Clinical Value of Whole-body FDG-PET for Recurrent Gastric Cancer: A Multicenter Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this multicenter study was to evaluate the clinical usefulness of positron emission tomography (PET) using (18)F-fluorodeoxyglucose (FDG) for suspected recurrent gastric cancer. METHODS: We performed a retrospective review of 92 consecutive patients who underwent PET [either integrated PET/computed tomography (CT) or manual fusion of dedicated PET and CT] scans for post-treatment surveillance of gastric cancer between June 2006 and December 2007. Of these patients, 46 patients were suspected of recurrence by other imaging modalities (Group A), 19 patients were suspected of recurrence by tumor markers without definite findings (Group B) and the remaining 27 patients underwent a PET scan without evidence of recurrence (Group C). The diagnostic performance and prevalence of the clinical impact of FDG-PET were analyzed. RESULTS: Recurrence of gastric cancer was confirmed in 31 patients (67%) in Group A, in 11 patients (58%) in Group B and in 2 patients (7%) in Group C. In addition, colon cancer (n = 3), lung cancer (n = 1) and pulmonary carcinoid (n = 1) were identified in five patients (5%). In patient-basis, the sensitivity, specificity and diagnostic accuracy of PET for recurrence were 81%, 87% and 83%, respectively, in Group A, 73%, 88% and 79%, respectively, in Group B and 50%, 88% and 85%, respectively, in Group C. Therapeutic management was influenced by PET results in 22 patients (48%) in Group A, in 8 patients (42%) in Group B and in 2 patients (7%) in Group C, including cases in which PET was helpful for detecting second primary cancer. CONCLUSIONS: PET with FDG yielded useful information in patients with suspected recurrent gastric cancer, especially when recurrence was suspected in the clinical setting.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".