Method for Determining the Required Sample Size.This study is observational and involves no intervention. Therefore, the sample size was
estimated using the following formula:
where N is the sample size, Uα is the value of υ corresponding to the test level α, S is
the overall standard deviation, and δ is the allowable error.
Since the specific value of δ could not be known in advance, the study referred to the
literature and identified three widely accepted methods for estimating δ. Method ①:
Conduct a pre-experiment before the study begins, using the inter-group difference as the
δ value directly. Method ②: Consult relevant authorities in advance to determine a
professionally meaningful δ value. Method ③: In the absence of pre-experiment results and
expert opinions, it is permissible to use 0.25 or 0.50 times the standard deviation as δ.
According to the allowable error δ reference flowchart by Ni Yanyan and Zhang Jinxin,
this study used method ③ to determine δ. Therefore, at α = 0.05, the minimum sample size
N can range from 16 to 62 cases. Considering a loss-to-follow-up rate of 15%-20%, the
sample size can be expanded to 19-78 cases. Finally, considering that Logistic regression
analysis will be used in statistical analysis with 15 quantitative indicators as
independent variables, the estimated sample size should be at least 150 cases. Taking
into account factors such as economy and time, the sample size for this study is
determined to be 150-200 cases.
Research Content.The collection of glioma patients is divided into two parts: the first part is a
retrospective study based on already collected glioma patients, and the second part is a
prospective collection of glioma patients; detailed as follows:
First Part: Retrospectively collect glioma patients who visited our hospital from May 1,
2014, to December 31, 2024, according to the inclusion criteria.
Second Part: Prospectively collect glioma patients who visited our hospital from January
1, 2021, to December 31, 2022, according to the inclusion criteria. Furthermore, track,
obtain, and analyze tumor specimens from the enrolled glioma patients, conducting tumor
grade analysis and detecting major molecular gene mutation status related to prognosis
(including IDH, 1p19q, TERT, ATRX, EGFR amplification, 7+/10-, etc.). By using
quantitative, qualitative, radiomics, and machine learning methods to analyze
multiparametric MRI data of glioma patients, statistical analysis and modeling will be
performed to establish a model based on multiparametric MRI for predicting glioma grade
and prognosis-related molecular mutation status.
Survey Content The outcome indicators are glioma grade and prognosis-related molecular
mutation status, which will be tested by the pathology department personnel. The exposure
factor is the quantitative analysis data from multiparametric MRI examinations, and
potential confounding factors are the age and sex of the subjects; these indicators will
be searched, measured, and recorded by specialized personnel in the medical system.
Data Management and Statistical Analysis Plan Data Management: Use EXCEL software to
establish an electronic spreadsheet, with specialized personnel manually inputting data,
such as general information (replaced by patient numbers), age, sex, etc.,
multiparametric MRI examination data such as Ktrans values obtained after DCE-MRI
post-processing, and pathological data such as tumor grade and classification, different
molecular mutation statuses, etc., saving and archiving data in real-time.
Statistical Analysis: Use SPSS 25.0 statistical analysis software for data analysis. For
quantitative data such as Ktrans values obtained after DCE-MRI post-processing, perform
normality tests with S-W and Levene's tests. Data that is normally distributed is
expressed as mean ± standard deviation, and non-normally distributed data is expressed as
M (P25, P75). Prognosis-related molecular mutation status, such as IDH mutation, is
dichotomous data, with mutations, deletions, or amplifications.Bias Control Bias that is prone to occur in this observational study is information bias.
The main research steps where bias is likely to occur and the control methods are as
follows: ① Acquisition process of multiparametric MRI quantitative data. The control
method involves establishing uniform measurement standards before analysis and adhering
strictly to these standards; measuring multiple tumor areas and taking the average result
as the final result; and ensuring that the measurements are conducted by the same person
throughout. ② Data entry process. The control method involves timely review of each data
item during data collection, and randomly selecting some data for double entry to check
the quality of data entry. ③ Pathology. The control method involves classifying according
to the 2016 WHO new classification standards for glioma; and attempting to control the
detection of molecular mutation status to be done by the same person using the same batch
of test reagents at the same time. In addition to this, factors such as age and sex may
be confounding variables that can cause confounding bias. The control method involves
collecting patients' age, sex, etc., during the data collection process, and using
multivariate statistical analysis methods, stratified analysis methods, etc., to control
bias during statistical analysis.
Quality Management The entire research process is carried out according to the
experimental plan, with particular attention to controlling bias. The quality of the
study is evaluated based on the quality evaluation criteria recommended by the Agency for
Healthcare Research and Quality (AHRQ). The criteria include 11 items, which are answered
with "yes," "no," or "unclear":
- (1) Is the source of the data (survey, literature review)
clearly stated? (2) Are the inclusion and exclusion criteria for the exposed and
non-exposed groups (cases and controls) listed, or are previous publications referenced?
(3) Is the time frame for identifying patients provided? (4) If not from a population
source, is the study population continuous? (5) Do the subjective factors of the
evaluator obscure other aspects of the study subjects? (6) Is any assessment described
that was done to ensure quality (e.g., testing/retesting of primary outcome indicators)?
(7) Is the reason for excluding any patients from the analysis explained? (8) Are the
measures for evaluating and/or controlling confounding factors described? (9) If
possible, is the handling of missing data in the analysis explained? (10) Is the patient
response rate and the completeness of data collection summarized? (11) If there is
follow-up, is the percentage of expected incomplete data or follow-up results identified.
The observational study report is written in the format of the STROBE (the Strengthening
the Reporting of Safety Evaluation The main content of this study involves the secondary
use of medical records, imaging data, and biological specimens, posing almost no risk to
the participants.
Ethical Review and Informed Consent This study complies with medical ethical standards
and has applied for ethical approval from the Ethics Committee of the First Affiliated
Hospital of Sun Yat-sen University. For the prospective study part, informed consent will
be obtained from the participants, and an informed consent form will be signed. The study
will strictly adhere to the rules of patient information confidentiality, not recording
patient names, and distinguishing cases by image number or examination number. The
participants' multiparametric MRI data examination and analysis are non-invasive,
involving only the extraction and analysis of image data, with the participants' personal
information kept confidential throughout. Subsequent tumor sample analysis is based on
specimens already obtained from surgical resection or biopsy, hence there is no
additional risk in sample collection. During the study, the main molecular mutation
status related to prognosis in the participants' tumor samples will be analyzed. This
information will be beneficial for the clinical diagnosis, treatment, and prognosis
assessment of cancer, from which the participants will benefit.
