Prognostic value of [18F]FDG PET/CT on treatment response and progression-free survival of gastroesophageal cancer patients undergoing perioperative FLOT chemotherapy
Bibliographic record
Abstract
Purpose To evaluate the prognostic role of [ 18 F]FDG PET/CT metabolic parameters in gastric cancer (GC) and gastroesophageal adenocarcinoma (GEJAC) patients receiving neoadjuvant chemotherapy. Method In this retrospective study, 31 patients with biopsy-proven GC or GEJAC were included between August 2016 and March 2020. [ 18 F]FDG PET/CT was performed before the neoadjuvant chemotherapy. Primary tumours' semi-quantitative metabolic parameters were extracted. All patients received a perioperative FLOT regimen thereafter. Post-chemotherapy [ 18 F]FDG PET/CT was performed in most patients (17/31). All patients underwent surgical resection. Histopathology response to treatment and progression-free survival (PFS) were evaluated. Two-sided p-values < 0.05 were considered statistically significant. Results Thirty-one patients (mean age = 62 ± 8), including 21 GC and 10 GEJAC patients, were evaluated. 20/31(65%) patients were histopathology responders to neoadjuvant chemotherapy, including twelve complete and eight partial responders. During the median follow-up of 42.0 months, nine patients experienced recurrence. The median PFS was 60(95% CI:32.9–87.1) months. Pre-neoadjuvant chemotherapy SULpeak was significantly correlated with pathological response to treatment (p-value = 0.03;odds ratio = 16.75). In survival analysis, SUVmax (p-value = 0.01;hazard ratio[HR] = 1.55), SUVmean (p-value = 0.04;HR = 2.73), SULpeak (p-value < 0.001;HR = 1.91) and SULmean (p-value = 0.04;HR = 4.22) in the post-neoadjuvant chemotherapy pre-operative [ 18 F]FDG PET/CT showed significant correlation with PFS. Additionally, aspects of staging were significantly correlated with PFS (p-value = 0.01;HR = 2.21). Conclusions Pre-neoadjuvant chemotherapy [ 18 F]FDG PET/CT parameters, especially SULpeak, could predict the pathological response to treatment in GC and GEJAC patients. Additionally, in survival analysis, post-chemotherapy metabolic parameters significantly correlated with PFS. Thus, performing [ 18 F]FDG PET/CT before chemotherapy may help to identify patients at risk for inadequate response to perioperative FLOT and, after chemotherapy, may predict clinical outcomes.
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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".