Predictive Role of <scp>FDG PET</scp>‐<scp>CT</scp> in Localised Rectal Carcinoma: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Introduction Rectal carcinoma (RC) has high incidence and rate of recurrence. Currently, routine 18‐ fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET‐CT) is not recommend for routine monitoring for post RC treatment. We examined the utility of FDG PET‐CT for the prognostication of patients with RC and what FDG PET‐CT metrics are of value. Methods PubMed, Embase, MEDLINE, and Cochrane (Central) were comprehensively searched till 19 May 2024. A modified Newcastle Ottawa scale was used to assess for study bias. We presented our systematic review alongside pooled hazard ratios (HR) for maximum standardised uptake values (SUV) as a predictor of disease‐free survival (DFS) and overall survival (OS). Results Eleven papers including 771 patients were included in our systematic review. Considering the current evidence, there is potential to consider percentage change in SUVmax, TLG, MTV, and lymph node highest peak SUV as possible predictors of outcome for localised non metastatic rectal carcinoma. Conclusions Pooled meta‐analysis of three homogenous parameters examines the relationship of SUVMax and survival, and did not demonstrate correlation with survival outcomes. The overall pooled hazard ratio for pretreatment SUVMax to DFS was 0.69, CI (0.29–1.63). The overall pooled HR for post treatment SUVMax to DFS was 0.88, CI (0.43–1.81), and posttreatment SUVMax to OS was 1.73, CI (0.34–8.66). Post treatment FDG PET‐CT may have a role to play in the prognostic evaluation of RC patients; however, further data is required.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".