MP61-17 CHARACTERIZING CHANGES IN MUSCLE MASS AFTER RADICAL NEPHRECTOMY FOR STAGE II-IV CLEAR CELL RENAL CELL CARCINOMA
Bibliographic record
Abstract
You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging/Surveillance III (MP61)1 Sep 2021MP61-17 CHARACTERIZING CHANGES IN MUSCLE MASS AFTER RADICAL NEPHRECTOMY FOR STAGE II-IV CLEAR CELL RENAL CELL CARCINOMA Mouneeb Choudry, Suzanne Lange, Matthew Covington, Jacob Ambrose, Heidi Hanson, Christopher Dechet, Brock O'Neil, Helena Furberg, Adriana Coletta, Jennifer Ose, Jeffrey Yap, Cornelia Ulrich, Jonathan Chipman, and Alejandro Sanchez Mouneeb ChoudryMouneeb Choudry More articles by this author , Suzanne LangeSuzanne Lange More articles by this author , Matthew CovingtonMatthew Covington More articles by this author , Jacob AmbroseJacob Ambrose More articles by this author , Heidi HansonHeidi Hanson More articles by this author , Christopher DechetChristopher Dechet More articles by this author , Brock O'NeilBrock O'Neil More articles by this author , Helena FurbergHelena Furberg More articles by this author , Adriana ColettaAdriana Coletta More articles by this author , Jennifer OseJennifer Ose More articles by this author , Jeffrey YapJeffrey Yap More articles by this author , Cornelia UlrichCornelia Ulrich More articles by this author , Jonathan ChipmanJonathan Chipman More articles by this author , and Alejandro SanchezAlejandro Sanchez More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002101.17AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Sarcopenia (low skeletal muscle mass) is a poor prognostic factor in patients undergoing nephrectomy for localized renal cancer. However, little is known about the trajectory of muscle mass change after surgery. Here, we characterize post-operative changes in muscle mass using a retrospective cohort of patients with clear cell renal cell carcinoma (ccRCC). METHODS: A total of 117 patients with stage II-IV ccRCC who underwent radical nephrectomy from 02/2014-09/2019 at the University of Utah were included. Skeletal muscle (SM) mass area and measures of adiposity (visceral and subcutaneous tissue area) were quantified using standard of care computed tomography (CT) images and Slice-o-matic software (Montreal, Canada). Sarcopenia (yes/no) was classified according to gender-specific international consensus definitions (SMI of <55cm2/m2 for men and <39 cm2/m2 for women). CT images were obtained within 4 months of surgery and at least one scan within 18 months after surgery. RESULTS: Median age 64 (IQR: 56-71), 74% male, 87% White, 69% had stage II-III and 31% had stage IV disease. At baseline, 48% were obese and 54% were considered sarcopenic. Among 80 patients with evaluable skeletal muscle area, 62 patients maintained muscles mass (± 1 SD, 78%) after surgery and 5 had significant muscle deterioration (≥ 2 SD, 6%). CONCLUSIONS: Among patients with stage II-IV clear cell renal cancer undergoing nephrectomy, post-operative muscle loss was present among 22% of patients and significant muscle loss in 6%. Patients with post-operative muscle wasting after surgery may be an ideal group to target with lifestyle interventions. Among a larger cohort of patients, we plan to assess the association of muscle loss with survival outcomes. Source of Funding: The research reported in this publication was supported by Huntsman Cancer Foundation. © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e1090-e1090 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Mouneeb Choudry More articles by this author Suzanne Lange More articles by this author Matthew Covington More articles by this author Jacob Ambrose More articles by this author Heidi Hanson More articles by this author Christopher Dechet More articles by this author Brock O'Neil More articles by this author Helena Furberg More articles by this author Adriana Coletta More articles by this author Jennifer Ose More articles by this author Jeffrey Yap More articles by this author Cornelia Ulrich More articles by this author Jonathan Chipman More articles by this author Alejandro Sanchez More articles by this author Expand All Advertisement Loading ...
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".