Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada.
Division of Urology, Western University and London Health Sciences Centre, London, ON, Canada.
Eur Urol Focus. 2018 Dec;4(6):995-1001. doi: 10.1016/j.euf.2018.01.015. Epub 2018 Feb 7.
Postchemotherapy retroperitoneal lymph node dissection (pcRPLND) is indicated in testicular cancer patients with normalised or plateaued serum tumour markers and residual retroperitoneal lesions >1cm. Challenges remain in predicting postchemotherapy residual mass (pcRM) histology, which may lead to unnecessary surgery.
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.
Chemotherapy and pcRPLND.
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.
PMM included the presence of teratoma in the orchiectomy, prechemotherapy α-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.
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.
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.
对于血清肿瘤标志物正常或平台化且腹膜后残留病变>1cm 的睾丸癌患者，建议进行化疗后腹膜后淋巴结清扫术（pcRPLND）。然而，预测化疗后残留肿块（pcRM）的组织学仍然存在挑战，这可能导致不必要的手术。
建立一种准确的模型，以预测非精原细胞瘤生殖细胞肿瘤（NSGCTs）患者的 pcRM 组织学。
设计、地点和参与者：对 335 例接受 pcRPLND 治疗转移性 NSGCTs 的患者进行回顾性分析，以建立预测腹膜后 pcRM 良性组织学的模型。我们的模型与其他模型进行了比较并进行了外部验证。
化疗和 pcRPLND。
多变量逻辑回归评估良性组织学的存在，分数多项式允许连续变量与结局之间存在非线性关联。最终的玛格丽特公主医院模型（PMM）是基于使用的变量数量、可靠性和预测良性 pcRM 的区分能力来选择的。
PMM 包括睾丸切除术时存在畸胎瘤、化疗前甲胎蛋白、化疗前肿块大小以及化疗过程中肿块大小的变化。模型的特异性为 99.3%。与 Vergouwe 等人的模型相比，PMM 的准确性显著提高（C 统计量 0.843 对 0.783）。PMM 适当识别出更多可以安全避免 pcRPLND 的患者（13.9%对 0%）。在外部队列中验证时，该模型仍然具有较高的区分能力（C 统计量分别为 0.88 和 0.80）。需要更大规模和前瞻性研究来进一步验证该模型。
我们的临床模型经过外部验证，在预测 pcRM 组织学方面显示出了比其他模型更高的判别能力。该模型具有更高的准确性和更少的变量，使其成为一种新颖且有吸引力的模型，可用于患者咨询和治疗策略。
玛格丽特公主医院模型准确预测了化疗后良性组织学。这些结果可能通过避免不必要的腹膜后淋巴结清扫术并改变晚期睾丸癌治疗模式而产生临床影响。
