0.85和0.92）及分子生物标志物数据集（转录组学）AUC（0.66、0.80和0.86）。联合数据集组模型ROC曲线和校准曲线均显示模型区分度和准确度良好。

超声图像纹理特征在评估前列腺癌的生物标志物方面具有潜在的应用价值，并且基于超声图像构建的影像转录组学联合模型比影像组学模型具有更好的预测效能。

To evaluate the value of radiotranscriptomics analysis of gray-scale ultrasound images and contrast-enhanced ultrasound (CEUS) images in the diagnosis of benign and malignant prostate nodules.

A total of 68 patients who underwent ultrasound-guided biopsy for suspected prostate cancer (PCa) at the First Medical Center of Chinese PLA General Hospital from December 2020 to March 2022 were analyzed. All patients underwent transrectal gray-scale ultrasound and CEUS, and the images were automatically segmented and texture features were analyzed. Biopsy specimens were subjected to RNA sequencing and prostate cancer-related gene expression profiling as well as functional enrichment and pathway analysis. Random forest, Bayesian, and support vector machine (SVM) methods were used to draw the receiver operating characteristic (ROC) curve and calibration curve to evaluate the prediction efficiency of the model.

Two key texture features, ca2-GLSZM-LZHGE and GLSZM-ZSV, were obtained by radiomics. RNA sequencing identified 120 differentially expressed genes related to PCa, and the biomarkers to distinguish PCa from benign prostatic hyperplasia (BPH) were obtained by correlation analysis: ITGB3, CAV1, miR141-3p, let-7a-5p, miR25-5p, and miR200c-3p. Functional enrichment and pathway analysis identified transcriptomic alterations associated with androgen receptor status, drug resistance, proliferation, and apoptosis. The area under the ROC curve (AUC) values of the three combined dataset models (random forest, naive Bayes, and SVM) and radiomics dataset models were 0.99, 0.98, and 0.99, and 0.99, 0.95, and 0.99, respectively, which were better than those of the clinical dataset models (0.79, 0.85, and 0.92) and molecular biomarker dataset (transcriptomics) models (0.66, 0.80, and 0.86). The ROC curve and calibration curve of the combined dataset group showed that the model had good discrimination and accuracy.

Ultrasound image texture features have potential application value in the evaluation of biomarkers of PCa, and the combined radiotranscriptomics model has better predictive efficiency than the radiomics model.