A New Model to Predict Benign Histology in Residual Retroperitoneal Masses After Chemotherapy in Nonseminoma
A New Model to Predict Benign Histology in Residual Retroperitoneal Masses After Chemotherapy in Nonseminoma
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DOI：
10.1016/j.euf.2018.01.015
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发表时间：
2018-12-01
影响因子：
5.4
通讯作者：
Hamilton, Robert J.
中科院分区：
文献类型：
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作者：
Leao, Ricardo;Nayan, Madhur;Hamilton, Robert J.
背景资料：化疗后腹膜后淋巴结清扫术（pcRPLND）适用于血清肿瘤标志物正常或稳定且残留腹膜后病变>1 cm的睾丸癌患者。在预测化疗后残留肿块（pcRM）的组织学，这可能会导致不必要的surgical.Objective的挑战仍然存在：开发一个准确的模型来预测pcRM组织学在患者与非恶性生殖细胞肿瘤（NSGCTs）。设计，设置，和参与者：回顾性分析335例患者接受pcRPLND转移性NSGCTs开发一个模型来预测良性组织学在腹膜后pcRM。我们的模型进行了比较与他人和外部validated.Intervention：化疗和pcRPLND.Outcome测量和统计分析：多变量logistic回归，以评估良性组织学的存在，分数多项式，允许连续变量和结果之间的非线性关联。最后的玛格丽特公主模型（PMM）的基础上使用的变量，可靠性和判别能力，预测良性pcRM。结果和限制：PMM包括睾丸切除术中的畸胎瘤的存在，化疗前甲胎蛋白，化疗前的质量大小，化疗过程中质量大小的变化。模型特异性为99.3%。与Vergouwe等人的模型相比，PMM具有显著更好的准确性（C统计量为0.843 vs 0.783）。PMM适当地确定了大量可以安全避免pcRPLND的患者（13.9% vs 0%）。在外部队列中验证，该模型保留了高区分度（C统计量为0.88和0.80）。需要更大的和前瞻性的研究，以进一步验证这个model.Conclusions：我们的临床模型，外部验证，表现出更好的判别能力，在预测pcRM组织学相比，与其他模型。更高的准确性和减少变量的数量，使这成为一种新颖的和有吸引力的模型，用于患者咨询和治疗strategies.Patient摘要：玛格丽特公主模型准确预测化疗后良性组织学。这些结果可能通过避免不必要的腹膜后淋巴结清扫产生临床影响，从而改变晚期睾丸癌治疗的模式。(c)2018年欧洲泌尿外科协会。Elsevier B.V.出版，保留所有权利。
Background: Postchemotherapy retroperitoneal lymph node dissection (pcRPLND) is indicated in testicular cancer patients with normalised or plateaued serum tumour markers and residual retroperitoneal lesions >1 cm. Challenges remain in predicting postchemotherapy residual mass (pcRM) histology, which may lead to unnecessary surgery.Objective: To develop an accurate model to predict pcRM histology in patients with nonseminomatous germ cell tumours (NSGCTs).Design, setting, and participants: A retrospective review of 335 patients undergoing pcRPLND for metastatic NSGCTs to develop a model to predict benign histology in retroperitoneal pcRM. Our model was compared with others and externally validated.Intervention: Chemotherapy and pcRPLND.Outcome measurements and statistical analysis: Multivariable logistic regression to evaluate the presence of benign histology, and fractional polynomials to allow for a nonlinear association between continuous variables and the outcome. The final Princess Margaret model (PMM) was selected based on the number of variables used, reliability, and discriminative capacity to predict benign pcRM.Results and limitations: PMM included the presence of teratoma in the orchiectomy, prechemotherapy alpha-fetoprotein, prechemotherapy mass size, and change in mass size during chemotherapy. Model specificity was 99.3%. Compared with Vergouwe et al's model, PMM had significantly better accuracy (C statistic 0.843 vs 0.783). PMM appropriately identified a larger number of patients for whom pcRPLND can safely be avoided (13.9% vs 0%). Validated in external cohorts, the model retained high discrimination (C statistic 0.88 and 0.80). Larger and prospective studies are needed to further validate this model.Conclusions: Our clinical model, externally validated, showed improved discriminative ability in predicting pcRM histology when compared with other models. The higher accuracy and reduced number of variables make this a novel and appealing model to use for patient counselling and treatment strategies.Patient summary: Princess Margaret model accurately predicted postchemotherapy benign histology. These results might have clinical impact by avoiding unnecessary retroperitoneal lymph node dissection and consequently changing the paradigm of advanced testicular cancer treatment. (c) 2018 European Association of Urology. Published by Elsevier B.V. All rights reserved.