多参数磁共振成像测量的放射组学特征可预测前列腺癌侵袭性。
Radiomics Features Measured with Multiparametric Magnetic Resonance Imaging Predict Prostate Cancer Aggressiveness.
机构信息
Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
Departments of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York.
出版信息
J Urol. 2019 Sep;202(3):498-505. doi: 10.1097/JU.0000000000000272. Epub 2019 Aug 8.
PURPOSE
We sought to 1) assess the association of radiomics features based on multiparametric magnetic resonance imaging with histopathological Gleason score, gene signatures and gene expression levels in prostate cancer and 2) build machine learning models based on radiomics features to predict adverse histopathological scores and the Decipher® genomics metastasis risk score.
MATERIALS AND METHODS
We retrospectively analyzed the records of 64 patients with prostate cancer with a mean age of 64 years (range 41 to 76) who underwent magnetic resonance imaging between January 2016 and January 2017 before radical prostatectomy. A total of 226 magnetic resonance imaging radiomics features, including histogram and texture features in addition to lesion size and the PI-RADS™ (Prostate Imaging Reporting and Data System) score, were extracted from T2-weighted, apparent diffusion coefficient and diffusion kurtosis imaging maps. Radiomics features were correlated with the pathological Gleason score, 40 gene expression signatures, including Decipher, and 698 prostate cancer related gene expression levels. Cross-validated, lasso regularized, logistic regression machine learning models based on radiomics features were built and evaluated for the prediction of Gleason score 8 or greater and Decipher score 0.6 or greater.
RESULTS
A total of 14 radiomics features significantly correlated with the Gleason score (highest correlation r = 0.39, p = 0.001). A total of 31 texture and histogram features significantly correlated with 19 gene signatures, particularly with the PORTOS (Post-Operative Radiation Therapy Outcomes Score) signature (strongest correlation r = -0.481, p = 0.002). A total of 40 diffusion-weighted imaging features correlated significantly with 132 gene expression levels. Machine learning prediction models showed fair performance to predict a Gleason score of 8 or greater (AUC 0.72) and excellent performance to predict a Decipher score of 0.6 or greater (AUC 0.84).
CONCLUSIONS
Magnetic resonance imaging radiomics features are promising markers of prostate cancer aggressiveness on the histopathological and genomics levels.
目的
我们旨在 1）评估基于多参数磁共振成像的放射组学特征与前列腺癌组织学 Gleason 评分、基因特征和基因表达水平的相关性，2）构建基于放射组学特征的机器学习模型，以预测不良组织学评分和 Decipher®基因组转移风险评分。
材料与方法
我们回顾性分析了 2016 年 1 月至 2017 年 1 月期间 64 例接受根治性前列腺切除术前行磁共振成像检查的前列腺癌患者的记录，这些患者的平均年龄为 64 岁（41-76 岁）。从 T2 加权像、表观弥散系数和弥散峰度成像图中提取了 226 个磁共振成像放射组学特征，包括直方图和纹理特征以及病灶大小和 PI-RADS（前列腺成像报告和数据系统）评分。放射组学特征与病理 Gleason 评分、包括 Decipher 在内的 40 个基因表达特征和 698 个前列腺癌相关基因表达水平相关。基于放射组学特征构建并验证了交叉验证、套索正则化、逻辑回归机器学习模型，用于预测 Gleason 评分 8 分或更高和 Decipher 评分 0.6 或更高。
结果
共有 14 个放射组学特征与 Gleason 评分显著相关（最高相关性 r = 0.39，p = 0.001）。共有 31 个纹理和直方图特征与 19 个基因特征显著相关，特别是与 PORTOS（术后放射治疗结局评分）特征相关性最强（最强相关性 r = -0.481，p = 0.002）。共有 40 个弥散加权成像特征与 132 个基因表达水平显著相关。机器学习预测模型在预测 Gleason 评分 8 分或更高方面表现出良好的性能（AUC 0.72），在预测 Decipher 评分 0.6 或更高方面表现出优异的性能（AUC 0.84）。
结论
磁共振成像放射组学特征是预测前列腺癌组织学和基因组水平侵袭性的有前途的标志物。
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