Abstract P3-08-73: Muscle measures, body composition, and function in patients with early breast cancer
Notice bibliographique
Résumé
Abstract Introduction: Sarcopenia and muscle composition are associated with treatment-related toxicities and adverse events in women with EBC (Shachar 2017). We investigated the relationship of muscle mass with other measures of body composition as well as comorbidities, physical function, fatigue, and quality of life. Methods: Women age 21 or older were enrolled in intervention studies (NCT02167932, NCT02328313) investigating home-based walking during chemotherapy for early breast cancer (stage I-III). Prior to the start of chemotherapy, patients completed the Short Physical Performance Battery (SPPB) and the Timed Up and Go (TUG) test. When available from staging, transverse views of computed tomography (CT) through L3 lumbar segments were analyzed using Slice-O-Matic software (Tomovision Quebec, Canada) to ascertain skeletal muscle area (SMA= -29 to +150 Hounsfield Units), skeletal muscle density (SMD= average attenuation of skeletal muscle in HU), skeletal muscle index (SMI= SMA/height2), and skeletal muscle gauge (SMG= SMI x SMD). BSA and BMI were calculated using standard formulas. Descriptive statistics (mean and standard deviation (SD)) were estimated and simple linear regression models were used to evaluate associations of body composition, function and quality of life and continuous muscle measures. Pearson correlation coefficients were estimated to assess the relationship between muscle metrics. Results: In 99 patients, mean age was 56 (SD 13), BMI was 30 (SD 7), 47% were obese (≥30 kg/m2), and 54% had stage III breast cancer. Mean SMI was 45.3 (SD. 8.5), 26% were sarcopenic (SMI <40cm2/m2), mean SMD was 31.2 (SD 9.8), 77% had low SMD (HU <37.8), and mean SMG was 1413.8 (SD 71.2) [normal SMG is >1512 Arbitrary Units]. For each additional comorbidity, mean SMD decreased by 1.91 (p=.003), mean BSA increased by 0.035 (p=.02), and mean BMI increased by 1.31 (p=.005). Lower SMD and SMG were seen for patients with TUG >14 seconds (-5.70, p=.04 and -325.4, p=.02, respectively). The mean SMD increased by 1.22 (p=.02) for each additional point on the SPPB scale. For the correlation between BMI and muscle matrices, there was a strong positive correlation for SMI (+0.648 p<.0001) and a moderate negative correlation for SMD (-0.490 p<.0001). Between BSA and muscle matrices, there was a moderate positive correlation for SMI (+0.433 p<.0001), a strong negative correlation for SMD (-0.572 p <.0001), and a low negative correlation for SMG (-0.282 p=.004). Conclusion: Our study showed that sub-optimal muscle metrics, especially lower muscle mass and density, were correlated worse physical function in women with EBC. We also show that muscle metrics are associated with conventional measures of body composition. Citation Format: Gabriel F. P. Aleixo, Allison M Deal, Grant R Williams, Hyman B Muss, Kirsten A Nyrop, Ji Hye Park, Hyeon Yu, Shlomit S Shachar. Muscle measures, body composition, and function in patients with early breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-08-73.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».