The EORTC-DeCOG nomogram adequately predicts outcomes of patients with sentinel node–positive melanoma without the need for completion lymph node dissection

Purpose: Based on recent advances in the management of patients with sentinel node (SN)–positive melanoma, we aimed to develop prediction models for recurrence, distant metastasis (DM) and overall mortality (OM). Methods: The derivation cohort consisted of 1080 patients with SN-positive melanoma from nine European Organization for Research and Treatment of Cancer (EORTC) centres. Prognostic factors for recurrence, DM and OM were studied with Cox regression analysis. Significant factors were incorporated in the models. Performance was assessed by discrimination (c-index) and calibration in cross-validation across centres. The models were externally validated using a prospective cohort consisting of 705 German patients with SN-positive: 473 trial participants of the German Dermatologic Cooperative Oncology Group study (DeCOG-SLT) and 232 screened patients. A nomogram was developed for graphical presentation. Results: The final model for recurrence and the calibrated models for DM and OM included ulceration, age, SN tumour burden and Breslow thickness. The models showed reasonable calibration. The c-index for the recurrence, DM and OM model was 0.68, 0.70 and 0.70, respectively, and 0.70, 0.72 and 0.74, respectively, in external validation. The EORTC-DeCOG model identified a robust low-risk group, with all identified low-risk patients (approximately 4% of the entire population) having a 5-year recurrence probability of <25% and an overall 5-year recurrence rate of 13%. A model including information on completion lymph node dissection (CLND) showed only marginal improvement in model performance. Conclusions: The EORTC-DeCOG nomogram provides an adequate prognostic tool for patients with SN-positive melanoma, without the need for CLND. It showed consistent results across validation. The nomogram could be used for patient counselling and might aid in adjuvant therapy decision-making