Investigator

Shuhei Noda

学内助教 · 獨協医科大学, 病理診断学

SNShuhei Noda
Papers(1)
Impact of immunohisto…
Collaborators(4)
Yoshimasa KawaraiAkira MitsuhashiKazuyuki IshidaMasaki Hirose
Institutions(1)
Dokkyo Medical Univer…

Papers

Impact of immunohistochemistry-based molecular classification with conventional risk stratification on recurrence and survival outcomes in endometrial cancer

The conventional histomorphology-based risk classification for endometrial cancer (EC) does not consider the molecular heterogeneity that influences prognosis and treatment response. The Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) system uses next-generation sequencing to assess DNA polymerase epsilon (POLE) mutations, but its high cost limits its accessibility. This study evaluated the prognostic value of a novel algorithm that combined immunohistochemistry (IHC) testing with conventional risk factors. This retrospective study included 237 patients with stage I-III EC who underwent surgery. Low-risk patients were classified without IHC, while intermediate- and high-risk patients were categorized as MMR-deficient (MMRd), p53-abnormal (p53abn), or nonspecific molecular profile (NSMP) groups based on IHC. Additionally, L1CAM expression was also evaluated. Survival outcomes were analyzed using Kaplan-Meier curves and Cox regression models. Data from 233 cases were analyzed; the median follow-up duration was 63 months. Among 87 low-risk patients, only 1 experienced recurrence. The intermediate- and high-risk groups were subdivided into 42 MMRd, 16 p53abn, and 88 NSMP patients. The 5-year disease-free survival (DFS) rates were 98.8% (low-risk), 94.7% (NSMP), 80.6% (MMRd), and 59.8% (p53abn), highlighting the poorer prognosis of p53abn. p53abn independently predicted recurrence (hazard ratio [HR], 10.1) and mortality (HR, 25.6). L1CAM positivity correlated with worse DFS but was not an independent prognostic factor. Conventional risk classification combined with IHC classification using p53 and MMR is a cost-effective prognostic tool that enables risk stratification and personalized treatment decisions, even when genetic testing is unavailable.

2Works
1Papers
4Collaborators

Positions

2019–

学内助教

獨協医科大学 · 病理診断学

Education

2019

獨協医科大学大学院 · 医学研究科

2014

学士

福井大学 · 医学部