Value of Automatically Segmented Three-Dimensional(3D) Volumetric Body Composition in Predicting the Pathological Grading and Prognosis of Gastroenteropancreatic Neuroendocrine Neoplasms: A Multicenter Study
Conditions: Neuroendocrine Tumors
Sponsor: Tongji Hospital

Study Protocol
TJ-IRB202411010
Retrospectively Analyze the
Correlation Between Body
Composition Based on CT
Measurement and Pathological
Grading and Prognosis in Patients
with Gastroenteropancreatic
Neuroendocrine Neoplasms
Version date: 2024.11.4

I.Research Background
Neuroendocrine Neoplasms (NENs) are a heterogeneous group of tumors.
Although relatively rare, their incidence has increased rapidly over the past decade (1).
NENs
can
occur
in
any
organ
or
tissue,
with
62-70%
occurring
in
the
gastrointestinal-pancreatic system (GEP-NENs)(2).
According
to
the
WHO
classification
criteria,
NENs
are
divided
into
well-differentiated
gastroenteropancreatic
neuroendocrine
tumors
(GEP-NETs),
poorly differentiated gastroenteropancreatic neuroendocrine carcinomas (GEP-NECs),
and mixed neuroendocrine/neuroepithelial tumors (MiNENs). Based on the Ki-67
proliferation index and/or mitotic count, GEP NET is classified into grades G1, G2,
and G3. GEP NET G3, GEP NEC, and MiNENs are considered high-grade GEP
NENs, while NET G1 and NET G2 are considered low-grade GEP NENs(3,4).
Treatment for high-grade and low-grade neuroendocrine tumors differ(5). Patients
with high-grade neuroendocrine tumors tend to have shorter median overall survival
and poorer prognosis(6). Therefore, pathological grading is of great significance for
clinical diagnosis, treatment, and prognosis of patients. However, most information
can only be obtained after surgical and histological evaluation(7). Thus, exploring
markers associated with GEP-NENs grading and prognosis is of great importance for
patient diagnosis and treatment.
Excess body fat is one of the causes of various cancers. Studies have shown that
BMI is a risk factor for neuroendocrine tumors(9). Although BMI is used to assess
obesity, it does not specifically reflect the distribution of body fat or distinguish
between muscle and fat tissue (10). Computed tomography (CT) can economically
and effectively obtain body composition parameters such as muscle and fat(11).
Recently, CT images at the third lumbar level have been considered a widely available
method and have been used to study body composition in several diseases(12-14).
However,
two-dimensional
parameters
have
their
limitations:
cannot
explain
differences in fat and muscle distribution among individuals. Prognostic analysis
using three-dimensional body composition parameters is relatively rare.
In this study, we attempted to explore the relationship between 3D body

composition parameters and
grading and prognosis of NENs through a larger
sample size.
II. Research Objectives
This study aims to analyze the association between body composition measured
by CT and the pathological grading and prognosis of patients with GEP-NENs
confirmed by surgical pathology through a retrospective review of CT imaging data..
III. Research Design
This study is a retrospective study.
IV. Selection of subjects
1. Inclusion criteria
(1) a confirmed diagnosis of GEP-NENs via histopathology with a WHO
grading.
(2) available enhanced CT scans that included full abdominal imaging before
surgery or biopsy.
2. Exclusion criteria
(1) therapeutic interventions before surgery or biopsy
(2) poor image quality or incomplete CT images of the whole abdomen.
V. Sample size
Retrospective analysis of GEP-NENs patients from 3 Hospitals from April 2014
to April 2024. The number of included cases was 633 patients.
VI. Research process
1. Clinical Data Collection: Through the Radiology Information System (RIS) , we
queried the list of patients with GEP-NENs from April 2014 to April 2024. Based on
postoperative pathology and imaging examination types, combined with the exclusion
criteria of this study, clinical cases were screened.
2. Collection of imaging data: Use PACS system to sort out the patient's imaging data
information through inclusion and exclusion criteria, screen the cases that can be
enrolled.

3. Image data processing: Through PACS system, DICOM format image data is
downloaded by workstation, and the image is post-processed and analyzed on the
computer with image post-processing software.
VII. Statistical analysis
Data are presented as medians (range) or number (proportion). The Shapiro-Wilk
test for normality is employed. Depending on their distribution, Mann-Whitney U or
student t tests are performed. We conduct univariate logistic regression analysis to
evaluate the relationship between certain variables and a high grade of GEP-NENs.
Variables with p<0.05 are chosen for multivariate analysis, for which odds ratios
(ORs) with 95% confidence intervals (CIs) are computed. The optimal cut-off of
continuous variables are obtained by using X-tile. A univariate and multivariable Cox
proportional hazard model is constructed to determine predictive factors for prognosis.
PFS and OS are estimated using the Kaplan-Meier method and compared by the
log-rank test. Statistical analyses are conducted using SPSS (version 26.0, IBM) and
GraphPad Prism (version 8.3.0, GraphPad Software Inc., USA), with a significance
threshold of p<0.05

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