Ritse Mann

Tsukasa Saida

This lecture is an educational session on the use of dynamic contrast-enhanced MRI (DCE-MRI) in the diagnosis of gynecologic tumors. The primary focus is on its role within the O-RADS MRI framework for ovarian tumor evaluation. Practical applications, diagnostic value, and common pitfalls will be explained using actual case examples. Advanced DCE techniques, such as quantitative analysis and pharmacokinetic modeling, will also be introduced. Additionally, the lecture briefly discusses the role of DCE-MRI in uterine cancer diagnosis, particularly for tumor staging, aiming to enhance diagnostic accuracy and clinical decision-making in gynecologic oncology.

Tsutomu Tamada, Yu Ueda

Keywords: Body: Urogenital, Contrast mechanisms: Contrast agents, Image acquisition: Sequences

In the management of prostate cancer (PC), it is necessary to efficiently detect clinically significant PC that requires therapeutic intervention, and prostate multiparametric MRI prior to prostate biopsy is an essential diagnostic method in daily clinical practice. This presentation will discuss the evolution of dynamic contrast-enhanced MRI (DCE-MRI), a MRI sequence of prostate multiparametric MRI, the acquisition method, its role in Prostate Imaging Reporting and Data System (PI-RADS), its value as a “safety net” or “backup” sequence, and the validity and points to note regarding the clinical application of biparameter MRI without DCE-MRI.

Azusa Kitao

Keywords: Body: Liver, Contrast mechanisms: Contrast agents

The liver-specific magnetic resonance imaging (MRI) contrast agent, gadoxetic acid, is useful for diagnosing hepatocellular carcinoma (HCC) because it can evaluate both hemodynamics in the dynamic phase and hepatocyte function in the hepatobiliary phase. However, in gadoxetic acid-enhanced MRI, it is difficult to obtain appropriate images in the arterial phase compared to extracellular fluid contrast agent-enhanced MRI. Moreover, contrast enhancement of the liver in the hepatobiliary phase can be influenced by the underlying liver disease. This presentation reveals several points for better scanning and interpretation of the dynamic and hepatobiliary phases of gadoxetic acid-enhanced MRI in HCC diagnosis.

Yoshiyuki Ozawa

Keywords: Body: Lung, Cross-organ: Cancer

Dynamic contrast-enhanced (DCE) MRI is one of the most effective options for evaluating lung and mediastinal tumors. It has demonstrated significant utility in diagnosing lung tumors, distinguishing malignant from benign tumors, classifying lung cancer subtypes, and monitoring treatment effectiveness. Additionally, DCE-MRI has been reported to be useful for diagnosing and classifying mediastinal tumors, particularly thymoma, thymic carcinoma, and lymphoma.

Trang Pham

Noriyuki Fujima

Keywords: Cross-organ: Cancer, Contrast mechanisms: Contrast agents, Contrast mechanisms: Perfusion

DCE is an imaging technique that visualizes tissue perfusion and permeability. While not routinely performed in head and neck imaging, it plays a crucial role in, for example, prognosis prediction in patients with cancer and tumor differentiation. This lecture will provide an overview of the current state and clinical significance of DCE perfusion in the head and neck. Additionally, I also shortly discuss related topics such as deep learning-based reconstruction, advanced parameters like intracellular water lifetime, and the ASL technique to explore their potential contributions to clinical practice.

KyungHyun Sung

Keywords: Body: Breast, Body: Urogenital, Contrast mechanisms: Perfusion

Dynamic Contrast-Enhanced MRI (DCE-MRI) is a crucial imaging tool for assessing vascular properties in in breast and prostate cancers. This presentation will cover DCE-MRI data acquisition, and quantitative and semi-quantitative modeling approaches. We will explore its role in breast and prostate cancer diagnosis, including BI-RADS and PI-RADS classification systems. Challenges such as variability in acquisition protocols, and measurement variability will be discussed. Finally, we will describe emerging AI-driven approaches and future directions in DCE-MRI analysis.