FDG-PET Predicts Neoadjuvant Therapy Response and Survival in Borderline Resectable/Locally Advanced Pancreatic Adenocarcinoma
Bibliographic record
Abstract
BACKGROUND: Neoadjuvant therapy (NAT) is used in borderline resectable/locally advanced (BR/LA) pancreatic ductal adenocarcinoma (PDAC). Anatomic imaging (CT/MRI) poorly predicts response, and biochemical (CA 19-9) markers are not useful (nonsecretors/nonelevated) in many patients. Pathologic response highly predicts survival post-NAT, but is only known postoperatively. Because metabolic imaging (FDG-PET) reveals primary tumor viability, this study aimed to evaluate our experience with preoperative FDG-PET in patients with BR/LA PDAC in predicting NAT response and survival. METHODS: We reviewed all patients with resected BR/LA PDAC who underwent NAT with FDG-PET within 60 days of resection. Pre- and post-NAT metabolic (FDG-PET) and biochemical (CA 19-9) responses were dichotomized in addition to pathologic responses. We compared post-NAT metabolic and biochemical responses as preoperative predictors of pathologic responses and recurrence-free survival (RFS) and overall survival (OS). RESULTS: We identified 202 eligible patients. Post-NAT, 58% of patients had optimization of CA 19-9 levels. Major metabolic and pathologic responses were present in 51% and 38% of patients, respectively. Median RFS and OS times were 21 and 48.7 months, respectively. Metabolic response was superior to biochemical response in predicting pathologic response (area under the curve, 0.86 vs 0.75; P<.001). Metabolic response was the only univariate preoperative predictor of OS (odds ratio, 0.25; 95% CI, 0.13-0.40), and was highly correlated (P=.001) with pathologic response as opposed to biochemical response alone. After multivariate adjustment, metabolic response was the single largest independent preoperative predictor (P<.001) for pathologic response (odds ratio, 43.2; 95% CI, 16.9-153.2), RFS (hazard ratio, 0.37; 95% CI, 0.2-0.6), and OS (hazard ratio, 0.21; 95% CI, 0.1-0.4). CONCLUSIONS: Among patients with post-NAT resected BR/LA PDAC, FDG-PET highly predicts pathologic response and survival, superior to biochemical responses alone. Given the poor ability of anatomic imaging or biochemical markers to assess NAT responses in these patients, FDG-PET is a preoperative metric of NAT efficacy, thereby allowing potential therapeutic alterations and surgical treatment decisions. We suggest that FDG-PET should be an adjunct and recommended modality during the NAT phase of care for these patients.
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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 | 0.001 | 0.002 |
| 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".