Breast Cancer Clinical Trial
Official title:
Clinical Study of Imaging Genomics Based on Machine Learning for Breast Cancer Molecular Typing and Risk Prediction (BCIG)
1. Identify the imaging features of breast cancer with different molecular types
2. Reveal the association between hormone receptor positive/HER2 negative breast cancer and
imaging histology, Oncotype Dx recurrence score
3. Combine genomics and imaging to establish a predictive model for the sensitivity of
HER2-positive breast cancer targeted therapy
4. Establish an imaging genomics prediction model for triple-negative breast cancer
molecular subtypes, and clarify the imaging genomics characteristics of the therapeutic
targets of each subtype
Research design
1. Research on the molecular typing of breast cancer based on imaging features
2. Establish a Luminal breast cancer recurrence risk prediction model
3. Establish HER2 targeted therapy sensitivity prediction model
4. Establish TNBC molecular subtype prediction model Research methods Research Object This
study used a multi-center study to prospectively enroll breast cancer patients diagnosed
with pathology. All enrolled patients had complete clinical data, including demographic
characteristics (gender, age, menstrual status and fertility history), and pathological
data (histopathological data). Staging, immunohistochemical status and FISH, genetic
testing records the recurrence score and genotype), imaging data, complete treatment and
follow-up (whether there is local recurrence and metastasis, and the time of diagnosis).
Magnetic resonance examination In order to maintain the comparability between the images and
reduce the systematic errors, each center selects a fixed MR device for scanning. Among them,
a. Oncology Hospital chose to scan images with 3.0T (Siemens Skyra) MR equipment. A special
breast coil is used to add high-definition diffusion-weighted scanning and multi-b value
diffusion-weighted scanning before the dynamic enhancement scan. Dynamically enhanced
acquisition in 5 phases with a time resolution of 65s. b. Renji Hospital uses Netherlands
Philips Achieva 3.0 T superconductor MR scanner, 4-channel dedicated breast phased array
coil. Scanning sequences include T1WI, T2WI, T2WI fat suppression, DWI and DCE-MRI. The
contrast agent was Gd-DTPA, with a dose of 0.1 mmol/kg, an injection rate of 2.0 mL/s, and an
additional 20 mL of saline was added to the tube after injection. The T1WI scan was performed
first, and 5 time phases were continuously scanned after the injection of contrast agent, and
each time phase was separated by 61 s, for a total of 6 time phases. c. Chinese women and
babies are scanned with 1.5T SIEMENS AERA MR equipment and special breast coils. Scanning
sequence includes 5 phases of T1WI, T2WI fat suppression, DWI and dynamic enhancement scan,
time resolution 71s.
Image processing Use software to make semi-automatic and automatic outlines of the tumor
interest area, and make the outline of the tumor solid enhancement part, the entire tumor
area and the surrounding edema zone in the transverse position. In order to accurately
delineate the tumor, compare the T1 and T2 weighted and dynamically enhanced images, two
imaging physicians are responsible, one is responsible for delineation and the other is
reviewed, and the disputed area is determined after discussion by a third person. Create a
dynamic enhanced tumor texture analysis program to automatically extract imaging omics
features in the region of interest. Using a labeled data set, a computer-based automatic
segmentation algorithm model based on machine learning is constructed to automatically
extract regions of interest, and segmentation performance evaluation is performed on manually
delineated labels.
Statistical analysis Perform statistical analysis on the obtained images and clinical data,
extract image omics features and use machine learning algorithms to screen important
features. Use statistical tools such as SPSS and R language. Paired t test (continuous
variable) and chi-square test (discontinuous variable) were used to compare the clinical and
imaging characteristics of patients with different prognosis; correlation analysis was used
to evaluate the imaging histology characteristics and different pathological tissue grades,
Correlation between lymph node metastasis and specific gene expression; use Kaplan-Meier
survival curve to analyze the prognostic difference between patients with different imaging
omics characteristics, and use log-rank method to test the difference; use cox survival model
to compare clinical characteristics and imaging omics The characteristics and prognosis of
patients (tumor-free survival, progression-free survival, overall survival) were analyzed by
multiple factors. Further, deep learning algorithms can be used to automatically learn
imaging omics features that may be related to molecular subtypes and prognosis to build
prediction models.
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