An external validation and comparison of the predictive accuracy of four models designed to predict the probability of a positive prostate biopsy
Bibliographic record
Abstract
Objective To validate and compare the predictive accuracy of four prostate cancer models designed to predict the likelihood of a positive initial transrectal biopsy. Methods Clinical data of 813 consecutive patients between January 2010 and September 2014 who had undergone a transrectal ultrasound (TRUS) guided prostate biopsy at our institution were reviewed, 431 patients fulfilling all criteria for four predictive models were enrolled for the final analysis. The risk of each individual positive biopsy was calculated using either of the four models. The predictive accuracy of each model was measured using area under the receiver operating characteristic curve (AUC), and the comparison of AUCs was performed by Z test. Results Of 431 participants, the statistical analysis of age, prostate-specific antigen (PSA), digital rectal examination (DRE), prostate volume and TRUS findings were all significantly different(P<0.05), except percentage of free prostate-specific antigen (%fPSA) (P=0.242) . AUCs were 0.774 (95% CI 0.726-0.822), 0.765 (95% CI 0.714-0.816), 0.813 (95% CI 0.767-0.858), 0.795 (95% CI 0.749-0.842) and 0.736 (95% CI 0.684-0.788) for the North-American prostate cancer prevention trial derived cancer risk calculator (PCPT-CRC) model, Montreal model, domestic model 1, domestic model 2 and PSA alone, respectively. There was no significant difference among AUCs of the four models, and a 7.7% increased predictive accuracy was observed for the domestic model 1 compared to unlimited PSA alone(P<0.05). When serum PSA ranging from 4 to 10 ng/ml, AUCs were 0.688(95% CI 0.560-0.816), 0.818 (95% CI 0.719-0.918), 0.830 (95% CI 0.740-0.919), 0.853(95% CI 0.771-0.935) and 0.565(95% CI 0.419-0.710) for the four models and PSA alone, respectively. Domestic model 2 owned the highest predictive accuracy and a 28.8% increased predictive accuracy was observed for the domestic model 2 compared to PSA alone(P<0.05). Conclusions External validation and comparison of the four models reveals that all of the four models have acceptable predictive accuracy in our cohort. There is no difference of predictive accuracy between foreign and domestic models according to the AUC results. However, domestic model 1 is superior to unlimited PSA alone, and domestic model 2 has the highest predictive accuracy when serum PSA ranging from 4 to 10 ng/ml. Key words: Prostate cancer; Prostate biopsy; Predictive model
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".