Machine learning algorithms for predicting outcomes after prostate brachytherapy

2020 Fiscal Year Final Research Report

• Project/Area Number: 18K07644
• 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: Keio University
• Principal Investigator: Shiraishi Yutaka 慶應義塾大学, 医学部(信濃町), 講師 (00445339)
• Project Period (FY): 2018-04-01 – 2021-03-31
• Keywords: 放射線治療 / 小線源治療 / 前立腺癌 / 機械学習

Outline of Final Research Achievements

Machine learning classification algorithms for prediction of treatment response are becoming more popular in radiotherapy literature. The purpose of this study is to estimate their discriminative performance for outcome prediction after prostate permanent brachytherapy. Machine learning algorithms yield higher discriminative　performance in toxicity prediction after permanent prostate brachytherapy than single dosimetric parameter. Our results also show that machine learning algorithms can predict clinical recurrence after prostate brachytherapy.

Free Research Field

医学

Academic Significance and Societal Importance of the Research Achievements

本研究で構築したモデルを活用し、前立腺癌小線源治療後の有害事象出現や再発リスクの高い症例をスクリーニングできる可能性が示唆された。特に、臨床的再発部位を予測することで、再発予防に適した後治療（例えば骨盤リンパ節転移の可能性が高い症例には骨盤領域に対する予防照射を行うなど）を検討できる。治療後の有害事象や再発予防につなげることで、個人のQOL向上が期待されるだけでなく、社会全体としての医療費の削減にも寄与できる可能性がある。