Currently, there is lack of a histologic classification of squamous cell carcinoma of the cervix that correlates significantly with patient survival. This study investigated the survival predictive value of two immunohistochemical markers, the blood group A,B,H isoantigens and the Oxford Ca antigen,
Quantitative microvessel density: A staging and prognostic marker for human prostatic carcinoma
β Scribed by Michael K. Brawer
- Publisher
- John Wiley and Sons
- Year
- 1996
- Tongue
- English
- Weight
- 452 KB
- Volume
- 78
- Category
- Article
- ISSN
- 0008-543X
No coin nor oath required. For personal study only.
β¦ Synopsis
Accurate methods of predicting pathologic stage and malignant potential of carcinoma of the prostate are lacking. Such advances would represent major additions to our ability to recommend therapy and offer prognostic information. Quantitation of microvessel density (MVD) has been shown in a number of organ systems to predict tumor aggressiveness.
METHODS.
We and others have investigated this modality in preoperative assessment of pathologic stage and freedom from progression in patients with prostatic carcinoma.
RESULTS.
Unique and useful staging information has been provided and indeed quantitation of MVD has been shown to be a better predictor of the findings of radical prostatectomy than clinical stage, serum prostate specific antigen (PSA), andlor the Gleason score. Applying a variety of therapeutic modalities, quantitative MVD can be shown to provide additional information to predict who will or will not progress. Recent observations have demonstrated that quantitative MVD may be performed on a limited tissue sample provided by prostate core needle biopsy.
CONCLUSIONS.
Quantitative MVD may provide important staging and prognostic information for males with carcinoma of the prostate. The results may influence the recommendation for additional staging studies, provide stratification of patients for neoadjuvant or adjuvant therapy, assist in technical aspects of definitive therapy, and aid in defining optimum monitoring regimens.
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