Evaluating the metabolic effects of neoadjuvant treatment in clear cell renal cell carcinoma using hyperpolarised [1- <sup>13</sup> C]pyruvate MRI
Bibliographic record
Abstract
Abstract Despite recent advances, ∼50% of people developing renal cell carcinoma (RCC) will die of the disease. The development of new neoadjuvant therapeutic strategies requires reliable companion biomarkers to measure early and successful response to treatment. Tumour size changes are often late markers of response, but novel imaging-based biomarkers may be more accurate for treatment response prediction. Here we evaluated the potential of hyperpolarised carbon-13 MRI (HP 13 C-MRI) as an emerging clinical imaging technique for assessing response to neoadjuvant treatment in RCC, as part of the WIndow of opportunity in REnal cancer (WIRE) trial. The change in LAC/PYR ratio following treatment was variable across the four patients (mean±S.D. %change = +6±27%). LAC/PYR decreased in the patient treated with cediranib monotherapy (−21%), and in one of the patients receiving combination treatment (−14%). A higher LAC/PYR ratio post-treatment was observed in the second patient receiving combination treatment (+21%) and in the patient receiving olaparib monotherapy (+35%). This is the first study to evaluate the potential of clinical HP 13 C-MRI in assessing early treatment response in renal cancer, which detected metabolic changes following treatment in the absence of significant changes in tumour size. Future studies should assess this finding in larger patient cohorts. Patient summary In this study we used an emerging clinical imaging technique, called hyperpolarised carbon-13 MRI, to visualise how kidney cancer changes with drug treatment before surgery. The method visualised rapid changes in cancer metabolism before substantial changes were seen in tumour size, the latter being the conventional method for detecting response to treatment. Hyperpolarised carbon-13 MRI holds promise in informing clinicians which cancers have successfully responded, and which may benefit from a change in treatment.
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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".