GRAPPATINI T2 Mapping to Predict Dopamine Agonist Resistance in Prolactinomas: A Pilot study

Motivation: Managing dopamine agonist (DA) resistance in prolactinoma is challenging, with no reliable imaging markers for prediction. Goal(s): This study used accelerated quantitative T2 mapping (GRAPPATINI) to evaluate DA sensitivity in prolactinoma patients. Approach: This cross-sectional study analyzed the diagnostic performance of T2 values with multiple echo times (TEs) and signal intensity (SI) ratios to predict drug sensitivity between drug-resistant and sensitive groups. Results: The study elucidated significantly reduced T2 values in patients with DA-resistant prolactinoma, reaching an area under the curve (AUC) of 0.850 for accurately distinguishing DA sensitivity, with pronounced differences observed at TE38ms. Conversely, T2 SI ratios showed no statistical significance. Impact: This pilot study was novel in using GRAPPATINI in prolactinoma. Quantitative T2 values of tumors were lower in patients with DA-resistant prolactinoma. Hence, T2 values may be a promising predictive imaging biomarker for DA sensitivity.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/1897
Primary Topic
Pituitary Gland Disorders and Treatments
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

GRAPPATINI T2 Mapping to Predict Dopamine Agonist Resistance in Prolactinomas: A Pilot study

Thomas J. Hwang, Tom Hilbert, Lei Ji, Weijun Tang et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Pituitary Gland Disorders and Treatments
article

GRAPPATINI T2 Mapping to Predict Dopamine Agonist Resistance in Prolactinomas: A Pilot study

Thomas J. Hwang, Tom Hilbert, Lei Ji, Weijun Tang, Bin Lü, Rong Lu, Ying‐Hua Chu, Tobias Kober, He Wang, Li Q, Shuzhen Shi, Caixia Fu
article en

Abstract

Motivation: Managing dopamine agonist (DA) resistance in prolactinoma is challenging, with no reliable imaging markers for prediction. Goal(s): This study used accelerated quantitative T2 mapping (GRAPPATINI) to evaluate DA sensitivity in prolactinoma patients. Approach: This cross-sectional study analyzed the diagnostic performance of T2 values with multiple echo times (TEs) and signal intensity (SI) ratios to predict drug sensitivity between drug-resistant and sensitive groups. Results: The study elucidated significantly reduced T2 values in patients with DA-resistant prolactinoma, reaching an area under the curve (AUC) of 0.850 for accurately distinguishing DA sensitivity, with pronounced differences observed at TE38ms. Conversely, T2 SI ratios showed no statistical significance. Impact: This pilot study was novel in using GRAPPATINI in prolactinoma. Quantitative T2 values of tumors were lower in patients with DA-resistant prolactinoma. Hence, T2 values may be a promising predictive imaging biomarker for DA sensitivity.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 66%
Pituitary Gland Disorders and Treatments
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.