Interstitial assessment of aggressive prostate cancer by physio‐chemical photoacoustics: An <i>ex vivo</i> study with intact human prostates
Bibliographic record
Abstract
PURPOSE: Transrectal ultrasound (TRUS)-guided biopsy is the standard procedure for evaluating the presence and aggressiveness of prostate cancer. TRUS biopsy involves tissue removal, and suffers from low core yield as well as high false negative rate. A less invasive and more accurate diagnostic procedure for prostate cancer is therefore highly desired. Combining the optical sensitivity and ultrasonic resolution to resolve the spatial distribution of the major molecular components in tissue, photoacoustic (PA) technology could be an alternative approach for the diagnosis of prostate cancer. The purpose of this study was to examine the feasibility of identifying aggressive prostate cancer using interstitial PA measurements. METHODS: Seventeen patients with prebiopsy magnetic resonance imaging (MRI), TRUS biopsies, and planned prostatectomies were enrolled in this study. The interstitial PA measurements were achieved using our recently developed needle PA probe, which was inserted into the ex vivo prostates in the fashion of a biopsy needle. A total of 70 interstitial PA measurements were acquired. The PA measurements were quantified by a previously established PA physio-chemical analysis (PAPCA) method. The histology has confirmed the nonaggressive and aggressive cancerous conditions at the insertion locations. The diagnostic accuracy was also compared to that provided by the prebiopsy MRI. RESULTS: The quantitative study shows significant differences between the individual parameters of the nonaggressive and the aggressive cancerous regions (P < 0.005). Multivariate analysis of the quantitative features achieved a diagnostic accuracy of 78.6% for differentiating nonaggressive and aggressive prostate cancer tissues. CONCLUSIONS: The proposed procedure has shown promises in the diagnosis of aggressive prostate cancer.
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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".