2023 Fiscal Year Final Research Report

Establishment of evaluation method in pretreatment prostate cancer tumor aggressiveness using MRI-US fusion-guided targeted prostate biopsy

Research Project

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Project/Area Number	19K08109
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	Kawasaki Medical School
Principal Investigator	TAMADA Tsutomu 川崎医科大学, 医学部, 教授 (40278932)
Co-Investigator(Kenkyū-buntansha)	宮地禎幸川崎医科大学, 医学部, 教授 (00294463) 山本亮川崎医科大学, 医学部, 准教授 (30319959) 鹿股直樹聖路加国際大学, 聖路加国際病院, 部長 (60263373) 曽根照喜川崎医科大学, 医学部, 教授 (90179383)
Project Period (FY)	2019-04-01 – 2024-03-31
Keywords	前立腺癌 / MRI / MRIガイド下生検 / 腫瘍悪性度 / 拡散強調像
Outline of Final Research Achievements	The accuracy of prostate cancer aggressiveness prior to treatment has a significant impact on subsequent treatment decisions. Recently, MRI-ultrasound fusion-guided prostate targeted biopsy (MRI-guided biopsy) has been covered by health insurance, and its discrimination ability has been improved compared to conventional systematic biopsy, but it is still insufficient. We focused on the ADC map, a diffusion-weighted prostate MRI image that reflects prostate cancer aggressiveness, and designed a clinical study to apply it to MRI-guided biopsy. The ability of MRI-guided biopsy with ADC maps to identify prostate cancer aggressiveness before treatment was a superior diagnostic method that could not be surpassed by machine learning models with the addition of MRI and clinical indicators to MRI-guided biopsy.
Free Research Field	放射線診断学
Academic Significance and Societal Importance of the Research Achievements	治療前の前立腺癌の悪性度はその後の治療法の決定に大きな影響を与えます。我々は、MRI‐超音波融合画像ガイド下前立腺標的生検の際に前立腺MRIの撮像法の一つである拡散強調像（ADC map）の情報を加えた生検方法を考案した。その診断精度は最近話題となっている人工知能（AI）を用いた機械学習モデルでも超えることができない高い診断能を有することが分かった。本研究の成果は、前立腺癌の患者さんに適切な治療を提供するための最適な診断法を明らかにし、今後それが臨床応用されることを示唆した。