Related Experiment Video
Updated: Aug 13, 2025

Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
Published on: August 1, 2018
Prognostic value of nuclear morphometry in myxoid liposarcoma
Kengo Kawaguchi1,2, Kenichi Kohashi1, Takeshi Iwasaki1
1Department of Anatomic Pathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Myxoid liposarcoma prognosis is better predicted by nuclear morphology grading than cellularity. A modified Fuhrman system identified high-nuclear-grade tumors with significantly poorer disease-free survival in patients.
Area of Science:
- Oncology
- Pathology
- Genetics
Background:
- Myxoid liposarcoma (MLS) is a common soft tissue sarcoma.
- The round cell component (RCC) is a suspected poor prognostic factor in MLS.
- Current RCC assessment criteria lack standardization, leading to inconsistent pathological evaluation.
Purpose of the Study:
- To refine and apply established grading systems for nuclear atypia in MLS.
- To investigate the prognostic value of nuclear morphology and cellularity in MLS.
- To correlate grading with molecular markers and patient survival.
Main Methods:
- Modified World Health Organization/International Society of Urological Pathology and Fuhrman grading systems applied to 64 MLS cases.
- Software-based analysis of morphology and cellularity.
- DNA mutation analysis, mRNA expression profiling, and immunohistochemistry.
Main Results:
- High-nuclear-grade MLS, assessed by the modified Fuhrman system, showed significantly poorer disease-free survival (HR: 4.43, p=0.047).
- Increased cellularity was observed in high-grade tumors but was not an independent prognostic factor.
- High-grade tumors exhibited significant upregulation of cell cycle genes (FOXM1, PLK1, CDK1).
Conclusions:
- Nuclear morphology, evaluated using a modified Fuhrman grading system, is a more reliable predictor of MLS prognosis than cellular density.
- This grading approach aids in identifying patients with a poorer prognosis, potentially guiding treatment decisions.
More Related Videos
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
08:57Author Spotlight: Genetically Engineered Mouse Models and Pathological Characterization of Neurofibromatosis Type 1 Associated Tumors
Published on: May 17, 2024