Imaging Biomarkers as New Predictors of Breast Cancer Prognosis Using Diffusion MRI

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K07618
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 52040:Radiological sciences-related
• Research Institution: Kyoto University
• Principal Investigator: Iima Mami 京都大学, 医学研究科, 特定講師 (60748797)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Keywords: 拡散MRI / 乳がん / 予後予測

Outline of Final Research Achievements

By correlating quantitative values from diffusion MRI with clinical information, such as molecular-biological and pathological factors, we explored imaging biomarkers that are versatile in clinical practice and useful for predicting prognosis. Using several mouse tumor models, we demonstrated that diffusion MRI parameters, such as ADC shift and ADC change rate, correlate with the N/C ratio and Ki67 labeling rate. We also explored the potential for tumor apoptosis and differences in therapeutic effects by tumor type by comparing groups treated with an anti-PD-1 antibody to untreated groups. In clinical studies, we compared several types of high-resolution diffusion-weighted images and showed that the quantitative value (kurtosis) calculated using a non-Gaussian diffusion model could be a biomarker for predicting metastasis.

Free Research Field

放射線診断学

Academic Significance and Societal Importance of the Research Achievements

マウス腫瘍モデルにおいてshifted ADCやADC変化率がN/C比やKi67標識率と相関することを示し、拡散MRI定量値ががんにおける病理情報を一部推測可能であることが示唆された。非ガウス拡散の定量値であるK値が高い乳がん患者群でDDFS（無遠隔転移生存期間）が短かったことは、K値が乳がんの予後予測可能なバイオマーカーとなり得ることを示唆する。また異なる拡散強調像（SPEN、SS-EPI、RESOLVE）を用いた評価では、異なる撮影技術が乳房病変の可視性に及ぼす影響を検証し、より高解像度で病変を詳細に評価可能であると考えられる。