Quantitative Assessment of Diffusion Spectrum Imaging in Breast Cancer
NCT05159323 · Status: UNKNOWN · Phase: NA · Type: INTERVENTIONAL · Enrollment: 64
Last updated 2021-12-16
Summary
This project intends to prospectively collect patients with suspected breast malignant tumors by ultrasound or mammography. After routine MRI scanning, all patients underwent diffusion spectrum imaging (DSI) sequence scanning. The inclusion criteria were as follows: (1) breast cancer was confirmed by surgery or biopsy. (2) pathologically diagnosis of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 (HER-2), Ki-67, and lymphatic vessel invasion (LVI) status in breast cancer. (3) routine MRI and DSI scans were performed within one week before the pathologic examination. The exclusion criteria were as follows: (1) patients who had received treatment before DSI scanning; (2) patients who underwent breast tumor biopsy within two weeks before DSI image acquisition; (3) pathology results of breast masses were other diseases besides breast cancer. (4) post-processing of DSI data cannot be performed due to poor image quality, such as motion artifacts.
Breast MRI data were collected on a 3T MR scanner (Magnetom skyra, Siemens Healthcare, Erlangen, Germany). All participants used standardized breast MRI scanning schemes, including T2 weighted imaging (T2WI), T1 weighted imaging (T1WI), diffusion-weighted imaging (DWI), DSI, and contrast dynamic enhancement (DCE). A total of 22 GSI quantitative parameters were derived from NeudiLab software that is based on the open-source platform DIPY (diffusion imaging in Python, http://nipy.org/dipy). The correlation between DSI quantitative parameters and pathological indexes (i.e., ER, PR, HER-2, Ki-67, and LVI) was evaluated by Spearman correlation analysis. The independent predictors of GSI quantitative parameters for different pathologic characteristics discrimination in breast cancer were determined by the logistic regression analysis. The predictive performance of DSI quantitative parameters for difference pathologic classifications was assessed by the receiver operating characteristic (ROC) curves and their respective area under the curves (AUCs).
Conditions
- Breast Carcinoma; Magnetic Resonance Imaging; Diffusion Spectrum Imaging
Interventions
- DIAGNOSTIC_TEST
-
Diffusion Spectrum Imaging
A novel diffusion-based magnetic resonance imaging method
Sponsors & Collaborators
-
Xiang Zhang
lead OTHER
Principal Investigators
-
Xiang Zhang, M.D. · Sun Yat-sen University
Study Design
- Allocation
- NA
- Purpose
- DIAGNOSTIC
- Masking
- NONE
- Model
- SINGLE_GROUP
Eligibility
- Min Age
- 18 Years
- Sex
- FEMALE
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2021-08-20
- Primary Completion
- 2022-08-19
- Completion
- 2022-08-19
Countries
- China
Study Locations
More Related Trials
-
The Application Value of Spectral CT in the Accurate Staging of Breast Cancer
NCT06977412 ·Status: NOT_YET_RECRUITING ·Phase: NA
-
Clinical Study on the Application of 18F-HER2 Affibody PET Imaging in HER2-Expressing Breast Cancer
NCT06916637 ·Status: RECRUITING
-
Diffusion-Weighted MRI for Diagnosis of Multifocal, Multicentric Breast Cancer
NCT04656639 ·Status: RECRUITING
-
Light-CT in the Diagnosis of Breast Tumor and Lymph Node
NCT03791853 ·Status: UNKNOWN
-
Detection and Analysis of MBC With Heterogeneous ER Expression
NCT05392985 ·Status: COMPLETED
-
Prospective Radiomics Study for Breast Cancer
NCT06095414 ·Status: COMPLETED
-
Radiomics Model Based on DCE-MRI and Ultrasound Images for Breast Lesion Classification
NCT06497023 ·Status: COMPLETED
-
Prospective Ultrafast MRI Radiomics for Breast Cancer
NCT06104189 ·Status: COMPLETED
-
Clinical Value of Breast High-Resolution MR Ductography in Patients With Pathological Nipple Discharge
NCT05812040 ·Status: RECRUITING ·Phase: NA
-
Development of Artificial Intelligence System for Detection and Diagnosis of Breast Lesion Using Mammography
NCT03708978 ·Status: COMPLETED
-
Ultrasound and Mammography for Screening Breast Cancer in Chinese Women
NCT01880853 ·Status: COMPLETED ·Phase: NA
-
PET/MR Radiomics for Breast Cancer Diagnosis
NCT05466760 ·Status: UNKNOWN
-
Using Deep Learning and Radiomics to Diagnose Benign and Malignant Breast Lesions Based on Ultrasound
NCT06069921 ·Status: COMPLETED
-
99mTc-MIRC213 SPECT/CT for the Detection of HER2-positive Breast Cancer
NCT05622240 ·Status: UNKNOWN ·Phase: EARLY_PHASE1
-
Breast Cancer Screening With Diffusion-weighted MRI in Women at High Risk for Breast Cancer
NCT03835897 ·Status: UNKNOWN
-
Clinical Study of Imaging Genomics Based on Machine Learning for BCIG
NCT04461990 ·Status: UNKNOWN
-
Diffusion-Weighted MRI for Breast Cancer Screening in Women With a Personal History of Breast Cancer
NCT04619186 ·Status: SUSPENDED
-
Clinical Application of ctDNA Dynamic Monitoring in Neoadjuvant Therapy for HER2-positive Breast Cancer Patients
NCT06479460 ·Status: RECRUITING
-
DTi Diffusion Findings in Breast MRI Screening and Diagnostic
NCT03158441 ·Status: WITHDRAWN
-
MRD in High-risk EBC
NCT06566729 ·Status: ACTIVE_NOT_RECRUITING
-
Accuracy Of Contrast Enhanced Mamography in Predicting Response of Breast Cancer Post Neoadjuvant Chemotherapy
NCT05141279 ·Status: UNKNOWN
-
Dynamic Monitoring of HER2 and ctDNA Specific Mutations in Patients With Recurrent or Metastatic Breast Cancer by Digital PCR
NCT03947736 ·Status: UNKNOWN
-
MRI to Detect Breast Tumors in Women
NCT00003302 ·Status: COMPLETED ·Phase: NA
-
Circulating Tumor Cell Detection in Patients With Luminal A Breast Cancer
NCT04065321 ·Status: RECRUITING
-
Using Diagnostic Tools to Stage Breast Cancer
NCT00367666 ·Status: ACTIVE_NOT_RECRUITING ·Phase: NA