Pathological classification of desmoplastic reaction is prognostic factor in cervical adenocarcinoma.
Taishi AkimotoAkira TakasawaKumi TakasawaTomoyuki AoyamaMotoki MatsuuraMasato TamateMasahiro IwasakiShutaro HabataTaro MurakamiMakoto OsanaiTsuyoshi SaitoPublished in: Medical molecular morphology (2022)
Desmoplastic reaction (DR) and inflammation are significant pathological manifestations of tumorigenesis in several cancers. However, the correlation between these stromal reactions and cervical adenocarcinoma has been poorly documented. This investigation elucidated whether DR is a prognostic indicator in early cervical adenocarcinoma patients. Fifty-nine patients with early stage cervical adenocarcinoma (stages I/II) were included in the study. DR was divided into three groups, mature, intermediate, and immature, based on the presence of myxoid stroma and hyalinized keloid-like collagen. Inflammatory cell responses were classified as mild, moderate, and severe. Those stromal reactions were separately evaluated in the invasion front stroma and intratumoral stroma. In both the intratumor and invasion front stroma, intermediate/immature DR was correlated with tumor size, T stage, N stage, lymphovascular invasion, and parametrial infiltration (p < 0.001 to p < 0.05). In addition, in the intratumoral stroma, intermediate/immature DR led to short relapse-free survival and overall survival (p < 0.001). In the invasion front stroma, inflammatory cell responses were associated with DR immaturity and FIGO stage (p < 0.01). These results suggest that the classification of DR maturity is a potential prognostic biomarker in early stage cervical adenocarcinoma patients. DR can be evaluated by routine H&E staining without immunohistochemistry, making it convenient and economical in clinical practice.
Keyphrases
- editorial comment
- prognostic factors
- early stage
- free survival
- end stage renal disease
- squamous cell carcinoma
- clinical practice
- cell migration
- newly diagnosed
- ejection fraction
- chronic kidney disease
- machine learning
- locally advanced
- bone marrow
- peritoneal dialysis
- deep learning
- cell therapy
- single cell
- risk assessment
- human health
- young adults
- wound healing