Rates of metastatic prostate cancer in newly diagnosed patients: Numbers needed to image according to risk level
Bibliographic record
Abstract
Abstract Background The numbers needed to image to identify pelvic lymph node and/or distant metastases in newly diagnosed prostate cancer (PCa) patients according to risk level are unknown. Methods Relying on Surveillance, Epidemiology, and End Results (2010–2016), we tabulated rates and proportions of patients with (a) lymph node or (b) distant metastases according to National Comprehensive Cancer Network (NCCN) risk level and calculated the number needed to image (NNI) for both endpoints. Multivariable logistic regression analyses were performed. Results Of 145,939 newly diagnosed PCa patients assessable for analyses of pelvic lymph node metastases (cN1), 4559 (3.1%) harbored cN1 stage: 13 (0.02%), 18 (0.08%), 63 (0.3%), 512 (2.8%), and 3954 (14.9%) in low, intermediate favorable, intermediate unfavorable, high, and very high‐risk levels. These resulted in NNI of 4619, 1182, 319, 35, and 7, respectively. Of 181,109 newly diagnosed PCa patients assessable for analyses of distant metastases (M1 a–c ), 8920 (4.9%) harbored M1 a–c stage: 50 (0.07%), 45 (0.1%), 161 (0.5%), 1290 (5.1%), and 7374 (22.0%) in low, intermediate favorable, intermediate unfavorable, high, and very high‐risk. These resulted in NNI of 1347, 602, 174, 20, and 5, respectively. Conclusions Our observations perfectly validated the NCCN recommendations for imaging in newly diagnosed high and very high‐risk PCa patients. However, in unfavorable intermediate‐risk PCa patients, in whom bone and soft tissue imaging is recommended, the NNI might be somewhat elevated to support routine imaging in clinical practice.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".