Non-Contrast Breast MRI Diagnosis and Risk Stratification Using DWI-Generated Synthetic Contrast Enhancement

Официальное название

Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI: Development and Clinical Validation of a Diffusion-Weighted Imaging-Based Synthetic Contrast-Enhanced MRI System for Non-Contrast Breast Cancer Diagnosis and Risk Stratification

Обзор

This study is conducted under the ethics-approved project titled "Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI''.The goal of this observational study is to develop an integrated breast MRI system that uses diffusion-weighted imaging (DWI) to create synthetic contrast-enhanced images. This system aims to diagnose and screen for breast cancer without the need for contrast agents, while using a generated risk score to perform imaging-based triage and risk stratification. Participants will include people aged 18 and older who require a breast MRI either for evaluation of a suspicious finding or for high-risk screening. This study seeks to answer two main questions: * Can synthetic contrast-enhanced images generated from DWI match real contrast-enhanced images in their ability to distinguish benign from malignant breast lesions? * Can the risk score derived from DWI-based synthetic images enable imaging-level risk stratification, allowing people at lower risk to avoid contrast agent injection? Researchers will compare the quality of synthetic images against real contrast-enhanced images and will recruit radiologists to assess how well these images perform for diagnostic and screening tasks. MRI data from participants undergoing breast MRI will be used to train, validate, and test this integrated system.

Подробное описание

We selected "other" in Time Perspective. This study will retrospectively collect MRI data from patients who underwent breast MRI at multiple centers between 2014 and 2024. We will also prospectively enroll MRI data from multiple centers for testing to assess the model's robustness.

Вмешательства

• Диагностический тест Non-contrast breast MRI diagnostic model
  An integrated AI model capable of generating synthetic contrast-enhanced images and distinguishing between benign and malignant lesions, as well as performing risk stratification

Первичные конечные точки

• MRI examination [Срок оценки: Baseline]

Критерии участия

Критерии включения

• Complete breast MRI data;
• Negative pathology biopsy results or negative follow-up examinations for at least 12 months for non-cancer cases;
• Positive biopsy results that meet the requirements for the pathological subtype of cancer for cancer cases;
• Original data that can be used to verify clinical status, including radiological and pathological reports;

Критерии исключения

• Partial mastectomy or puncture biopsy on the diseased side of the breast prior to breast MRI examination;
• Poor image quality;
• Implants in the affected breast;

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Список центров уточняется — проверьте первичный протокол.

Публикации

• Wang P, Wang H, Nie P, Dang Y, Liu R, Qu M, Wang J, Mu G, Jia T, Shang L, Zhu K, Feng J, Chen B. Enabling AI-Generated Content for Gadolinium-Free Contrast-Enhanced Breast Magnetic Resonance Imaging. J Magn Reson Imaging. 2025 Mar;61(3):1232-1243. doi: 10.1002/jmri.29528. Epub 2024 Jul 25. PMID 39052258
• Chung M, Calabrese E, Mongan J, Ray KM, Hayward JH, Kelil T, Sieberg R, Hylton N, Joe BN, Lee AY. Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer. Radiology. 2023 Mar;306(3):e239004. doi: 10.1148/radiol.239004. No abstract available. PMID 36803003
• Youn I, Biswas D, Hippe DS, Winter AM, Kazerouni AS, Javid SH, Lee JM, Rahbar H, Partridge SC. Diagnostic Performance of Point-of-Care Apparent Diffusion Coefficient Measures to Reduce Biopsy in Breast Lesions at MRI: Clinical Validation. Radiology. 2024 Feb;310(2):e232313. doi: 10.1148/radiol.232313. PMID 38349238
• Witowski J, Heacock L, Reig B, Kang SK, Lewin A, Pysarenko K, Patel S, Samreen N, Rudnicki W, Luczynska E, Popiela T, Moy L, Geras KJ. Improving breast cancer diagnostics with deep learning for MRI. Sci Transl Med. 2022 Sep 28;14(664):eabo4802. doi: 10.1126/scitranslmed.abo4802. Epub 2022 Sep 28. PMID 36170446
• Gao Y, Zeng S, Xu X, Li H, Yao S, Song K, Li X, Chen L, Tang J, Xing H, Yu Z, Zhang Q, Zeng S, Yi C, Xie H, Xiong X, Cai G, Wang Z, Wu Y, Chi J, Jiao X, Qin Y, Mao X, Chen Y, Jin X, Mo Q, Chen P, Huang Y, Shi Y, Wang J, Zhou Y, Ding S, Zhu S, Liu X, Dong X, Cheng L, Zhu L, Cheng H, Cha L, Hao Y, Jin C, Zhang L, Zhou P, Sun M, Xu Q, Chen K, Gao Z, Zhang X, Ma Y, Liu Y, Xiao L, Xu L, Peng L, Hao Z, PMID 35216752
• Amornsiripanitch N, Bickelhaupt S, Shin HJ, Dang M, Rahbar H, Pinker K, Partridge SC. Diffusion-weighted MRI for Unenhanced Breast Cancer Screening. Radiology. 2019 Dec;293(3):504-520. doi: 10.1148/radiol.2019182789. Epub 2019 Oct 8. PMID 31592734
• Baltzer A, Dietzel M, Kaiser CG, Baltzer PA. Combined reading of Contrast Enhanced and Diffusion Weighted Magnetic Resonance Imaging by using a simple sum score. Eur Radiol. 2016 Mar;26(3):884-91. doi: 10.1007/s00330-015-3886-x. Epub 2015 Jun 27. PMID 26115653
• Rahbar H, Zhang Z, Chenevert TL, Romanoff J, Kitsch AE, Hanna LG, Harvey SM, Moy L, DeMartini WB, Dogan B, Yang WT, Wang LC, Joe BN, Oh KY, Neal CH, McDonald ES, Schnall MD, Lehman CD, Comstock CE, Partridge SC. Utility of Diffusion-weighted Imaging to Decrease Unnecessary Biopsies Prompted by Breast MRI: A Trial of the ECOG-ACRIN Cancer Research Group (A6702). Clin Cancer Res. 2019 Mar 15;25(6):1 PMID 30647080

Идентификаторы

NCT: NCT07598084 · 2026-Ζ-52

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗