A Novel Use for Pelvic MRI Scans to Estimate Lean Body Mass and Screen for Sarcopenia in Patients With Rectal Cancer
Notice bibliographique
Résumé
BACKGROUND: Sarcopenia, the combination of low lean body mass and decreased muscle strength, is associated with significant morbidity and mortality among patients with colorectal cancer. Standard methods for assessing lean body mass and muscle strength, such as bioelectric impedance analysis and handgrip dynamometry, are rarely obtained clinically. Per National Cancer Center Network recommendations, pelvic MRI is routinely collected for staging and surveillance among patients with rectal cancer. However, there are no data assessing the relationship of pelvic MRI lean body mass measurements at the fifth lumbar vertebrae with bioelectric impedance analysis, handgrip strength, or abdominal CT in patients with rectal cancer. Therefore, we aimed to assess whether pelvic MRI lean body mass correlates with a standard for lean body mass measurement (bioelectric impedance analysis), muscle function (handgrip strength), and an imaging modality frequently used in the literature to identify sarcopenia (abdominal CT at the third lumbar vertebrae). IMPACT OF INNOVATION: Lean body mass measurements from routinely collected pelvic MRI at the fifth lumbar vertebrae accurately and reproducibly estimate lean body mass and modestly correlate with handgrip strength. Rectal cancer pelvic MRI may be repurposed for identifying sarcopenia without increasing inconvenience, ionizing radiation exposure, or expenditure to patients with rectal cancer. TECHNOLOGY, MATERIALS, AND METHODS: Patients with locally advanced rectal cancer with pretreatment bioelectric impedance analysis and handgrip strength measurements within 3 months of their staging pelvic MRI were eligible. Axial skeletal muscle areas were segmented using T1-weighted series pelvic MRI at the fifth lumbar vertebrae and abdominal CT at the third lumbar vertebrae using Slice-O-Matic (Tomovision, Montreal, Canada). Lean body mass (kilograms) was derived from skeletal muscle area with standard equations. Handgrip strength (kilograms) was the maximum of 3 dominant hand attempts in the standing anatomical position. The primary outcome was the agreement between lean body mass measured by pelvic MRI (at the fifth lumbar vertebrae) and bioelectric impedance analysis. Secondary outcomes included the concordance of pelvic MRI lean body mass (at the fifth lumbar vertebrae) with abdominal CT (at the third lumbar vertebrae) and handgrip strength. Additionally, the intra- and interobserver validity, internal consistency, and the mean difference (bias) between lean body mass measurements by pelvic MRI and bioelectric impedance analysis were evaluated. PRELIMINARY RESULTS: Sixteen patients were eligible. The average lean body mass was similar and consistent across 2 observers between bioelectric impedance analysis and pelvic MRI. There was a strong correlation between lean body mass measured on pelvic MRI, bioelectric impedance analysis, and abdominal CT. The reliability of 2 pelvic MRI lean body mass measurements (2 weeks apart by blinded observers) and the correlation of lean body mass between pelvic MRI and bioelectric impedance analysis was strong. Inter- and intraobserver correlation, reliability, and internal consistency were strong for the entire cohort. There was a moderate correlation between pelvic MRI lean body mass and handgrip strength. CONCLUSIONS: Lean body mass measured at the fifth lumbar vertebrae on pelvic MRI is reproducible and correlates strongly with measurements from bioelectric impedance analysis (standard) and abdominal CT at the third lumbar vertebrae and modestly with handgrip strength. These data suggest that MRI lean body mass measurements may be a method to screen patients with rectal cancer for sarcopenia. FUTURE DIRECTIONS: Future studies may evaluate changes in lean body mass on serial pelvic MRI studies among patients with rectal cancer.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 ».