54P Survival outcomes and clinicopathological features of rectal neuroendocrine tumors: A retrospective analysis from a Latin American cohort

Background: Radical surgical treatment is the standard-of-care in localized and locally advanced Lung Neuroendocrine tumors (Lu-NET).Although considered indolent, recurrence rates, especially in the first 2 years post-surgery, are not negligible. Methods:The aim of this study is to design and evaluate a predictive model of postsurgical recurrence in Lu-NET.Data were collected from all patients referred to our center from 01/2021 undergoing radical surgery with at least 12 months of follow-up.Clinicopathological information was collected and impact selected factors on RFS was evaluated by KM analysis.Three models were constructed using selected predictors of recurrence using (ML) algorithms, specifically a multilayer perceptron (MLP).70% of the information was used as data for the training group and 30% was used for the verification group.A Repeated Holdout method was used for internal validation.Accuracy and AUC were calculated.Results: We included 81 patients (54F); 70 typical carcinoid and 11 atypical carcinoid(AC).Ki-67% was<3% in 53 and mitotic index was<2 in 64 cases.A functional syndrome was present in 6. Stage was I in 57, and 15 had LV1. 13 recurrence events were observed (16.7%), including 5 during the first 12 months, 11 in 24 months.According to KM analysis, age (p<0.001),AC (p0.006),Ki-67% (p<0.001),IM (p<0.001) functional syndrome (p<0.001),stage (p<0.001)LV1 (p0.023) were found to be predictors of shorter RFS.Three models were then constructed to predict recurrence at 12m, 24m, and independently of time using the predictors identified at KM analysis.The model at 12m had an AUC0.911(SD:0.134)with accuracy96.49%(SD:3.32%); the predictive model at 24m AUC0.883(SD:0.086)and accu-racy91.10%(SD:7.17%).Finally, the time-independent model demonstrated an AUC0.922(SD:0.054)and accuracy88.78%(SD:8.62%).Conclusions: Valid predictive model based on ML could be an important tool to support clinician.For the first time in this preliminary study, a similar model was investigated in Lu-NETs with promising results.Results must be interpreted considering the study limitations, like its retrospective nature and low numerosity.Subsequent prospective study will be able to confirm these results.