Correlation Between mpMRI Staging and Final Surgical Pathology in Prostate Cancer
Bibliographic record
Abstract
Purpose: We evaluated the role of multiparametric magnetic resonance imaging (mpMRI) in the diagnosis of prostate cancer and predicting of surgical staging of prostate cancer. Materials and Methods: The study was done in 110 subjects who got mpMRI before radical prostatectomy in our hospital from 2016 to 2019. Preoperative mpMRI findings of 110 were compared to surgical pathology results following radical Prostatectomy. A comparison was made between pathologic staging of prostate cancer and the mpMRI findings. Results: pathologic evaluation confirmed prostate cancer foci (237) were recognized in 110 subjects. Generally, mpMRI sensitivity of 46.4% was found for prostate cancer detection (110/237). Pathological tumor volume was a significant predictor of prostate cancer detection using mpMRI. In 33% of the cases, the pathologic staging is precisely similar to mpMRI and in 43%of the cases, there was a slight difference between the pathologic staging and staging by mpMRI but the cancer was confined to the prostate.in 24% of the cases, there was a significant difference between the pathologic staging and staging by mpMRI. The mpMRI was not able to identify the significant cancer in 24% of the cases. Conclusion: The preoperative mpMRI was useful in detecting prostate cancer and in predicting surgical staging. However, the detection of 24% of clinically significant cancer was missed using mpMRI. As we move toward personalized medicine, use of MRI to biopsy each man's prostate differently rather than based on a pre-defined 12 core seems to be supported in the recent literature.
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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 | 0.001 | 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 | 0.001 |
| 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".