Sarcopenia as a predictor of survival in patients with epithelial ovarian cancer (EOC) receiving platinum and taxane-based chemotherapy.
Bibliographic record
Abstract
e17030 Background: Severe skeletal muscle loss (sarcopenia) is associated with poor cancer outcomes, including reduced survival and increased treatment toxicity. This relationship has recently been demonstrated in women with metastatic breast cancer, but there is a paucity of data regarding this correlation in women with EOC. Thus, our goal was to evaluate if sarcopenia, as assessed by computed tomography (CT) morphometric measurements, was associated with worse survival outcomes in EOC patients undergoing primary platinum and taxane-based chemotherapy. Methods: EOC patients diagnosed between 06/2000 and 02/2017 who received treatment with platinum and taxane-based chemotherapy were included. CT abdominal images closest to the time of diagnosis were retrospectively evaluated for skeletal muscle area at the 3rd lumbar vertebrae. Measurements were obtained with use of TomoVision® radiological software (SliceOmatic – version 5.0, Quebec, Canada). Sarcopenia was defined as Skeletal Muscle Index (SMI = SMA/height2) ≤ 41. Data analysis included Kaplan-Meier plots to assess survival, and descriptive statistics was utilized to describe characteristics between the two groups. Results: 201 EOC patients were evaluated. Sixty-four percent (128/201) met criteria for sarcopenia (SMI ≤ 41) at time of diagnosis. Seventy-six percent of patients were diagnosed with Stage III or IV disease, with high-grade serous as the most common histology (74%). Median age at diagnosis was 61 years. Approximately one third were obese. Body mass index was greater in the SMI > 41 group compared to the SMI ≤ 41 group (31.3 vs 26.3, p < 0.001). There was no difference in the prevalence of chronic conditions, including diabetes, coronary artery disease, hypertension, chronic kidney disease, or tobacco use, between the two groups. The mean overall survival did not differ between patients with SMI > 41 and SMI ≤ 41 (36.5 vs 40.8 months, p = 0.4, respectively). Conclusions: Based on this patient cohort, sarcopenia was not associated with worse survival outcomes in EOC patients receiving first-line platinum and taxane-based chemotherapy. Further prospective studies are needed to explore other diagnostics that may allow us to provide improved accuracy and individualization in the care of women with advanced ovarian cancer.
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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 | 0.001 | 0.001 |
| 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".