Combining Sarcopenia and [18F] FDGPET/CT Derived Metabolic Parameters in Patients with Adenocarcinoma Esophageal Cancer Might Improve Outcome Prediction
Bibliographic record
Abstract
Abstract Purpose To determine the prognostic value of sarcopenia measurements done on staging [18F] FDG PET/CT together with metabolic activity of the tumor in patients with adenocarcinoma esophagogastric cancer with surgical treatment. Methods Patients with early stage, surgically treated esophageal adenocarcinoma and available pre-treatment PET/CT were included. The standard uptake value (SUV) and SUV normalized by lean body mass (SUL) were recorded. Skeletal muscle index (SMI) was measured at the L3 level on the CT component of the PET/CT. Sarcopenia was defined as SMI < 34.4cm2/m2 in women and < 45.4cm2/m2 in men. Results Of the included 145 patients. 30% were sarcopenic at baseline. On the univariable Cox proportional hazards analysis, ECOG, surgical T and N staging, Lymphovascular Invasion (LVI) positive lymph nodes and sarcopenia were significant prognostic factors concerning RFS, and OS. On multivariable Cox regression analysis, surgical N staging (p = 0.025) and sarcopenia (p = 0.022) remained significant poor prognostic factors for OS and RFS. Combining the clinical parameters with the imaging derived nutritional evaluation of the patient but not metabolic parameters of the tumor showed improved predictive ability for OS and RFS. Conclusion Combining the patients’ imaging derived sarcopenic status with standard clinical data, but not metabolic parameters offered an overall improved prognostic value concerning OS and RFS.
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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) | 0.001 | 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 | 0.000 |
| 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".