预测切除边缘阳性前列腺癌患者局部复发的形态图

K. Nyushko, V. M. Perepukhov, B. Alekseev
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引用次数: 0

摘要

背景。根治性前列腺切除术后切缘阳性的前列腺癌患者有局部复发的危险。为了选择最佳的管理策略,应该仔细评估这种风险。利用接受手术的患者的数据,开发一种预测切除边缘阳性患者局部复发风险的nomogram方法。材料和方法。对2255例具有临床意义的局部和局部晚期前列腺癌患者的手术标本进行常规病理形态学检查,发现364例切缘阳性。统计分析使我们能够确定最重要的预后因素。使用选定的术前因素和数学模型,我们创建了一个nomogram来预测切除边缘阳性患者的局部复发。我们的nomogram准确度为93% (ROC曲线下面积(AUC) 0.9392;P <0.005),敏感性0.99438,特异性0.94545。最重要的预后因素包括活检阳性标本的比例、Gleason评分(国际泌尿病理学会(ISUP)在常规病理形态学检查中估计的分级)、阳性切除边缘的存在和长度。我们的数学模型和基于它的态图对于预测局部复发具有很高的准确性,因此可以用于选择最佳的管理策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nomograms for predicting local recurrence in prostate cancer patients with a positive resection margin
Background. Prostate cancer patients with a positive resection margin after radical prostatectomy are at risk of developing local recurrence. This risk should be carefully estimated in order to choose an optimal management strategy.Aim. To develop a nomogram to predict the risk of local recurrence in patients with a positive resection margin using the data on patients who have undergone surgery.Materials and methods. Routine pathomorphological examination of surgical specimens from 2255 patients with clinically significant local and locally advanced prostate cancer revealed 364 cases of positive resection margin. Statistical analysis allowed us to identify the most significant prognostic factors. Using selected preoperative factors and a mathematical model, we created a nomogram to predict local recurrence in patients with a positive resection margin.Results. Our nomogram had an accuracy of 93% (area under the ROC curve (AUC) 0.9392; p <0.005), sensitivity of 0.99438, and specificity of 0.94545. The most significant prognostic factors included proportion of positive biopsy specimens, Gleason score (International Society of Urological Pathology (ISUP) grade estimated at routine pathomorphological examination), and presence and length of positive resection margin.Conclusion. Our mathematical model and the nomogram based on it are highly accurate for predicting local recurrence and can therefore be used for choosing an optimal management strategy.
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