The University of Tokyo · 의학
Shota Tanaka 교수의 연구실은 뇌종양 및 연골세포종과 같은 희귀 신경종양의 영상유전체학적 분석을 중심으로, 의료 영상과 기계학습을 융합한 정밀의료 기반 진단 모델 개발에 주력하고 있습니다. 특히, MRI 영상에서 추출한 다차원적 영상 특징을 활용해 종양 간의 미세한 차이를 정량화하고, 수술 전 정확한 진단을 위한 지능형 분석 시스템을 구축하고 있습니다. 또한, 신경종양 분야의 향후 연구 동향을 예측하는 알고리즘 개발을 통해 연구 분야의 방향성 제시에도 기여하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Chordoma and chondrosarcoma share common radiographic characteristics yet are distinct clinically. A radiomic machine learning model differentiating these tumors preoperatively would help plan surgery. MR images were acquired from 57 consecutive patients with chordoma (N = 32) or chondrosarcoma (N = 25) treated at the University of Tokyo Hospital between September 2012 and February 2020. Preoperative T1-weighted images with gadolinium enhancement (GdT1) and T2-weighted images were analyzed. Data
In conducting medical research, a system which can objectively predict the future trends of the given research field is awaited. This study aims to establish a novel and versatile algorithm that predicts the latest trends in neuro-oncology. Seventy-nine neuro-oncological research fields were selected with computational sorting methods such as text-mining analyses. Thirty journals that represent the recent trends in neuro-oncology were also selected. As a novel concept, the annual impact (AI) of
Abstract Johan Hjort's “critical period” hypothesis, which postulates that year‐class strength is determined in the short period following the onset of exogenous feeding, has rarely been supported by empirical data. Instead, the current understanding is that recruitment is determined by cumulative mortality throughout early life. Recent studies relied on the measure of growth autocorrelation derived from otolith daily increment widths to test the link between growth rate achieved during the post
The newly developed MDASI-BT-Japanese has demonstrated feasibility, reliability and validity in evaluation of clinical benefit in Japanese-speaking brain tumor patients.
