The Clinical Value of an Artificial Intelligence System Using Abbreviated Protocol of Breast MRI Facilitates Classification of Breast Lessions

Trial Title: The Clinical Value of an Artificial Intelligence System Using Abbreviated Protocol of Breast MRI Facilitates Classification of Breast Lessions

NCT ID: NCT05892380

Condition: Breast Cancer

Conditions: Official terms:
Breast Neoplasms

Conditions: Keywords:
Artificial intelligence
Breast cancer
Magnetic resonance imaging
Maximum intensity projection
Abbreviated protocol

Study type: Observational [Patient Registry]

Overall status: Not yet recruiting

Study design:

Time perspective: Prospective

Summary: This study aims to use a combination of abbreviated protocol and artificial intelligence to automatically identify lesions and make diagnosis without decreasing the diagnostic accuracy of breast cancer, thus enhancing the comfort of patient examination, accelerating the flow of examination and reducing the load of clinical work.

Detailed description: Full sequence of MRI scan is: MR scan protocol: 1. Magnetic fields and coils: Use MR scanners with high fields of 1.5 T and above with dedicated breast coils. 2. Scanning position: prone position with bilateral breasts naturally draped over the center of the breast coil. The position should ensure that all breast tissue is located in the coil, the skin and breast are not folded, the bilateral breast is symmetrical, the nipple is perpendicular to the ground, and the midline of the sternum is located in the middle line of the coil. 3. Scanning sequence and parameters: T1WI non-fat suppressed sequence, T2WI fat suppressed sequence, dynamic five-phase enhanced T1WI fat suppressed sequence, and diffusion weighted imaging (DWI); delayed sagittal T1W1 fat suppressed sequence. Imaging parameters: the thickness of the scanned layer should be ≤3 mm, the resolution within the layer should be <1.5 mm, and the single scan time should be <2 min. Enhancement scan: Gadobutrol used as the contrast agent, and the injection dose was 0.1 ml/kg, which was injected through the elbow vein at a rate of 2-3 mL/s using a pressure syringe, and 10-20 mL of saline was injected into the tube at the same rate after the contrast agent injection. The T1WI sequences before and after enhancement were preferably fat-suppressed and bilateral mammary glands were imaged simultaneously, and subtraction treatment was recommended. The recommended duration of delayed enhancement scan is 7 min, not less than 5 min. Abbreviated sequences : T1WI + dynamic enhanced T1 phase I + maximum density projection images generated by automatic reconstruction in three directions. No extra sequences are required. By adding artificial inteeligence, a diagnostic performance comparable with full sequences is expected.

Criteria for eligibility:

Study pop:
The statistical analyses in this study were all descriptive, without any predefined hypotheses. Referring to a recent study* of artificial intelligence combined with abbreviated protocol MRI to classify benign and malignant non-mass-enhancing lesions in the breast*, a sample size of 800 cases was taken, which would require 800 patients to be enrolled, based on at least one lesion at the initial consultation of one patient.

Sampling method: Probability Sample
Criteria:
Inclusion Criteria: 1. Patients with breast lesions detected by ultrasound and mammography that cannot be characterized 2. Patients who were consecutively included in our hospital for breast MRI without treatment 3. Underwent preoperative full-protocol breast MRI 4. Pathological results are available, of which benign lesions can be determined by follow-up Exclusion Criteria: 1. Poor MRI image quality 2. Patients who have been received the biopsy

Gender: All

Minimum age: 18 Years

Maximum age: N/A

Locations:

Facility:
Name: Fudan university Shanghai Cancer Center

Address:
City: Shanghai
Zip: 200032
Country: China

Contact:
Last name: Yajia Gu, MD

Phone: +8621-64175590
Email: cjr.guyajia@vip.163.com

Contact backup:
Last name: Tianwen Xie, MD

Phone: +8621-64175590
Email: 7583724@qq.com

Start date: July 1, 2023

Completion date: August 1, 2025

Lead sponsor:
Agency: Fudan University
Agency class: Other

Collaborator:
Agency: Bayer
Agency class: Industry

Source: Fudan University

Record processing date: ClinicalTrials.gov processed this data on November 12, 2024

Source: ClinicalTrials.gov page: https://clinicaltrials.gov/ct2/show/NCT05892380