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基于定量扩散磁共振成像的自动化患者层面前列腺癌检测

Automated Patient-level Prostate Cancer Detection with Quantitative Diffusion Magnetic Resonance Imaging.

作者信息

Zhong Allison Y, Digma Leonardino A, Hussain Troy, Feng Christine H, Conlin Christopher C, Tye Karen, Lui Asona J, Andreassen Maren M S, Rodríguez-Soto Ana E, Karunamuni Roshan, Kuperman Joshua, Kane Christopher J, Rakow-Penner Rebecca, Hahn Michael E, Dale Anders M, Seibert Tyler M

机构信息

Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.

Department of Radiology, University of California San Diego, La Jolla, CA, USA.

出版信息

Eur Urol Open Sci. 2022 Dec 15;47:20-28. doi: 10.1016/j.euros.2022.11.009. eCollection 2023 Jan.

Abstract

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the subjective Prostate Imaging Reporting and Data System (PI-RADS) system and quantitative apparent diffusion coefficient (ADC) are inconsistent. Restriction spectrum imaging (RSI) is an advanced diffusion-weighted MRI technique that yields a quantitative imaging biomarker for csPCa called the RSI restriction score (RSI).

OBJECTIVE

To evaluate RSI for automated patient-level detection of csPCa.

DESIGN SETTING AND PARTICIPANTS

We retrospectively studied all patients ( = 151) who underwent 3 T mpMRI and RSI (a 2-min sequence on a clinical scanner) for suspected prostate cancer at University of California San Diego during 2017-2019 and had prostate biopsy within 180 d of MRI.

INTERVENTION

We calculated the maximum RSI and minimum ADC within the prostate, and obtained PI-RADS v2.1 from medical records.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We compared the performance of RSI, ADC, and PI-RADS for the detection of csPCa (grade group ≥2) on the best available histopathology (biopsy or prostatectomy) using the area under the curve (AUC) with two-tailed  = 0.05. We also explored whether the combination of PI-RADS and RSI might be superior to PI-RADS alone and performed subset analyses within the peripheral and transition zones.

RESULTS AND LIMITATIONS

AUC values for ADC, RSI, and PI-RADS were 0.48 (95% confidence interval: 0.39, 0.58), 0.78 (0.70, 0.85), and 0.77 (0.70, 0.84), respectively. RSI and PI-RADS were each superior to ADC for patient-level detection of csPCa ( < 0.0001). RSI alone was comparable with PI-RADS ( = 0.8). The combination of PI-RADS and RSI had an AUC of 0.85 (0.78, 0.91) and was superior to either PI-RADS or RSI alone ( < 0.05). Similar patterns were seen in the peripheral and transition zones.

CONCLUSIONS

RSI is a promising quantitative marker for patient-level csPCa detection, warranting a prospective study.

PATIENT SUMMARY

We evaluated a rapid, advanced prostate magnetic resonance imaging technique called restriction spectrum imaging to see whether it could give an automated score that predicted the presence of clinically significant prostate cancer. The automated score worked about as well as expert radiologists' interpretation. The combination of the radiologists' scores and automated score might be better than either alone.

摘要

背景

多参数磁共振成像(mpMRI)可提高临床显著前列腺癌(csPCa)的检测率,但主观的前列腺影像报告和数据系统(PI-RADS)以及定量表观扩散系数(ADC)并不一致。限制谱成像(RSI)是一种先进的扩散加权磁共振成像技术,可产生一种用于csPCa的定量成像生物标志物,称为RSI限制评分(RSI)。

目的

评估RSI在患者层面自动检测csPCa的效果。

设计、设置和参与者:我们回顾性研究了2017年至2019年期间在加利福尼亚大学圣地亚哥分校因疑似前列腺癌接受3T mpMRI和RSI(临床扫描仪上2分钟序列)检查且在MRI后180天内进行前列腺活检的所有患者(n = 151)。

干预措施

我们计算了前列腺内的最大RSI和最小ADC,并从病历中获取PI-RADS v2.1。

结果测量和统计分析

我们使用曲线下面积(AUC)比较了RSI、ADC和PI-RADS在最佳可用组织病理学(活检或前列腺切除术)上检测csPCa(分级组≥2)的性能,双侧α = 0.05。我们还探讨了PI-RADS和RSI的组合是否可能优于单独的PI-RADS,并在周边区和移行区进行了亚组分析。

结果和局限性

ADC、RSI和PI-RADS的AUC值分别为0.48(95%置信区间:0.39,0.58)、0.78(0.70,0.85)和0.77(0.70,0.84)。在患者层面检测csPCa时,RSI和PI-RADS均优于ADC(P < 0.0001)。单独的RSI与PI-RADS相当(P = 0.8)。PI-RADS和RSI的组合AUC为0.85(0.78,0.91),优于单独使用PI-RADS或RSI(P < 0.05)。在周边区和移行区也观察到类似模式。

结论

RSI是一种用于患者层面csPCa检测的有前景的定量标志物,值得进行前瞻性研究。

患者总结

我们评估了一种名为限制谱成像的快速、先进的前列腺磁共振成像技术,以确定它是否能给出一个预测临床显著前列腺癌存在的自动评分。该自动评分的效果与专家放射科医生的解读相当。放射科医生的评分与自动评分的组合可能比单独使用任何一种都更好。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2b0/9806706/4ef6622a2452/gr1.jpg

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