Clinical Study of Imaging Genomics Based on Machine Learning for BCIG

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

Study Overview

• Status: Not yet recruiting
• Conditions: Breast Cancer; Image, Body; Pathology; Molecular Typing
• Intervention / Treatment: Procedure: Multidisciplinary cooperative comprehensive treatment

Detailed Description

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.

• Study Type: Observational
• Enrollment (Anticipated): 1500

Contacts and Locations

• Study Contact: Name: Gu Ya Jia; Phone Number: 86-18017317817; Email: guyajia@126.com

Study Locations

• China
  • Shanghai
    • Shanghai, Shanghai, China, 200032
      • Fudan University Shanghai Cancer Center
      • Contact: Gu Ya Jia; Phone Number: 86-18017312040; Email: guyajia@126.com
      • Principal Investigator: Hua jia
      • Principal Investigator: Qian zhaoxia
      • Principal Investigator: Wang he
      • Sub-Investigator: You chao
      • Sub-Investigator: Zhuang zhiguo
      • Sub-Investigator: Jiang ling
      • Sub-Investigator: Zheng rencheng
      • Sub-Investigator: Xiao qin
      • Sub-Investigator: Chen yanqiong
      • Sub-Investigator: Hu xiaoxin

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: Female

• Sampling Method: Probability Sample

Study Population

Prospectively enrolled breast cancer patients diagnosed by pathology, all clinical data of all enrolled patients are complete, including demographic characteristics (gender, age, menstrual status and fertility history), pathological data (staging in histopathology, immunohistochemistry) 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)

Description

Inclusion Criteria:

1. Pathological and immunohistochemical diagnosis of breast cancer by biopsy
2. No MRI contraindications and no biopsy before MRI
3. Without radiotherapy and chemotherapy before enrollment

Exclusion Criteria:

1. Those with previous history of breast cancer surgery, hormone replacement therapy and chest radiotherapy
2. Patients with severe diseases who cannot cooperate with the examination
3. People with contraindications to MRI
4. The researchers believe that other conditions are not suitable for breast MRI examination

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Luminal; Luminal A：ER+ and/or PR+,HER2- Luminal B：ER+ and/or PR+,HER2+ * ER:estrogen receptor PR:progesterone receptor HER2:human epidermalgrowth factor receptor-2	Procedure: Multidisciplinary cooperative comprehensive treatment; Local surgery, radiation therapy, and systemic therapy such as chemotherapy, endocrine and molecular targeting.
HER2 overexpression; ER- PR-,HER2+ * ER:estrogen receptor PR:progesterone receptor HER2:human epidermalgrowth factor receptor-2	Procedure: Multidisciplinary cooperative comprehensive treatment; Local surgery, radiation therapy, and systemic therapy such as chemotherapy, endocrine and molecular targeting.
Triple negative; ER- PR-,HER2- * ER:estrogen receptor PR:progesterone receptor HER2:human epidermalgrowth factor receptor-2	Procedure: Multidisciplinary cooperative comprehensive treatment; Local surgery, radiation therapy, and systemic therapy such as chemotherapy, endocrine and molecular targeting.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Image prediction model of different molecular typing	Build a model to predict molecular typing based on image; Establish a prediction model for predicting the risk of Luminal breast cancer recurrence; Establish a prediction model for predicting her2 targeted drug resistance; Establishing a triple-negative molecular model for breast cancer	30 December，2022----30 December，2023

Collaborators and Investigators

• Sponsor: Fudan University
• Collaborators: RenJi Hospital; International Peace Maternity and Child Health Hospital
• Investigators: Principal Investigator: Gu Ya Jia, Fudan University

Study record dates

Study Major Dates

• Study Start (Anticipated): December 1, 2020
• Primary Completion (Anticipated): December 30, 2022
• Study Completion (Anticipated): December 30, 2023

Study Registration Dates

• First Submitted: July 4, 2020
• First Submitted That Met QC Criteria: July 4, 2020
• First Posted (Actual): July 8, 2020

Study Record Updates

• Last Update Posted (Actual): August 21, 2020
• Last Update Submitted That Met QC Criteria: August 20, 2020
• Last Verified: August 1, 2020

More Information

Terms related to this study

• Keywords: breast cancer; Molecular Typing; Pathology; magnetic resonance
• Additional Relevant MeSH Terms: Skin Diseases; Neoplasms; Neoplasms by Site; Breast Diseases; Breast Neoplasms
• Other Study ID Numbers: BCIG

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No