Gastroenteropancreatic neuroendocrine tumors: Role of surgery

Impact of grade on workup of rectal neuroendocrine tumors: a retrospective cohort study : Grade impact on workup of rectal NETs

BACKGROUND

Rectal neuroendocrine tumors (RNETs) are often discovered on screening colonoscopy. Indications for staging and definitive resection are inconsistent in current guidelines. We evaluated the role of grade in guiding staging and procedural decision-making.

METHODS

Patients with biopsy confirmed RNETs between 2004 and 2015 were reviewed. Baseline characteristics, staging investigations (biochemical and imaging), and endoscopic/surgical treatment were recorded. Associations between grade, preoperative staging, interventions, and survival were determined using Fisher-Freeman-Halton Exact, log-rank, and Kaplan-Meier analysis.

RESULTS

Amongst 139 patients with RNETs, 9% were aged ≥ 75 years and 44% female. Tumor grade was: 73% grade 1 (G1), 18%, grade 2 (G2) and 9% grade 3 (G3). Staging investigations were performed in 52% of patients. All serum chromogranin A and 97% of 24-hour urine 5-hydroxyindoleacetic acid tests were normal. The large majority of staging computed tomography (CT) scans were negative (76%) with subgroup analysis showing no G1 patients with CT identified distant disease compared with 38% of G2 and 50% of G3 patients (p < 0.001). G1 patients were more likely to achieve R0/R1 resections compared to G2 (95% vs. 50%, p < 0.001) and G1 patients had significantly better 5-year overall survival (G1: 98%, G2: 67%, G3: 10%, p < 0.001).

CONCLUSION

Tumor grade is important in preoperative workup and surgical decision-making. Biochemical staging may be omitted but staging CT should be considered for patients with grade ≥ 2 lesions. Anatomic resections should be considered for patients with grade 2 disease.

Nomogram for preoperative estimation of histologic grade in gastrointestinal neuroendocrine tumors

BACKGROUND

The treatment strategies and prognosis for gastroenteropancreatic neuroendocrine tumors were associated with tumor grade. Preoperative predictive grading could be of great benefit in the selection of treatment options for patients. However, there is still a lack of effective non-invasive strategies to detect gastrointestinal neuroendocrine tumors (GI-NETs) grading preoperatively.

METHODS

The data on 147 consecutive GI-NETs patients was retrospectively collected from January 1, 2012, to December 31, 2019. Logistic regression was used to construct a predictive model of gastrointestinal neuroendocrine tumor grading using preoperative laboratory and imaging parameters.The validity of the model was assessed by area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).

RESULTS

The factors associated with GI-NETs grading were age, tumor size, lymph nodes, neuron-specific enolase (NSE), hemoglobin (HGB) and sex, and two models were constructed by logistic regression for prediction. Combining these 6 factors, the nomogram was constructed for model 1 to distinguish between G3 and G1/2, achieving a good AUC of 0.921 (95% CI: 0.884-0.965), and the sensitivity, specificity, accuracy were 0.9167, 0.8256, 0.8630, respectively. The model 2 was to distinguish between G1 and G2/3, and the variables were age, tumor size, lymph nodes, NSE, with an AUC of 0.847 (95% CI: 0.799-0.915), and the sensitivity, specificity, accuracy were 0.7882, 0.8710, 0.8231, respectively. Two online web servers were established on the basis of the proposed nomogram to facilitate clinical use. Both models showed an excellent calibration curve through 1000 times bootstrapped dataset and the clinical usefulness were confirmed using decision curve analysis.

CONCLUSION

The model served as a valuable non-invasive tool for differentiating between different grades of GI-NETs, personalizing the calculation which can lead to a rational treatment choice.