使用双参数前列腺MRI、影像组学和激肽释放酶检测前列腺癌：对临床怀疑前列腺癌男性的回顾性多中心研究
Detection of Prostate Cancer Using Biparametric Prostate MRI, Radiomics, and Kallikreins: A Retrospective Multicenter Study of Men With a Clinical Suspicion of Prostate Cancer.
作者信息
Montoya Perez Ileana, Merisaari Harri, Jambor Ivan, Ettala Otto, Taimen Pekka, Knaapila Juha, Kekki Henna, Khan Ferdhos L, Syrjälä Elise, Steiner Aida, Syvänen Kari T, Verho Janne, Seppänen Marjo, Rannikko Antti, Riikonen Jarno, Mirtti Tuomas, Lamminen Tarja, Saunavaara Jani, Falagario Ugo, Martini Alberto, Pahikkala Tapio, Pettersson Kim, Boström Peter J, Aronen Hannu J
机构信息
Department of Diagnostic Radiology, University of Turku, Turku, Finland.
Department of Computing, University of Turku, Turku, Finland.
出版信息
J Magn Reson Imaging. 2022 Feb;55(2):465-477. doi: 10.1002/jmri.27811. Epub 2021 Jul 5.
BACKGROUND
Accurate detection of clinically significant prostate cancer (csPCa), Gleason Grade Group ≥ 2, remains a challenge. Prostate MRI radiomics and blood kallikreins have been proposed as tools to improve the performance of biparametric MRI (bpMRI).
PURPOSE
To develop and validate radiomics and kallikrein models for the detection of csPCa.
STUDY TYPE
Retrospective.
POPULATION
A total of 543 men with a clinical suspicion of csPCa, 411 (76%, 411/543) had kallikreins available and 360 (88%, 360/411) did not take 5-alpha-reductase inhibitors. Two data splits into training, validation (split 1: single center, n = 72; split 2: random 50% of pooled datasets from all four centers), and testing (split 1: 4 centers, n = 288; split 2: remaining 50%) were evaluated.
FIELD STRENGTH/SEQUENCE: A 3 T/1.5 T, TSE T2-weighted imaging, 3x SE DWI.
ASSESSMENT
In total, 20,363 radiomic features calculated from manually delineated whole gland (WG) and bpMRI suspicion lesion masks were evaluated in addition to clinical parameters, prostate-specific antigen, four kallikreins, MRI-based qualitative (PI-RADSv2.1/IMPROD bpMRI Likert) scores.
STATISTICAL TESTS
For the detection of csPCa, area under receiver operating curve (AUC) was calculated using the DeLong's method. A multivariate analysis was conducted to determine the predictive power of combining variables. The values of P-value < 0.05 were considered significant.
RESULTS
The highest prediction performance was achieved by IMPROD bpMRI Likert and PI-RADSv2.1 score with AUC = 0.85 and 0.85 in split 1, 0.85 and 0.83 in split 2, respectively. bpMRI WG and/or kallikreins demonstrated AUCs ranging from 0.62 to 0.73 in split 1 and from 0.68 to 0.76 in split 2. AUC of bpMRI lesion-derived radiomics model was not statistically different to IMPROD bpMRI Likert score (split 1: AUC = 0.83, P-value = 0.306; split 2: AUC = 0.83, P-value = 0.488).
DATA CONCLUSION
The use of radiomics and kallikreins failed to outperform PI-RADSv2.1/IMPROD bpMRI Likert and their combination did not lead to further performance gains.
LEVEL OF EVIDENCE
1 TECHNICAL EFFICACY: Stage 2.
背景
准确检测具有临床意义的前列腺癌（csPCa），即Gleason分级组≥2，仍然是一项挑战。前列腺MRI放射组学和血液激肽释放酶已被提议作为提高双参数MRI（bpMRI）性能的工具。
目的
开发并验证用于检测csPCa的放射组学和激肽释放酶模型。
研究类型
回顾性研究。
研究对象
共有543名临床怀疑患有csPCa的男性，其中411名（76%，411/543）有激肽释放酶数据，360名（88%，360/411）未服用5-α还原酶抑制剂。对两个数据分割进行了评估，分别用于训练、验证（分割1：单中心，n = 72；分割2：来自所有四个中心的合并数据集的随机50%）和测试（分割1：4个中心，n = 288；分割2：其余50%）。
场强/序列：3T/1.5T，快速自旋回波T2加权成像，3x自旋回波扩散加权成像。
评估
除了临床参数、前列腺特异性抗原、四种激肽释放酶、基于MRI的定性（PI-RADSv2.1/IMPROD bpMRI李克特）评分外，还评估了从手动勾勒的整个腺体（WG）和bpMRI可疑病变掩码计算出的总共20363个放射组学特征。
统计检验
对于csPCa的检测，使用DeLong方法计算受试者操作特征曲线下面积（AUC）。进行多变量分析以确定组合变量的预测能力。P值<0.05被认为具有统计学意义。
结果
在分割1中，IMPROD bpMRI李克特评分和PI-RADSv2.1评分实现了最高的预测性能，AUC分别为0.85和0.85；在分割2中，AUC分别为0.85和0.83。bpMRI WG和/或激肽释放酶在分割1中的AUC范围为0.62至0.73，在分割2中的AUC范围为0.68至0.76。bpMRI病变衍生的放射组学模型的AUC与IMPROD bpMRI李克特评分在统计学上无差异（分割1：AUC = 0.83，P值 = 0.306；分割2：AUC = 0.83，P值 = 0.488）。
数据结论
放射组学和激肽释放酶的使用未能优于PI-RADSv2.1/IMPROD bpMRI李克特评分，且它们的组合并未带来进一步的性能提升。
证据水平
1 技术效能：2级
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