Primary carcinoma of the lacrimal drainage system: a systematic review of treatment, outcomes and associated prognostic factors

BACKGROUND: Primary carcinoma of the lacrimal drainage system (NLCA) is a rare and aggressive malignancy with poorly defined prognostic factors and no established treatment guidelines. OBJECTIVE: To characterize epidemiological and histopathological features, evaluate survival outcomes, and identify independent prognostic factors through a pooled analysis. METHODS: A systematic review was conducted per PRISMA guidelines. Individual patient-level data were pooled for logistic regression and Kaplan-Meier analyses. A random-effects meta-analysis of proportions was performed on aggregate data from retrospective case series. Methodological quality was assessed using JBI Critical Appraisal Checklists. RESULTS: Eighty-one studies encompassing 201 patients were included. The most frequent histologic subtype was squamous cell carcinoma (38.3 %), followed by adenoid cystic carcinoma (ACC, 15.4 %). Estimated 2-year overall survival, recurrence-free survival (RFS), and metastasis-free survival (MFS) were 96 %, 88 %, and 92 %; 5-year RFS and MFS were 85 % and 91 %. ACC was independently associated with disease-related death (OR 1.79, 95 % CI 1.04-3.09, p = 0.037) and with significantly worse overall survival (log-rank p = 0.013). Positive surgical margins (R1) were associated with higher odds of recurrence (OR 8.68, 95 % CI 1.99-37.81, p = 0.004), metastasis (OR 5.05, 95 % CI 1.02-25.04, p = 0.048), and death (OR 9.62, 95 % CI 1.10-83.89, p = 0.040). Pooled recurrence, metastasis, and mortality rates were 15.0 %, 13.5 %, and 13.3 %, respectively. CONCLUSIONS: In this exploratory analysis, positive surgical margins and ACC histology were associated with worse oncological outcomes. Given the primary independent prognostic determinants in NLCA, R0 resection should be the principal surgical objective, while ACC warrants intensified surveillance. Multicentre prospective studies are needed to define optimal treatment algorithms.