Expression of p63 Protein to Differentiate Benign Prostatic Hyperplasia and Carcinoma of Prostate in Pakistani Population
Bibliographic record
Abstract
AbstractBackground: Prostate cancer is the world’s foremost and second cause of cancer associated death typically in males after lung cancer. Histopathological diagnosis of prostatic hyperplasia and prostatic carcinoma can be challenging. The expression of p63 can be used in diagnosis and differentiation of benign pro-static hyperplasia from carcinoma of prostate.Materials and Methods: We studied sixty prostatic biopsies obtained by TURP and radical prostatectomy. For each case, clinical data was collected. The tissue sections were then diagnosed on basis of routine hematoxylin and eosin. Then immunohistochemical (IHC) analysis was performed on routinely processed, forma-lin-fixed, paraffin embedded tissue. We also analyzed P63 expression in regions of benign prostatic hyperplasia and prostatic adenocarcinoma. Moreover, detailed examination of tissue sections was observed with light microscopy.Results: Mean age of patients with adenocarcinoma was 70 ± 12 years however mean age in prostate hyperplasia cases was 66 ± 8 years. With the use of Receiver Operative Characteristic Curve (ROC), the optimal cut point found of PSA was 30.0ng/ml. The results showed that prostate specific antigen (PSA) at this cutoff had a sensitivity of 71.4%, with specificity of 74.4% and accuracy of 73.3%. So PSA could not be considered reliable independently for the diagnosis of carcinoma of prostate. Benign cases in the present study were exclusively positive for immunohistoche-mical expression of p63 while all the cases of prostatic carcinomas expressed negative pattern of staining.Conclusion: Immunostaining with p63 is useful to differentiate benign prostatic hyperplasia from pro-static carcinoma, so it may be used as valuable tool in the diagnosis of prostatic carcinoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".