Prediction model for para-aortic lymph node metastasis in patients with locally advanced cervical cancer.

OBJECTIVE: Concurrent chemoradiotherapy is usually administered to patients with locally advanced cervical cancer (LACC). Extended-field chemoradiotherapy is required if para-aortic lymph node (PALN) metastasis is detected. This study aimed to construct a prediction model for PALN metastasis in patients with LACC before definitive treatment.

METHODS: Between 2009 and 2016, all consecutive patients with LACC who underwent para-aortic lymphadenectomy at two tertiary centers were retrospectively analyzed. A multivariate logistic model was constructed, from which a prediction model for PALN metastasis was developed and internally validated. Before analysis, risk grouping was predefined based on the likelihood ratio.

RESULTS: In total, 245 patients satisfied the eligibility criteria. Thirty-four patients (13.9%) had pathologically proven PALN metastases. Additionally, 16/222 (7.2%) patients with negative PALNs on positron emission tomography/computed tomography (PET/CT) had PALN metastasis. Moreover, 11/105 (10.5%) patients with both negative PALNs and positive pelvic lymph nodes on PET/CT had PALN metastasis. Tumor size on magnetic resonance imaging and PALN status on PET/CT were independent predictors of PALN metastasis. The model incorporating these two predictors displayed good discrimination and calibration (bootstrap-corrected concordance index=0.886; 95% confidence interval=0.825-0.947). The model categorized 169 (69%), 52 (22%), and 23 (9%) patients into low-, intermediate-, and high-risk groups, respectively. The predicted probabilities of PALN metastasis for these groups were 2.9, 20.8, and 76.2%, respectively.

CONCLUSION: We constructed a robust model predicting PALN metastasis in patients with LACC that may improve clinical trial design and help clinicians determine whether nodal-staging surgery should be performed.