Investigator

Aki Kido

Associate Professor · Toyama University, Department of Radiology

About

AKAki Kido
Papers(5)
Preoperative Imaging …MRI-based Radiomics M…Multiparametric magne…Editorial for “A Mult…Differentiation of ut…
Collaborators(10)
Yuki HimotoAzusa SakuraiHisamitsu TakayaIkuko EmotoKana AkagiKaoru AbikoKentaro IshidaKoji YamanoiKosuke MurakamiMasahiro Sumitomo
Institutions(8)
Toyama University Hos…京都大学 / Kyoto Universi…Unknown InstitutionKindai UniversityKyoto Medical CenterOsaka National Hospit…Osaka Red Cross Hospi…Tenri Hospital

Papers

Preoperative Imaging Evaluation of Endometrial Cancer in FIGO 2023

The staging of endometrial cancer is based on the International Federation of Gynecology and Obstetrics (FIGO) staging system according to the examination of surgical specimens, and has revised in 2023, 14 years after its last revision in 2009. Molecular and histological classification has incorporated to new FIGO system reflecting the biological behavior and prognosis of endometrial cancer. Nonetheless, the basic role of imaging modalities including ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography, as a preoperative assessment of the tumor extension and also the evaluation points in CT and MRI imaging are not changed, other than several point of local tumor extension. In the field of radiology, it has also undergone remarkable advancement through the rapid progress of computational technology. The application of deep learning reconstruction techniques contributes the benefits of shorter acquisition time or higher quality. Radiomics, which extract various quantitative features from the images, is also expected to have the potential for the quantitative prediction of risk factors such as histological types and lymphovascular space invasion, which is newly included in the new FIGO system. This article reviews the preoperative imaging diagnosis in new FIGO system and recent advances in imaging analysis and their clinical contributions in endometrial cancer.Evidence Level4Technical EfficacyStage 3

Multiparametric magnetic resonance imaging facilitates the selection of patients prior to fertility-sparing management of endometrial cancer

To compare the diagnostic performance of biparametric magnetic resonance imaging (bpMRI) versus multiparametric MRI (mpMRI) for the staging of well-differentiated endometrioid endometrial cancer (EC) in potential candidates for fertility-sparing management. This multi-center retrospective study included 48 potential candidates for fertility-sparing management (age <46 years, grade 1 endometroid EC) who did not wish to undergo fertility-sparing management and thus underwent definitive surgery. Two readers (R1, R2) independently reviewed bpMRI (T1, T2, and diffusion-weighted imaging) and mpMRI (bpMRI and dynamic contrast-enhanced imaging, DCE) during two separate sessions spaced one month apart for the presence of myometrial invasion (MI), cervical stromal involvement (CSI), malignant adnexal disease (mAD), and pelvic lymphadenopathy (pLNM). Each reader also recorded maximum tumor diameter, tumor volume, and tumor-to-uterine volume ratio (TVR) on T2-weighted imaging. The diagnostic performance of bpMRI and mpMRI was determined for each reader with surgical pathology serving as a gold standard. The area under the receiver operating curve (AUC) for bpMRI versus mpMRI was 0.76/0.78 (R1/R2) versus 0.84/0.83 for MI, 0.79/0.76 versus 0.99/0.80 for CSI, 0.84/0.84 versus 0.84/0.80 for mAD, and 0.82/0.82 for pLMN. The sensitivity and specificity of MRI for detecting tumor spread beyond the endometrium were 71%/77% and 71%/65% for bpMRI (R1/R2) vs. 84%/90% and 71%/65% for mpMRI (R1/R2), respectively. The AUC of maximum tumor diameter, tumor volume, and TVR for MI was 0.71/0.61, 0.73/0.75, and 0.75/0.77 for R1/R2, respectively. MRI had moderate diagnostic performance across potential candidates for fertility-sparing treatment of EC. mpMRI outperformed bpMRI for detecting EC spreading beyond the endometrium.

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.

54Works
5Papers
20Collaborators

Positions

2023–

Associate Professor

Toyama University · Department of Radiology

2022–

Lecturer

Kyoto University · Department of Diagnostic Radiology and Nuclear Medicine, Graduate School of Medicine

2022–

講師 / Senior Lecturer/ Junior Associate Professor

京都大学 / Kyoto University · 医学研究科 / Graduate Schools of Medicine

2016–

助教 / Assistant Professor

京都大学 / Kyoto University · 医学部附属病院 / Kyoto University Hospital

2014–

特定助教 / Program-Specific Assistant Professor

京都大学 / Kyoto University · 医学部附属病院 / Kyoto University Hospital

2010–

特定助教(産官学連携)

京都大学 / Kyoto University · 医学部附属病院 / Kyoto University Hospital

Country

JP