Preoperative nomograms incorporating magnetic resonance imaging and spectroscopy for prediction of insignificant prostate cancer.
Preoperative nomograms incorporating magnetic resonance imaging and spectroscopy for prediction of insignificant prostate cancer.
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DOI：
10.1111/j.1464-410x.2011.10612.x
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发表时间：
2012-05
影响因子：
4.5
通讯作者：
Kattan MW
中科院分区：
文献类型：
--
作者：
Shukla-Dave A;Hricak H;Akin O;Yu C;Zakian KL;Udo K;Scardino PT;Eastham J;Kattan MW
·验证先前发表的用于预测不显著前列腺癌（PCa）的列线图，其结合临床数据、活检芯阳性百分比（%BC+）和磁共振成像（MRI）或MRI/MR光谱成像（MRSI）结果。·我们还设计了新的诺模图模型，将磁共振结果和临床数据结合起来，而没有详细的活检数据。·预测无意义PCa的列线图可以帮助医生为临床低风险疾病患者提供咨询，这些患者正在积极监测和确定性治疗之间进行选择。·总共有181名低风险PCa患者（临床分期T1 c-T2 a，前列腺特异性抗原水平< 10 ng/mL，活检Gleason评分为6）在手术前进行了MRI/MRSI。·对于MRI和MRI/MRSI，由两名放射科医生在0（肯定不显著）至3（肯定显著PCa）的量表上前瞻性和独立地记录不显著PCa的概率。·根据手术病理学定义不显著的PCa。·有四个模型结合了MRI或MRI/MRSI和具有和不具有%BC+的临床数据，将其与不具有%BC的基础临床模型和具有%BC+的更全面的临床模型进行比较。·使用受试者-操作者特征曲线下的面积评估预测准确性。·在病理学检查中，27%的患者具有不显著的PCa，56.4%的患者的Gleason评分升高。·对于两个读片者，所有磁共振模型的表现都显著优于基础临床模型（所有模型P ≤ 0.05），并且与更全面的临床模型相似。·验证了结合磁共振数据、临床数据和用于预测不显著PCa概率的%BC+的现有模型。·所有包含MR的模型的表现均显著优于基础临床模型。
• To validate previously published nomograms for predicting insignificant prostate cancer (PCa) that incorporate clinical data, percentage of biopsy cores positive (%BC+) and magnetic resonance imaging (MRI) or MRI/MR spectroscopic imaging (MRSI) results. • We also designed new nomogram models incorporating magnetic resonance results and clinical data without detailed biopsy data. • Nomograms for predicting insignificant PCa can help physicians counsel patients with clinically low-risk disease who are choosing between active surveillance and definitive therapy. • In total, 181 low-risk PCa patients (clinical stage T1c–T2a, prostate-specific antigen level < 10 ng/mL, biopsy Gleason score of 6) had MRI/MRSI before surgery. • For MRI and MRI/MRSI, the probability of insignificant PCa was recorded prospectively and independently by two radiologists on a scale from 0 (definitely insignificant) to 3 (definitely significant PCa). • Insignificant PCa was defined on surgical pathology. • There were four models incorporating MRI or MRI/MRSI and clinical data with and without %BC+ that were compared with a base clinical model without %BC and a more comprehensive clinical model with %BC+. • Prediction accuracy was assessed using areas under receiver–operator characteristic curves. • At pathology, 27% of patients had insignificant PCa, and the Gleason score was upgraded in 56.4% of patients. • For both readers, all magnetic resonance models performed significantly better than the base clinical model (P ≤ 0.05 for all) and similarly to the more comprehensive clinical model. • Existing models incorporating magnetic resonance data, clinical data and %BC+ for predicting the probability of insignificant PCa were validated. • All MR-inclusive models performed significantly better than the base clinical model.