Clinical Trial Details
— Status: Completed
Administrative data
NCT number |
NCT06293612 |
Other study ID # |
22/449-3651 |
Secondary ID |
2022-I2M-C&T-B-0 |
Status |
Completed |
Phase |
|
First received |
|
Last updated |
|
Start date |
January 1, 2015 |
Est. completion date |
November 30, 2022 |
Study information
Verified date |
February 2024 |
Source |
Cancer Institute and Hospital, Chinese Academy of Medical Sciences |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
The goal of this observational study is to construct a multimodal intelligent model to
predict the risk of heterochronous metastasis of rectal cancer, which is helpful for
individualized diagnosis and treatment and follow-up planning. The main questions it aims to
answer are:
- what are the independent risk factors of distant metastasis (DM) in locally advanced
rectal cancer (LARC)
- What is the influence weight of the above factors on the heterochronous metastasis of
LARC, and how to build a risk-prediction model
This study will not affect or interfere with the routine medical diagnosis and treatment of
participants, and will not increase the cost and risk of participants. Participant's
information is protected and identified by a unique code.
Description:
Data collection process in strict implementation of special management, the full name of
quality control. Special personnel are responsible for collecting clinical images and
pathological information of patients in the medical record system, and personal
identification data will be used to identify and process, in order to protect the privacy of
test patients/participants. At the same time, there is a special person responsible for
quality control and source data verification. Participants use the project unified number as
the unique identification code, personal data and case information by the sample management
personnel input and save, the user only see the individual number, no longer show the
participants' name and other personal information. Data dictionary that contains detailed
descriptions of each variable used by the registry, including the source of the variable,
coding information was built. Participants' sample information is stored electronically in a
dedicated computer that is not used for other purposes and is provided with a password, which
is only available to the person (1 person) who manages the sample. Participants' medical
records will be kept at the hospital and will be accessible only to researchers; If
necessary, members of the bidding organization, ethics committee or government management
department may access the personal data of the participants according to the corresponding
authority. The results of the study will be published as statistically analyzed data and will
not contain any identifiable participant information.
The sponsor is responsible for implementing and maintaining quality assurance and quality
control systems with written Standard Operating Procedures (SOPs) to ensure that trials are
conducted and data are generated, documented (recorded), and reported in compliance with the
protocol, good clinical practices (GCP), and the applicable regulatory requirement(s). The
SOPs should cover system setup, installation, and use. The SOPs should describe system
validation and functionality testing, data collection and handling, system maintenance,
system security measures, change control,data backup, recovery, contingency planning, and
decommissioning. The responsibilities of the sponsor, investigator, and other parties with
respect to the use of these computerized systems should be clear, and the users should be
provided with training in use. Noncompliance with the protocol,SOPs,GCP, and/or applicable
regulatory requirement(s) by an investigator/institution, or by member(s) of the sponsor's
staff should lead to prompt action by the sponsor to secure compliance.
According to the morbidity of LARC and the risk of distant metastasis, the sample size is
300, and the follow-up interval is at least 3 years.
Plan for missing data: If the proportion of missing data is very large, such as greater than
95%, the investigators can directly remove this field; At 50~95%, the investigators have two
processing methods, one is to remove this field directly; another way is to turn the field
into an indicator variable; that is the 0-1 variable. If the field is empty, the field is 0;
Otherwise the field is 1. Between 5% and 50% : In this scenario, the investigators need to
fill in the missing values. In the process of filling, there are two categories: simple
filling and algorithm filling. Simple filling includes: 0 filling, mean filling, median
filling, mode filling; Algorithm filling methods such as K Nearest Neighbors (KNN) filling,
random forest filling and so on.
Statistics Statistical Product and Service Solutions (SPSS, version 26.0) and R software
(version 4.0.5) were used for statistical analyses. Receiver operating characteristic (ROC)
curve analysis was used to evaluate the optimum cutoff value of tumor stromal ratio (TSR) in
discriminating DM risk based on the maximum Youden index. Heat maps showed the distribution
of variables between patients with or without DM within 3 years. The independent DMFS risk
factors were determined using Kaplan-Meier (K-M) curves and Cox regression analysis
sequentially based on the data of the training cohort. Statistical significance was set at
P<0.05. Inter-observer variability was assessed using κ statistics for categorical and ranked
variables, and ICC for continuous variables.
TSR assessment Biopsy specimens from colonoscopy were sectioned into 5 μm slices and stained
with H&E. Areas with both stromal and tumor cells presented on all four sides were selected
to evaluate tumor stroma ratio (TSR) using an automated scoring method. The highest
proportion of stromal components in all measured areas was recorded as the final TSR value in
this study.
Magnetic Resonance analysis Machine-learning method was used to analyze the MR images, which
includ image acquisition and reconstruction, image segmentation, feature extraction and
qualification, analysis, and model building.
Pretreatment magnetic resonance (MR) examinations were conducted using 3.0 T scanners with an
8-channel phased-array wrap-around surface coil. An intramuscular injection of 10 mg
raceanisodamine hydrochloride was administered to minimize bowel movement unless
contraindicated. Sequences acquired included T1-weighted imaging (T1WI), T2-weighted imaging
(T2WI) with and without fat saturation, and diffusion-weighted imaging (DWI) .
The tumor region of interest (ROI) was manually delineated slice-by-slice on high-resolution
oblique axial T2WI (orthogonal to the rectal lumen) by the first radiologist and subsequently
confirmed by the second radiologist with more experience on Insight Segmentation and
Registration Toolkit-Standford Network Analysis Project (ITK-SNAP), from which the
three-dimensional whole tumor volume of interest (VOI) was obtained. Disagreements were
resolved through discussions. The radiologists were blinded to the clinicopathological
information. Overall, 1229 features were extracted from each VOI, which can be classified
into four categories: (1) shape characteristics; (2) first-order statistical characteristics;
(3) texture features; and (4) high-order statistical characteristics.
The extracted features' inter- and intraclass correlation coefficients (ICCs) were calculated
to assess the reproducibility of the features. Features with <0.75 ICCs were considered
non-stable and were eliminated. Pearson's correlation analysis was used to identify redundant
features, and for any two features with a coefficient of 0.9, the one with the larger mean
absolute coefficient was eliminated. The least absolute shrinkage and selection operator
algorithm (LASSO) was applied to select the most significant predictive parameter from the
training cohort, and 5-fold cross-validation was used to perform dimensionality reduction. A
signature (i.e. Radscore) was calculated using a linear combination of the final selected
features weighted by the respective coefficients.
Model built and validation According to the results of Cox regression, a nomogram (Mr)
integrating all independent risk factors except TSR, a TSR nomogram (Mt) integrating all
independent risk factors except the Radscore, and a combined model (Mrt) incorporating all
the independent risk factors were constructed to predict the 3-year DM risk. The
discriminative ability of these models was evaluated and compared using ROC curves.
Calibration plots were drawn to explore the calibration ability of the three models. Decision
curve analysis was performed to explore the clinical benefits by calculating the net benefit
of each decision strategy at each threshold probability.