Establishment of a novel test method for early prediction of malignant transformation of endometriosis using MR spectroscopy.

2021 Fiscal Year Final Research Report

• Project/Area Number: 19K09758
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 56040:Obstetrics and gynecology-related
• Research Institution: Nara Medical University
• Principal Investigator: Chiharu Yoshimoto 奈良県立医科大学, 医学部, 研究員 (00526725)
• Co-Investigator(Kenkyū-buntansha): 山田 有紀 奈良県立医科大学, 医学部, 助教 (20588537) ; 小林 浩 奈良県立医科大学, 医学部, 研究員 (40178330) ; 川口 龍二 奈良県立医科大学, 医学部, 准教授 (50382289)
• Project Period (FY): 2019-04-01 – 2022-03-31
• Keywords: 卵巣腫瘍 / 内膜症性嚢胞 / 内膜症関連卵巣癌 / MRS / R2値 / R2 Predictive Index

Outline of Final Research Achievements

MRI can be used to distinguish endometriosis-associated ovarian cancer noninvasively. In this study, we showed that tumor diameter and CEA values can be used in place of MRI to distinguish endometriosis-associated ovarian cancer, and we defined the R2 Predictive Index. By setting the cutoff value to 18.70, we showed that endometriosis cysts and endometriosis-related ovarian cancer can be differentiated with a high accuracy of 83.2% sensitivity and 76.4% specificity. Furthermore, we evaluated the ability of this prediction formula to differentiate benign from malignant ovarian tumors [endometrioid cysts (54 cases) and endometriosis-related ovarian cancer (51 cases)] without MRS in a multivariate analysis, and confirmed that the R2 Predictive Index was an independent predictor.

Free Research Field

卵巣癌

Academic Significance and Societal Importance of the Research Achievements

これまで内膜症と内膜症関連卵巣癌の良悪性鑑別にはMRI検査が有用であることを示してきたが、本研究によりMRI検査を用いなくとも血液検査と超音波などの画像検査で容易に鑑別可能であることを示した。
本成果は容易にMRI検査を行うことができない地域や発展途上国などで良悪性の鑑別に役立ち、世界の卵巣癌の早期発見に貢献できると考える。