Abstract 5886: Pre-treatment adiposity measured by computed tomography and survival of women with high-grade serous ovarian cancer
Bibliographic record
Abstract
Abstract The association of body mass index (BMI) with survival of women with ovarian cancer remains unclear due to mixed epidemiological evidence. This may be due, in part, to the fact that BMI is an imperfect measure of body fat as BMI does not distinguish weight from lean muscle versus adipose tissue. Here, we investigated the association of adiposity measured by computed tomography (CT) with survival among the most common histotype of ovarian cancer, high-grade serous ovarian cancer (HGSOC). The present study included 383 women diagnosed with HGSOC from 2008 to 2019 who were evaluated at H. Lee Moffitt Cancer Center and Research Institute and had pre-treatment computed tomography scans available for analysis. The sliceOmatic v5.0 rev13 (Tomovision, Magog, Canada) medical image analysis software and accompanying ABACS module for segmentation was used to quantify subcutaneous (SAT), visceral (VAT), and intermuscular adipose tissue (IMAT) from the third lumbar (L3) axial slice including the transverse processes. We used Cox proportional hazard regression to estimate hazard ratios (HR) and 95% confidence intervals (CIs) for the association of each measure of adiposity with overall survival (OS) and recurrence-free survival (RFS) while adjusting for age at diagnosis, stage, race and ethnicity, and first-line treatment. The degree of ascites was included in the VAT models as ascites fluid density can mask VAT. We also assessed these associations within first-line treatment groups (upfront chemotherapy [n=147], upfront surgery [n=236]). In the overall study population, we observed a positive but not statistically significant association with OS and RFS for the highest vs. lowest tertile of IMAT (HR= 1.18, 95% CI=0.83, 1.67 and HR=1.16, 95% CI=0.85, 1.58, respectively). Among women who received upfront surgery, the highest tertile of IMAT was associated with a 57% increased risk of recurrence compared to the lowest tertile (HR=1.57, 95% CI=1.04, 2.37), while the association between IMAT and OS was similar to the findings in the overall population (HR=1.14, 95% CI=0.73, 1.78). No association was observed between IMAT and OS or RFS among women who received upfront chemotherapy. No associations with OS or RFS were observed for SAT or VAT overall or within first-line treatment groups. In summary, we observed inferior RFS among HGSOC patients with higher IMAT. These findings suggest that IMAT measured from standard-of-care imaging may represent a biomarker of recurrence among HGSOC patients, and incorporating lifestyle and behavioral changes (e.g., diet, exercise) to decrease IMAT may be warranted for this patient population. Citation Format: Christelle Colin-Leitzinger, Daniel Jeong, Mahmoud Abdalah, Rikki Cannioto, Jing-Yi Chern, Evan Davis, Robert Gillies, Melissa McGettigan, Jaileene Perez-Morales, Natarajan Raghunand, Sweta Sinha, Olya Stringfield, Rajwantee Tirbene, Matthew Schabath, Lauren C. Peres. Pre-treatment adiposity measured by computed tomography and survival of women with high-grade serous ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5886.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".