Development and Internal Validation of a Novel Model to Identify the Candidates for Extended Pelvic Lymph Node Dissection in Prostate Cancer

列线图 医学 前列腺癌 前列腺切除术 逻辑回归 接收机工作特性 淋巴结 阶段(地层学) 解剖(医学) 活检 切断 泌尿科 前列腺特异性抗原 放射科 癌症 肿瘤科 内科学 古生物学 物理 生物 量子力学
作者
Giorgio Gandaglia,Nicola Fossati,Emanuele Zaffuto,Marco Bandini,Paolo Dell’Oglio,Carlo Andrea Bravi,Giuseppe Fallara,Francesco Pellegrino,Luigi Nocera,Pierre I. Karakiewicz,Zhe Tian,Massimo Freschi,Rodolfo Montironi,Francesco Montorsi,Alberto Briganti
出处
期刊:European Urology [Elsevier BV]
卷期号:72 (4): 632-640 被引量:203
标识
DOI:10.1016/j.eururo.2017.03.049
摘要

Preoperative assessment of the risk of lymph node invasion (LNI) is mandatory to identify prostate cancer (PCa) patients who should receive an extended pelvic lymph node dissection (ePLND). To update a nomogram predicting LNI in contemporary PCa patients with detailed biopsy reports. Overall, 681 patients with detailed biopsy information, evaluated by a high-volume uropathologist, treated with radical prostatectomy and ePLND between 2011 and 2016 were identified. A multivariable logistic regression model predicting LNI was fitted and represented the basis for a coefficient-based nomogram. The model was evaluated using the receiver operating characteristic-derived area under the curve (AUC), calibration plot, and decision-curve analyses (DCAs). The median number of nodes removed was 16. Overall, 79 (12%) patients had LNI. A multivariable model that included prostate-specific antigen, clinical stage, biopsy Gleason grade group, percentage of cores with highest-grade PCa, and percentage of cores with lower-grade disease represented the basis for the nomogram. After cross validation, the predictive accuracy of these predictors in our cohort was 90.8% and the DCA demonstrated improved risk prediction against threshold probabilities of LNI ≤20%. Using a cutoff of 7%, 471 (69%) ePLNDs would be spared and LNI would be missed in seven (1.5%) patients. As compared with the Briganti and Memorial Sloan Kettering Cancer Center nomograms, the novel model showed higher AUC (90.8% vs 89.5% vs 89.5%), better calibration characteristics, and a higher net benefit at DCA. An ePLND should be avoided in patients with detailed biopsy information and a risk of nodal involvement below 7%, in order to spare approximately 70% ePLNDs at the cost of missing only 1.5% LNIs. We developed a novel nomogram to predict lymph node invasion (LNI) in patients with clinically localized prostate cancer based on detailed biopsy reports. A lymph node dissection exclusively in men with a risk of LNI > 7% according to this model would significantly reduce the number of unnecessary pelvic nodal dissections with a risk of missing only 1.5% of patients with LNI.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
乐观姒发布了新的文献求助10
刚刚
anyilin发布了新的文献求助10
刚刚
aniu完成签到,获得积分10
1秒前
所所应助MS903采纳,获得10
1秒前
罗成完成签到,获得积分10
1秒前
2秒前
脑洞疼应助正直的笑蓝采纳,获得10
2秒前
3秒前
爱老婆发布了新的文献求助10
3秒前
AzA发布了新的文献求助20
3秒前
zhang完成签到,获得积分10
3秒前
Zz发布了新的文献求助10
4秒前
5秒前
欢喜招牌完成签到,获得积分10
5秒前
小马甲应助芋头采纳,获得10
5秒前
大渡河发布了新的文献求助10
5秒前
麻果发布了新的文献求助10
5秒前
流年亦梦完成签到 ,获得积分10
6秒前
6秒前
Akim应助科研通管家采纳,获得10
7秒前
深情安青应助科研通管家采纳,获得10
7秒前
风清扬应助科研通管家采纳,获得10
7秒前
莫名完成签到,获得积分10
7秒前
科目三应助科研通管家采纳,获得10
7秒前
CipherSage应助科研通管家采纳,获得10
7秒前
星辰大海应助科研通管家采纳,获得10
7秒前
852应助科研通管家采纳,获得30
7秒前
7秒前
7秒前
7秒前
8秒前
8秒前
自觉白桃应助科研通管家采纳,获得10
8秒前
8秒前
大模型应助科研通管家采纳,获得10
8秒前
曾梦发布了新的文献求助10
8秒前
9秒前
小马甲应助稳重的小刺猬采纳,获得10
9秒前
9秒前
10秒前
高分求助中
【请各位用户详细阅读此贴后再求助】科研通的精品贴汇总(请勿应助) 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 1000
Global Eyelash Assessment scale (GEA) 1000
Maritime Applications of Prolonged Casualty Care: Drowning and Hypothermia on an Amphibious Warship 500
Comparison analysis of Apple face ID in iPad Pro 13” with first use of metasurfaces for diffraction vs. iPhone 16 Pro 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4050659
求助须知:如何正确求助?哪些是违规求助? 3588955
关于积分的说明 11405008
捐赠科研通 3315278
什么是DOI,文献DOI怎么找? 1823634
邀请新用户注册赠送积分活动 895517
科研通“疑难数据库(出版商)”最低求助积分说明 816871