To evaluate the two newly established nomograms for predicting lymph node metastasis in penile cancer based on the clinical data on a large cohort of patients.
We retrospectively studied the clinical data on 93 patients with penile cancer treated in the Center for Tumor Prevention and Treatment. Using the two recently established nomograms (Bhagat nomogram and Zhu nomogram), we predicted lymph node metastasis in the patients, analyzed the differences between prediction and the results of postoperative pathology, and compared the accuracy of prediction between the two nomograms with the receiver operating characteristic (ROC) curve and the area under the curve (AUC).
The median age of the patients was 55 (27－82) years. Positive lymph nodes were found in 31 cases (33.3%) postoperatively and in 9 (21.9%) of the 41 clinically negative cases. The AUC of the Bhagat nomogram was 0.739 and that of Zhu nomogram was 0.808, both of which were similar to the prediction accuracy of internal verification and manifested a medium predictive ability.
The newly established Bhagat and Zhu nomograms can be used for predicting lymph node metastasis in penile cancer, but with a low precision, and therefore cannot be relied exclusively for the option of inguinal lymphadenectomy.
目的： 通过阴茎癌患者资料来独立地外部验证新近建立的2个诺模图，并阐述其中的联系与不足。方法： 回顾并分析93例阴茎癌患者资料，应用新近建立的2个诺模图（Bhagat诺模图，Zhu诺模图）对阴茎癌患者淋巴结转移进行预测，比较预测结果与实际术后病理结果之间的差异。为比较各自模型的预测准确性，选择了临床中常用的受试者工作特征曲线 （ROC）下面积（AUC）来分析。结果： 93例患者年龄为55（27~82）岁，术后发现阳性淋巴结比例为33.3%，其中41例患者为临床阴性淋巴结，术后发现阳性淋巴结的比例为21.9%。Bhagat诺模图的AUC为0.739，Zhu诺模图的AUC为0.808，均与其内部验证的预测准确性相近，表现出中等的预测能力。结论： 预测阴茎癌淋巴结转移的Bhagat和Zhu诺模图均表现出一定的预测能力，但离高准确性仍有一定的差距，临床上单纯依赖诺模图而决定该患者是否需行腹股沟淋巴结清扫术仍需谨慎。
Zhonghua nan ke xue = National journal of andrology. 2018 May [Epub]
Jing Li, Bin Wang, Shun-Sheng Zheng, Fang-Jian Zhou, Jian-An Yang, Dao-Zhang Yuan, Yan-Fei Chen
Department of Urological Tumors, Tumor Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510095, China., Department of Urological Tumors, Center for Tumor Prevention and Treatment, Sun Yat-sen University, Guangzhou, Guangdong 510060, China.