Investigator

Kentaro Ishida

Osaka Red Cross Hospital

KIKentaro Ishida
Papers(2)
Synchronous ovarian a…Differentiation of ut…
Collaborators(10)
Koji YamanoiKosuke MurakamiMasahiro SumitomoMasaki MandaiMotonori MatsubaraNaoki HorikawaNoriomi MatsumuraTakahito AshiharaTomoyuki OtaniYasuhisa Kurata
Institutions(6)
Osaka Red Cross Hospi…京都大学 / Kyoto Universi…Kindai University Fac…Tenri HospitalToyooka HospitalKyoto University

Papers

Differentiation of uterine fibroids and sarcomas by MRI and serum LDH levels: a multicenter study of the KAMOGAWA study

In the differential diagnosis between uterine fibroids and uterine sarcomas, real-world magnetic resonance imaging (MRI) diagnostic information is scarce; furthermore, high diagnostic sensitivity is important in clinical practice. We previously developed a diagnostic algorithm to detect uterine sarcoma with high sensitivity using simple MRI images and serum lactate dehydrogenase (LDH) levels. In this multicenter study, we investigated the preoperative diagnosis of sarcoma in the real world and further validated the usefulness of our diagnostic algorithm. Of 154 uterine sarcomas and 154 uterine fibroids treated at 15 centers between January 2006 and December 2020, 139 sarcomas (16 smooth muscle tumors of uncertain malignant potential) and 141 fibroids with diffusion-weighted imaging information were included in the analysis. The diagnostic algorithm was validated by 3 radiologists who were blinded to the clinical information and pathologic diagnoses and who read the MRIs. The sensitivity/specificity of preoperative diagnosis was 77.7%/92.9% for the preoperative report; 92.1%/72.3% for algorithm A; and 82.0%/85.8% for algorithm B (McNemar's test p<0.05). Comparison of overall survival rates among 3 groups (Group 1: negative A, Group 2: positive A and negative B; Group 3: positive B) using algorithms A and B showed p=0.012. On multivariate analysis, stage, and serum LDH level were independent prognostic factors. MRI is useful for preoperative diagnosis of uterine sarcoma, and the sarcoma diagnostic algorithm presented in this study is an option for diagnosing sarcoma with greater sensitivity. This information should be shared with patients.

18Works
2Papers
20Collaborators

Education

2024

Kyoto University

2016

Kyoto University

Links & IDs
0000-0001-8218-4373

Scopus: 58040536200

Researcher Id: ISU-6056-2023