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区域特异性逻辑回归模型可改善多参数磁共振成像对前列腺癌的分类。

Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI.

作者信息

Dikaios Nikolaos, Alkalbani Jokha, Abd-Alazeez Mohamed, Sidhu Harbir Singh, Kirkham Alex, Ahmed Hashim U, Emberton Mark, Freeman Alex, Halligan Steve, Taylor Stuart, Atkinson David, Punwani Shonit

机构信息

Centre for Medical Imaging, University College London, Level 3 East, 250 Euston Road, London, NW1 2PG, UK.

出版信息

Eur Radiol. 2015 Sep;25(9):2727-37. doi: 10.1007/s00330-015-3636-0. Epub 2015 Feb 14.

Abstract

OBJECTIVES

To assess the interchangeability of zone-specific (peripheral-zone (PZ) and transition-zone (TZ)) multiparametric-MRI (mp-MRI) logistic-regression (LR) models for classification of prostate cancer.

METHODS

Two hundred and thirty-one patients (70 TZ training-cohort; 76 PZ training-cohort; 85 TZ temporal validation-cohort) underwent mp-MRI and transperineal-template-prostate-mapping biopsy. PZ and TZ uni/multi-variate mp-MRI LR-models for classification of significant cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any grade with CCL ≥ 4 mm) were derived from the respective cohorts and validated within the same zone by leave-one-out analysis. Inter-zonal performance was tested by applying TZ models to the PZ training-cohort and vice-versa. Classification performance of TZ models for TZ cancer was further assessed in the TZ validation-cohort. ROC area-under-curve (ROC-AUC) analysis was used to compare models.

RESULTS

The univariate parameters with the best classification performance were the normalised T2 signal (T2nSI) within the TZ (ROC-AUC = 0.77) and normalized early contrast-enhanced T1 signal (DCE-nSI) within the PZ (ROC-AUC = 0.79). Performance was not significantly improved by bi-variate/tri-variate modelling. PZ models that contained DCE-nSI performed poorly in classification of TZ cancer. The TZ model based solely on maximum-enhancement poorly classified PZ cancer.

CONCLUSION

LR-models dependent on DCE-MRI parameters alone are not interchangable between prostatic zones; however, models based exclusively on T2 and/or ADC are more robust for inter-zonal application.

KEY POINTS

• The ADC and T2-nSI of benign/cancer PZ are higher than benign/cancer TZ. • DCE parameters are significantly different between benign PZ and TZ, but not between cancerous PZ and TZ. • Diagnostic models containing contrast enhancement parameters have reduced performance when applied across zones.

摘要

目的

评估前列腺癌分类中特定区域(外周区(PZ)和移行区(TZ))多参数磁共振成像(mp-MRI)逻辑回归(LR)模型的互换性。

方法

231例患者(70例TZ训练队列;76例PZ训练队列;85例TZ时间验证队列)接受了mp-MRI和经会阴模板前列腺映射活检。用于显著癌症(任何癌芯长度(CCL)且Gleason评分>3+3或任何分级且CCL≥4mm)分类的PZ和TZ单变量/多变量mp-MRI LR模型分别来自各自队列,并通过留一法分析在同一区域内进行验证。通过将TZ模型应用于PZ训练队列,反之亦然,来测试区域间性能。在TZ验证队列中进一步评估TZ模型对TZ癌症的分类性能。采用ROC曲线下面积(ROC-AUC)分析比较模型。

结果

分类性能最佳的单变量参数是TZ内的归一化T2信号(T2nSI)(ROC-AUC=0.77)和PZ内的归一化早期对比增强T1信号(DCE-nSI)(ROC-AUC=0.79)。双变量/三变量建模未显著提高性能。包含DCE-nSI的PZ模型在TZ癌症分类中表现不佳。仅基于最大增强的TZ模型对PZ癌症分类不佳。

结论

仅依赖DCE-MRI参数的LR模型在前列腺区域之间不可互换;然而,仅基于T2和/或ADC的模型在区域间应用时更稳健。

关键点

•良性/癌性PZ的ADC和T2-nSI高于良性/癌性TZ。•良性PZ和TZ之间的DCE参数有显著差异,但癌性PZ和TZ之间无显著差异。•包含对比增强参数的诊断模型在跨区域应用时性能降低。

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