Shota Tanaka

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.