Researcher basic information

• https://researchmap.jp/kenda?lang=en

• https://orcid.org/0000-0002-4398-3292
• https://jglobal.jst.go.jp/detail?JGLOBAL_ID=202601000998494532
• https://endaaman.com
• https://github.com/endaaman

• 71044757

• https://orcid.org/0000-0002-4398-3292

• 202601000998494532

Research activity information

• Transfer Learning Strategies for Pathological Foundation Models: A Systematic Evaluation in Brain Tumor Classification
  Ken Enda; Yoshitaka Oda; Zen‐ichi Tanei; Kenichi Satoh; Hiroaki Motegi; Shunsuke Terasaka; Shigeru Yamaguchi; Takahiro Ogawa; Lei Wang; Masumi Tsuda; Shinya Tanaka
  Pathology International, Feb. 2026, [Peer-reviewed], [Lead author]
  Scientific journal
• Artificial intelligence can extract important features for diagnosing axillary lymph node metastasis in early breast cancer using contrast-enhanced ultrasonography
  Tomohiro Oshino; Ken Enda; Hirokazu Shimizu; Megumi Sato; Mutsumi Nishida; Fumi Kato; Yoshitaka Oda; Mitsuchika Hosoda; Kohsuke Kudo; Norimasa Iwasaki; Shinya Tanaka; Masato Takahashi
  Scientific Reports, 15 Feb. 2025, [Peer-reviewed], [Lead author]
  English, Scientific journal, Abstract
  Contrast-enhanced ultrasound (CEUS) plays a pivotal role in the diagnosis of primary breast cancer and in axillary lymph node (ALN) metastasis. However, the imaging features that are clinically crucial for lymph node metastasis have not been fully elucidated. Hence, we developed a bimodal model to predict ALN metastasis in patients with early breast cancer by integrating CEUS images with the annotated imaging features. The model adopted a light-gradient boosting machine to produce feature importance, enabling the extraction of clinically crucial imaging features. In this retrospective study, the diagnostic performance of the model was investigated using 788 CEUS images of ALNs obtained from 788 patients who underwent breast surgery between 2013 and 2021, with the ground truth defined by the pathological diagnosis. The results indicated that the test cohort had an area under the receiver operating characteristic curve (AUC) value of 0.93 (95% confidence interval: 0.88, 0.98). The model had an accuracy of 0.93, which was higher than the radiologist’s diagnosis (accuracy of 0.85). The most important imaging features were heterogeneous enhancement, diffuse cortical thickening, and eccentric cortical thickening. Our model has an excellent diagnostic performance, and the extracted imaging features could be crucial for confirming ALN metastasis in clinical settings.
• Diagnosis on Ultrasound Images for Developmental Dysplasia of the Hip with a Deep Learning-Based Model Focusing on Signal Heterogeneity in the Bone Region
  Hirokazu Shimizu; Ken Enda; Hidenori Koyano; Takuya Ogawa; Daisuke Takahashi; Shinya Tanaka; Norimasa Iwasaki; Tomohiro Shimizu
  Diagnostics, 07 Feb. 2025, [Peer-reviewed], [Lead author]
  Scientific journal
• Asphyxiation due to obstructive fibrinous tracheal pseudomembrane after closure of repeated tracheostomy in a case of von Recklinghausen disease
  Taiki Hara; Ken Enda; Taku Maeda; Yohei Ikebe; Hideki Ujiie; Masahiro Onozawa
  Respiratory Medicine Case Reports, 2025, [Peer-reviewed]
  English, Scientific journal
• Bimodal machine learning model for unstable hips in infants: integration of radiographic images with automatically-generated clinical measurements
  Hirokazu Shimizu; Ken Enda; Hidenori Koyano; Tomohiro Shimizu; Shun Shimodan; Komei Sato; Takuya Ogawa; Shinya Tanaka; Norimasa Iwasaki; Daisuke Takahashi
  Scientific Reports, 01 Aug. 2024, [Peer-reviewed], [Lead author]
  English, Scientific journal
• Fatal case of subdural empyema caused by Campylobacter rectus and Slackia exigua
  Yuki Munekata; Saki Yamamoto; Shun Kato; Yutaro Kitagawa; Ken Enda; Nanase Okazaki; Satoshi Tanikawa; Zen-ichi Tanei; Yohei Ikebe; Takahiro Osawa; Soichiro Takamiya; Hideki Ujiie; Masahiro Onozawa; Satoshi Hirano; Miki Fujimura; Shinya Tanaka
  Autopsy Case Reports, 2023, [Peer-reviewed]
  Scientific journal
• Machine Learning Algorithms: Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture
  Hirokazu Shimizu; Ken Enda; Tomohiro Shimizu; Yusuke Ishida; Hotaka Ishizu; Koki Ise; Shinya Tanaka; Norimasa Iwasaki
  Journal of Clinical Medicine, 05 Apr. 2022, [Peer-reviewed], [Lead author]
  English, Scientific journal, Background: The number of patients with fragility fracture has been increasing. Although the increasing number of patients with fragility fracture increased the rate of fracture (refracture), the causes of refracture are multifactorial, and its predictors are still not clarified. In this issue, we collected a registry-based longitudinal dataset that contained more than 7000 patients with fragility fractures treated surgically to detect potential predictors for clinical refracture. Methods: Based on the fact that machine learning algorithms are often used for the analysis of a large-scale dataset, we developed automatic prediction models and clarified the relevant features for patients with clinical refracture. Formats of input data containing perioperative clinical information were table data. Clinical refracture was documented as the primary outcome if the diagnosis of fracture was made at postoperative outpatient care. A decision-tree-based model, LightGBM, had moderate accuracy for the prediction in the test and the independent dataset, whereas the other models had poor accuracy or worse. Results: From a clinical perspective, rheumatoid arthritis (RA) and chronic kidney disease (CKD) were noted as the relevant features for patients with clinical refracture, both of which were associated with secondary osteoporosis. Conclusion: The decision-tree-based algorithm showed the precise prediction of clinical refracture, in which RA and CKD were detected as the potential predictors. Understanding these predictors may improve the management of patients with fragility fractures.

• がんゲノム病理学
  田中, 伸哉; 西原, 広史, コラム記載
  文光堂, Apr. 2025, 9784830604980, viii, 245p, Japanese, [Others]

• 日本メディカルAI学会
• 日本病理学会

• WSI Toolbox
  Ken Enda, 17 Nov. 2026 - Present, Toolkit for WSI Analysis, [Software]
• tym
  Ken Enda, 16 Jun. 2017, Lua-configurable terminal emulator, [Software]