Clinical evaluation of a computer-aided diagnosis system for determining cancer aggressiveness in prostate MRI.
Clinical evaluation of a computer-aided diagnosis system for determining cancer aggressiveness in prostate MRI.
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DOI：
10.1007/s00330-015-3743-y
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发表时间：
2015-11
影响因子：
5.9
通讯作者：
Huisman HJ
中科院分区：
文献类型：
--
作者：
Litjens GJ;Barentsz JO;Karssemeijer N;Huisman HJ
目的探讨计算机辅助诊断(CAD)对PIRADS报告的诊断准确性和肿瘤侵袭性评估的附加值。包括连续130例患者的多参数磁共振成像和MR引导活检的组织病理学结果。所有病例均前瞻性报道了PIRADS，并对报告的病变进行了CAD分析。Logistic回归将CAD预测和放射科医生PIRADS评分合并为一个组合评分。采用受试者操作特征(ROC)分析和Spearman‘s相关系数评价诊断准确率及与肿瘤分级的相关性。评估是为了区分良性病变和癌症，以及区分惰性和侵袭性病变。总共有141个病灶(107名患者)进入最终分析。综合评分的ROC曲线下面积高于放射科医生的PIRADS评分(良性肿瘤分别为0.013.88和0.81，p = 分别为0.88和0.78，p <； 均为0.01)。联合评分与肿瘤分级(0.69，p = 0.0014)的相关性明显强于单独的CAD系统或放射科医生(0.54和0.58)。将CAD预测和PIRADS结合成一个组合评分，有可能提高诊断的准确性。此外，这样的综合评分与癌症分级有很强的相关性。·计算机辅助诊断帮助放射科医生在前列腺癌MRI中区分良性病变和癌症。·将PIRADS和计算机辅助诊断结合起来，提高了对惰性癌症和侵袭性癌症的区分。·将计算机辅助诊断添加到PIRADS中，增加了与癌症级别的相关系数。
To investigate the added value of computer-aided diagnosis (CAD) on the diagnostic accuracy of PIRADS reporting and the assessment of cancer aggressiveness. Multi-parametric MRI and histopathological outcome of MR-guided biopsies of a consecutive set of 130 patients were included. All cases were prospectively PIRADS reported and the reported lesions underwent CAD analysis. Logistic regression combined the CAD prediction and radiologist PIRADS score into a combination score. Receiver-operating characteristic (ROC) analysis and Spearman’s correlation coefficient were used to assess the diagnostic accuracy and correlation to cancer grade. Evaluation was performed for discriminating benign lesions from cancer and for discriminating indolent from aggressive lesions. In total 141 lesions (107 patients) were included for final analysis. The area-under-the-ROC-curve of the combination score was higher than for the PIRADS score of the radiologist (benign vs. cancer, 0.88 vs. 0.81, p = 0.013 and indolent vs. aggressive, 0.88 vs. 0.78, p < 0.01). The combination score correlated significantly stronger with cancer grade (0.69, p = 0.0014) than the individual CAD system or radiologist (0.54 and 0.58). Combining CAD prediction and PIRADS into a combination score has the potential to improve diagnostic accuracy. Furthermore, such a combination score has a strong correlation with cancer grade. • Computer-aided diagnosis helps radiologists discriminate benign findings from cancer in prostate MRI. • Combining PIRADS and computer-aided diagnosis improves differentiation between indolent and aggressive cancer. • Adding computer-aided diagnosis to PIRADS increases the correlation coefficient with respect to cancer grade.