A Novel Predictive Model of Pathological Lymph Node Metastasis Constructed with Preoperative Independent Predictors in Patients with Renal Cell Carcinoma

Abstract

Introduction: Renal cell carcinoma (RCC) is one of the most common urinary tumors. The risk of metastasis for patients with RCC is about 1/3, among which 30–40% have lymph node metastasis, and the existence of lymph node metastasis will greatly reduce the survival rate of patients. However, the necessity of lymph node dissection is still controversial at present. Therefore, a new predictive model is urgently needed to judge the risk of lymph node metastasis and guide clinical decision making before operation. Method: We retrospectively collected the data of 189 patients who underwent retroperitoneal lymph node dissection or enlarged lymph node resection due to suspected lymph node metastasis or enlarged lymph nodes found during an operation in Tongji Hospital from January 2016 to October 2021. Univariate and multivariate logistic regression and least absolute shrinkage and selection operator (lasso) regression analyses were used to identify preoperative predictors of pathological lymph node positivity. A nomogram was established to predict the probability of lymph node metastasis in patients with RCC before surgery according to the above independent predictors, and its efficacy was evaluated with a calibration curve and a DCA analysis. Result: Among the 189 patients, 54 (28.60%) were pN1 patients, and 135 (71.40%) were pN0 patients. Three independent impact factors were, finally, identified, which were the following: age (OR = 0.3769, 95% CI = 0.1864–0.7622, p < 0.01), lymph node size according to pre-operative imaging (10–20 mm: OR = 15.0040, 95% CI = 1.5666–143.7000, p < 0.05; >20 mm: OR = 4.4013, 95% CI = 1.4892–7.3134, p < 0.01) and clinical T stage (cT1–2 vs. cT3–4) (OR = 3.1641, 95% CI = 1.0336–9.6860, p < 0.05). The calibration curve and DCA (Decision Curve Analysis) showed the nomogram of this predictive model had good fitting. Conclusions: Low age, large lymph node size in pre-operative imaging and high clinical T stage can be used as independent predictive factors of pathological lymph node metastasis in patients with RCC. Our predictive nomogram using these factors exhibited excellent discrimination and calibration.

Therapeutic options for different metastatic sites arising from renal cell carcinoma: A review

“…This model shows potential for the more precise presurgical prediction of LNM-RCC, thereby enabling earlier clinical interventions and potentially improving patient prognosis. [73] Lymph node dissection (LND) has been proposed as a strategy to improve the outcomes of surgical therapy for many genitourinary malignancies. Nonetheless, the therapeutic efficacy of LND in RCC remains a subject of debate.…”

“…This model shows potential for the more precise presurgical prediction of LNM-RCC, thereby enabling earlier clinical interventions and potentially improving patient prognosis. [73]…”

Is there an association between lymph node size and hyperprogression in immunotherapy-treated patients?

“…Therefore, a novel predictive model of pathological LN metastasis constructed with preoperative independent predictors in patients with RCC is urgently needed. Luckily, a recent study by Sun JX et al concluded that Low age, large LN size in preoperative imaging, and high clinical T stage could be used as independent predictive factors of pathological LN metastasis in patients with RCC [38].…”

Using machine learning to predict lymph node metastasis in patients with renal cell carcinoma: A population-based study

“…Babaian et al (8) noted that LN dissection (LND) can improve the 5-year survival rate of patients with LN involvement, suggesting that patients with renal cancer undergoing LND may have better survival. LN infiltration is one of the most important predictors of tumor progression and mortality (9). Therefore, it is imperative to investigate the strong predictors of LN metastasis and accurately identify at-risk patients who require LND.…”