Diagnostic accuracy of transrectal elastosonography (TRES) imaging for the diagnosis of prostate cancer: a systematic review and meta‐analysis
Bibliographic record
Abstract
What's known on the subject? and What does the study add? Cancer tissue is stiffer than normal tissue, a fact known for many years. This has been measured using ultrasound (US) technology and is termed as elastosonography (ES). There have been reports of this technique being used in the detection of prostate cancer; however, no definite guidelines for its clinical application exist. The present review, for the first time synthesises published data of transrectal ES (TRES) using diagnostic review methodology. TRES increases prostate cancer detection as compared with grey‐scale US. Also, the study highlights limitations and strengths of data in this area and includes recommendations for future research. To assess the diagnostic performance of transrectal elastosonography (TRES) for the detection of prostate cancer. Two reviewers independently extracted the data from each study. Quality was assessed with a validated quality assessment tool for diagnostic accuracy studies. Diagnostic accuracy of TRES in relation to current standard references (transrectal ultrasonography [TRUS] biopsies and histopathology of radical prostatectomy [RP] specimens) was estimated. A bivariate random effects model was used to obtain sensitivity and specificity values. Hierarchical summary receiver operating characteristic (HSROC) were calculated. In all, 16 studies (2278 patients) were included in the review. Using histopathology of the RP specimen as reference standard, the pooled data of four studies showed that the sensitivity of TRES ranged between 0.71 to 0.82 and the specificity ranged between 0.60 to 0.95 (pooled diagnostic odds ratio [DOR] 19.6; 95% confidence interval [CI] 7.7–50.03). The sensitivity varied from 0.26 to 0.87 and specificity varied from 0.17 to 0.76 (pooled DOR 2.141; 95% CI 0.525 to –8.737) using TRUS biopsies (minimum of 10) as a reference standard. The quality of most studies was modest. SROC estimated 0.8653 area under the curve predicting high chances of detecting prostate cancer. There were no health economics or health‐related quality of life of the participants reported in the studies and all the studies used compressional technique with no reported standardisation. The TRES technique appears to improve the detection of prostate cancer compared with systematic biopsy and shows a good accuracy in comparison with histopathology of the RP specimen. However, studies lacked standardisation of the technique, had poor quality of reporting and a large variation in the outcomes based on the reference standards and techniques used.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| 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".