Cui Yadong, Han Siyuan, Liu Ming, Wu Pu-Yeh, Zhang Wei, Zhang Jintao, Li Chunmei, Chen Min
Department of Radiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing P. R., China.
Graduate School of Peking Union Medical College, Beijing P. R., China.
J Magn Reson Imaging. 2020 Aug;52(2):552-564. doi: 10.1002/jmri.27075. Epub 2020 Feb 6.
The interpretation system for prostate MRI is largely based on qualitative image contrast of different tissue types. Therefore, a fast, standardized, and robust quantitative technique is necessary. Synthetic MRI is capable of quantifying multiple relaxation parameters, which might have potential applications in prostate cancer (PCa).
To investigate the use of quantitative relaxation maps derived from synthetic MRI for the diagnosis and grading of PCa.
Prospective.
In all, 94 men with pathologically confirmed PCa or benign pathological changes.
FIELD STRENGTH/SEQUENCE: T -weighted imaging, T -weighted imaging, diffusion-weighted imaging, and synthetic MRI at 3.0T.
Four kinds of tissue types were identified on pathology, including PCa, stromal hyperplasia (SH), glandular hyperplasia (GH), and noncancerous peripheral zone (PZ). PCa foci were grouped as low-grade (LG, Gleason score ≤6) and intermediate/high-grade (HG, Gleason score ≥7). Regions of interest were manually drawn by two radiologists in consensus on parametric maps according to the pathological results.
Independent sample t-test, Mann-Whitney U-test, and receiver operating characteristic curve analysis.
T and T values of PCa were significantly lower than SH (P = 0.015 and 0.002). The differences of T and T values between PCa and noncancerous PZ were also significant (P ≤ 0.006). The area under the curve (AUC) of the apparent diffusion coefficient (ADC) value was significantly higher than T , T , and proton density (PD) values in discriminating PCa from SH and noncancerous PZ (P ≤ 0.025). T , PD, and ADC values demonstrated similar diagnostic performance in discriminating LG from HG PCa (AUC = 0.806 [0.640-0.918], 0.717 [0.542-0.854], and 0.817 [0.652-0.925], respectively; P ≥ 0.535).
Relaxation maps derived from synthetic MRI were helpful for discriminating PCa from other benign pathologies. But the overall diagnostic performance was inferior to the ADC values. T , PD, and ADC values performed similarly in discriminating LG from HG PCa lesions.
2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;52:552-564.
前列腺MRI的解读系统很大程度上基于不同组织类型的定性图像对比度。因此,需要一种快速、标准化且稳健的定量技术。合成MRI能够量化多个弛豫参数,这可能在前列腺癌(PCa)中具有潜在应用。
研究从合成MRI获得的定量弛豫图在PCa诊断和分级中的应用。
前瞻性研究。
总共94名经病理证实患有PCa或良性病理改变的男性。
场强/序列:3.0T下的T加权成像、T加权成像、扩散加权成像和合成MRI。
病理上确定了四种组织类型,包括PCa、基质增生(SH)、腺性增生(GH)和非癌性外周带(PZ)。PCa病灶分为低级别(LG,Gleason评分≤6)和中/高级别(HG,Gleason评分≥7)。两名放射科医生根据病理结果在参数图上共同手动绘制感兴趣区域。
独立样本t检验、Mann-Whitney U检验和受试者操作特征曲线分析。
PCa的T和T值显著低于SH(P = 0.015和0.002)。PCa与非癌性PZ之间的T和T值差异也显著(P≤0.006)。在区分PCa与SH和非癌性PZ时,表观扩散系数(ADC)值的曲线下面积(AUC)显著高于T、T和质子密度(PD)值(P≤0.025)。T、PD和ADC值在区分低级别与高级别PCa时表现出相似的诊断性能(AUC分别为0.806[0.640 - 0.918]、0.717[0.542 - 0.854]和0.817[0.652 - 0.925];P≥0.535)。
从合成MRI获得的弛豫图有助于区分PCa与其他良性病变。但总体诊断性能不如ADC值。T、PD和ADC值在区分低级别与高级别PCa病变时表现相似。
2 技术效能阶段:2 《磁共振成像杂志》2020年;52:552 - 564。