Use of FDG-PET/CT for peritoneal carcinomatosis before hyperthermic intraperitoneal chemotherapy
Bibliographic record
Abstract
BACKGROUND: Peritoneal carcinomatosis (PC) is associated with a very poor prognosis. Complete cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy has been shown to improve survival rates of PC. However, this treatment is beneficial for patients if the complete cytoreductive surgery is macroscopically completed before implementing hyperthermic intraperitoneal chemotherapy. Even so, a strict selection of patients is of fundamental importance because of the invasive nature of the intervention. The aim of this study was to assess the performance of FDG-PET/CT examinations for the diagnosis and evaluation of the extent of PC. METHODS: A retrospective analysis was conducted on 28 consecutive patients with suspected PC, scheduled for a complete cytoreductive surgery and hyperthermic intraperitoneal chemotherapy, and who underwent an FDG-PET/CT examination. We compared the results of PET examinations with histological and intraoperative findings. The extent of PC was assessed precisely using a simplified 'peritoneal cancer index', within the three modalities (PET, surgery and histology). RESULTS: Of 28 patients, 23 had histological PC. The sensitivity and specificity of the PET examination for the diagnosis of PC were, respectively, 82 and 100%. Even if the extent of PC was underestimated by PET, there was a good correlation when compared with histology and intraoperative results. CONCLUSION: PET presented a good performance level in the diagnosis and evaluation of the extent of PC. PET/CT examinations could be useful to avoid unnecessary surgery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".