Unit of Urology/Division of Oncology, Urological Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy.
Department of Urology, Saint Jean Languedoc/La Croix du Sud Hospital, Toulouse, France.
Eur Urol. 2019 Mar;75(3):506-514. doi: 10.1016/j.eururo.2018.10.012. Epub 2018 Oct 17.
BACKGROUND: Available models for predicting lymph node invasion (LNI) in prostate cancer (PCa) patients undergoing radical prostatectomy (RP) might not be applicable to men diagnosed via magnetic resonance imaging (MRI)-targeted biopsies. OBJECTIVE: To assess the accuracy of available tools to predict LNI and to develop a novel model for men diagnosed via MRI-targeted biopsies. DESIGN, SETTING, AND PARTICIPANTS: A total of 497 patients diagnosed via MRI-targeted biopsies and treated with RP and extended pelvic lymph node dissection (ePLND) at five institutions were retrospectively identified. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSES: Three available models predicting LNI were evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analyses. A nomogram predicting LNI was developed and internally validated. RESULTS AND LIMITATIONS: Overall, 62 patients (12.5%) had LNI. The median number of nodes removed was 15. The AUC for the Briganti 2012, Briganti 2017, and MSKCC nomograms was 82%, 82%, and 81%, respectively, and their calibration characteristics were suboptimal. A model including PSA, clinical stage and maximum diameter of the index lesion on multiparametric MRI (mpMRI), grade group on targeted biopsy, and the presence of clinically significant PCa on concomitant systematic biopsy had an AUC of 86% and represented the basis for a coefficient-based nomogram. This tool exhibited a higher AUC and higher net benefit compared to available models developed using standard biopsies. Using a cutoff of 7%, 244 ePLNDs (57%) would be spared and a lower number of LNIs would be missed compared to available nomograms (1.6% vs 4.6% vs 4.5% vs 4.2% for the new nomogram vs Briganti 2012 vs Briganti 2017 vs MSKCC). CONCLUSIONS: Available models predicting LNI are characterized by suboptimal accuracy and clinical net benefit for patients diagnosed via MRI-targeted biopsies. A novel nomogram including mpMRI and MRI-targeted biopsy data should be used to identify candidates for ePLND in this setting. PATIENT SUMMARY: We developed the first nomogram to predict lymph node invasion (LNI) in prostate cancer patients diagnosed via magnetic resonance imaging-targeted biopsy undergoing radical prostatectomy. Adoption of this model to identify candidates for extended pelvic lymph node dissection could avoid up to 60% of these procedures at the cost of missing only 1.6% patients with LNI.
背景：现有的预测接受根治性前列腺切除术（RP）的前列腺癌（PCa）患者淋巴结侵犯（LNI）的模型可能不适用于通过磁共振成像（MRI）靶向活检诊断的患者。
目的：评估现有工具预测 LNI 的准确性，并为通过 MRI 靶向活检诊断的患者开发一种新模型。
设计、设置和参与者：总共回顾性地确定了 5 家机构中通过 MRI 靶向活检诊断并接受 RP 和扩展盆腔淋巴结清扫术（ePLND）治疗的 497 名患者。
结局测量和统计学分析：使用接受者操作特征曲线（ROC）下面积（AUC）、校准图和决策曲线分析评估了三种预测 LNI 的现有模型。开发并内部验证了一种预测 LNI 的列线图。
结果和局限性：总体而言，62 名患者（12.5%）有 LNI。切除的淋巴结中位数为 15 个。Briganti 2012、Briganti 2017 和 MSKCC 列线图的 AUC 分别为 82%、82%和 81%，其校准特征不理想。一个包含 PSA、临床分期和多参数 MRI（mpMRI）上指数病变的最大直径、靶向活检的分级组以及同时进行的系统活检中存在临床显著 PCa 的模型的 AUC 为 86%，并以此为基础建立了基于系数的列线图。与使用标准活检建立的现有模型相比，该工具具有更高的 AUC 和更高的净获益。使用 7%的截断值，与现有列线图相比（新列线图为 1.6%，而 Briganti 2012 为 4.6%，Briganti 2017 为 4.5%，MSKCC 为 4.2%），可以节省 244 例 ePLND（57%），并且漏诊的 LNI 数量更少。
结论：现有的预测 LNI 的模型在通过 MRI 靶向活检诊断的患者中表现出亚优的准确性和临床净获益。应该使用包括 mpMRI 和 MRI 靶向活检数据的新列线图来确定在这种情况下进行 ePLND 的候选者。
患者总结：我们开发了第一个预测通过磁共振成像靶向活检诊断的前列腺癌患者接受根治性前列腺切除术后淋巴结侵犯（LNI）的列线图。采用该模型识别接受扩展盆腔淋巴结清扫术的候选者，可避免多达 60%的此类手术，而仅漏诊 1.6%的 LNI 患者。
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