Modified Histopathological Grading Optimizes Prediction of Survival Outcomes in Small Intestinal Neuroendocrine Tumors.
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CONTEXT: One of the major prognostic indices in neuroendocrine tumors (NETs) is Ki67 proliferation index. OBJECTIVE: To identify optimal grading Ki67 cutoffs to delineate differences in prognosis of patients with small intestinal NETs (SI-NETs). METHODS: Multicenter retrospective cohort analysis of 551 SI-NET patients diagnosed from 1993 through 2021 at 5 European referral centers with a mean (±SD) follow-up time of 51.5 (±52.9) months, measuring rates of overall survival (OS) and event-free survival (EFS). RESULTS: Median age at baseline was 62.3 (range, 17-90) years; 252 (45.7%) patients were female. All SI-NETs were well-differentiated, with 326 being grade 1 (G1; 59.2%), 169 G2 (30.7%), and 8 G3 (1.5), while 48 tumors were unspecified grade (8.7%). The median Ki67 was 2% (range, 1%-70%). At baseline, 247 (44.8%) patients had distant metastases (stage IV), 217 locoregional disease (41.1%; stage III), while 29 (7.1%) and 25 (4.5%) presented at stages II and I, respectively. Median OS was 214.7 (95% CI, 152.7-276.6) months and median EFS was 79.8 (68.2-91.5) months. In multivariable Cox-regression OS analysis, the proposed modified histopathological Ki67 grading system (Ki67 5%-10% group: HR = 2.2 [95% CI, 1.15-4.31], P = .018 and Ki67 ≥ 10% group: HR = 5.11 [2.87-9.09], P < .001), age (HR = 1.07 [1.04-1.09], P < .001), Charlson Comorbidity Index (HR = 1.08 [1-1.16], P = .028), and TNM stage (HR = 1.79 [1.05-3.06], P = .034) were independent predictors for death. Pertinent EFS analysis confirmed the proposed modified histopathological Ki67 grading system (Ki67 ≥ 10% group: HR = 4.01 [2.6-6.37], P < .001) and age (HR = 1.04 [1.02-1.05], P < .001) as independent predictors for recurrence, progression, and/or death. CONCLUSION: Ki67 proliferation index was a strong and independent predictor of OS and EFS. A modified histopathological grading system applying Ki67 cutoffs of 5% and 10% could be superior to predict differences in SI-NET patient survival outcomes.