The Added Value of Circulating Tumor Cell Enumeration to Standard Markers in Assessing Prognosis in a Metastatic Castration-Resistant Prostate Cancer Population
Bibliographic record
Abstract
Abstract Purpose: Metastatic castration-resistant prostate cancer (mCRPC) is a heterogeneous disease for which better prognostic models for survival are needed. We examined the added value of circulating tumor cell (CTC) enumeration relative to common prognostic laboratory measures from patients with CRPC. Methods: Utility of CTC enumeration as a baseline and postbaseline prognostic biomarker was examined using data from two prospective randomized registration-directed trials (COU-AA-301 and ELM-PC4) within statistical models used to estimate risk for survival. Discrimination and calibration were used to measure model predictive accuracy and the added value for CTC enumeration in the context of a Cox model containing albumin, lactate dehydrogenase (LDH), PSA, hemoglobin, and alkaline phosphatase (ALK). Discrimination quantifies how accurately a risk model predicts short-term versus long-term survivors. Calibration measures the closeness of actual survival time to the predicted survival time. Results: Adding CTC enumeration to a model containing albumin, LDH, PSA, hemoglobin, and ALK (“ALPHA”) improved its discriminatory power. The weighted c-index for ALPHA without CTCs was 0.72 (SE, 0.02) versus 0.75 (SE, 0.02) for ALPHA + CTCs. The increase in discrimination was restricted to the lower-risk cohort. In terms of calibration, adding CTCs produced a more accurate model-based prediction of patient survival. The absolute prediction error for ALPHA was 3.95 months (SE, 0.28) versus 3.75 months (SE, 0.22) for ALPHA + CTCs. Conclusions: Addition of CTC enumeration to standard measures provides more accurate assessment of patient risk in terms of baseline and postbaseline prognosis in the mCRPC population. Clin Cancer Res; 23(8); 1967–73. ©2016 AACR.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".