Tissue characterization of prostate cancer using quantitative analysis of low frequency ultrasound
Bibliographic record
Abstract
Current accepted methodologies used for detection of the prostate rumor involve measurements of prostate specific antigen (PSA) levels, patient age followed by ultrasound guided biopsies leaving a lot to desire in the ability to correctly identify lesions. Also PSA level test has been shown to produce a high number of false positives leading to unnecessary invasive biopsies. The goal of this thesis is to investigate the use of trans-rectal conventional low frequency (1-10MHz) ultrasound as a non-invasive imaging modality for the detection of prostate tumors. Currently we are investigating the use of multiparameter spectroscopic analysis of the ultrasound radio frequency signal in combination with ultrasound elastrographic imaging of the prostate and correlating the results with whole-mount histopathology from radical prostatectomy. Ten patients with prostate cancer prior to surgery were subjected to trans-rectal conventional low frequency ultrasound scans. Parametric maps are generated for each individual spectral parameter. Ratios of disease area versus normal prostatic tissue are identified using low frequency ultrasound and compared with the equivalent ratios obtained from whole-mount histopathology. Preliminary results show that areas of suspected disease identified by spectral parameters correlate with areas of disease presence in the corresponding whole-mount sections. An initial software platform performing visualization of areas of disease based on parametric maps generated from spectral analysis methods was developed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".