Metabolic Imaging of Prostate Cancer Reveals Intrapatient Intermetastasis Response Heterogeneity to Systemic Therapy
Bibliographic record
Abstract
Although intrapatient heterogeneity of prostate cancer (PCa) has recently been characterized via genomic and transcriptomic studies, the heterogeneity of systemic treatment responses has yet to be reported or imaged. Our objective was to evaluate the intrapatient intermetastasis response to systemic treatment among patients with metastatic PCa. We evaluated the metabolic response for each individual metastatic lesion ( n =165) in 15 patients with metastatic PCa who underwent 18 F-fluorodeoxyglucose positron emission tomography/computed tomography before and at least 3 mo after initiation of a systemic therapy that did not change in that period. Intermetastasis heterogeneity was defined as opposite metabolic responses for at least two metastases from the same compartment (bone or soft tissue) between the two time points. We found intrapatient intermetastasis response heterogeneity in 40% of the cases in our retrospective series. Our results suggest that systemic therapies can induce heterogeneous responses among individual metastases in patients with PCa, supporting the polyclonal evolution of PCa in advanced disease. Molecular imaging may thus be useful in identifying clinical resistance early after therapy initiation and could also allow targeted biopsy of resistant clones for molecular analysis. Patient summary Systemic therapies can lead to heterogeneous responses in individual metastases of prostate cancer in a patient. Molecular imaging may be useful for identifying heterogeneity and could allow targeted biopsy for molecular analysis or therapy.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".