首页> 外文期刊>Annals of surgical oncology >Prediction of Non-sentinel Node Status in Patients with Melanoma and Positive Sentinel Node Biopsy: An Italian Melanoma Intergroup (IMI) Study
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Prediction of Non-sentinel Node Status in Patients with Melanoma and Positive Sentinel Node Biopsy: An Italian Melanoma Intergroup (IMI) Study

机译:黑色素瘤患者非哨兵节点状态的预测和正哨节点活组织检查:意大利黑色素瘤族群(IMI)研究

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Abstract Background and Purpose Approximately 20% of melanoma patients harbor metastases in non-sentinel nodes (NSNs) after a positive sentinel node biopsy (SNB), and recent evidence questions the therapeutic benefit of completion lymph node dissection (CLND). We built a nomogram for prediction of NSN status in melanoma patients with positive SNB. Methods Data on anthropometric and clinicopathological features of patients with cutaneous melanoma who underwent CLND after a positive SNB were collected from nine Italian centers. Multivariate logistic regression was utilized to identify predictors of NSN status in a training set, while model efficiency was validated in a validation set. Results Data were available for 1220 patients treated from 2000 through 2016. In the training set ( n ?=?810), the risk of NSN involvement was higher when (1) the primary melanoma is thicker or (2) sited in the trunk/head and neck; (3) fewer nodes are excised and (4) more nodes are involved; and (5) the lymph node metastasis is larger or (6) is deeply located. The model showed high discrimination (area under the receiver operating characteristic curve 0.74, 95% confidence interval [CI] 0.70–0.79) and calibration (Brier score 0.16, 95% CI 0.15–0.17) performance in the validation set ( n ?=?410). The nomogram including these six clinicopathological variables performed significantly better than five other previously published models in terms of both discrimination and calibration. Conclusions Our nomogram could be useful for follow-up personalization in clinical practice, and for patient risk stratification while conducting clinical trials or analyzing their results.
机译:摘要背景和目的大约20%的黑色素瘤患者在正哨节点活检(SNB)后非哨兵节点(NSN)中的转移,以及最近的证据询问完成淋巴结解剖(CLND)的治疗益处。我们建立了一种载体,用于预测黑色素瘤患者阳性SNB的NSN状态。方法从九个意大利中心收集阳性SNB后,在阳性SNB后接受CLND的皮肤黑素瘤患者的人体测量和临床病理特征的数据。利用多变量逻辑回归来识别训练集中NSN状态的预测因子,而在验证集中验证了模型效率。结果可用于从2000到2016年治疗的1220名患者提供数据。在训练组(n?= 810)中,当(1)初级黑素瘤更厚或(2)占主干/时头部和颈部; (3)切除较少的节点,(4)涉及更多节点; (5)淋巴结转移较大或(6)深受置位。该模型显示出高识别(接收器下的区域,操作特性曲线0.74,95%置信区间[CI] 0.70-0.79)和校准(BRIER得分0.16,95%CI 0.15-0.17)在验证集中的性能(n?=? 410)。在歧视和校准方面,包括这六种临床病理变量的墨迹显着比其他五个出版的模型显着优于五个。结论我们的NOM图可能对临床实践中的后续个性化以及患者风险分层有用,同时进行临床试验或分析结果。

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