Deep Learning-Based Body Composition Analysis for Cancer Patients Using Computed Tomographic Imaging.
Malnutrition is a commonly observed side effect in cancer patients, with a 30–85% worldwide prevalence in this population. Existing malnutrition screening tools miss ~ 20% of at-risk patients at initial screening and do not capture the abnormal body composition phenotype. Meanwhile, the gold-standar...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 2281 - 2294 |
|---|---|
| Autores principales: | , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187278990&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278990 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278990 187278990 187278990 10.1007/s10278-024-01373-7 187278990 ppf: 2281 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning-Based Body Composition Analysis for Cancer Patients Using Computed Tomographic Imaging. aug: au: Yıldız Potter, İlkay Velasquez-Hammerle, Maria Virginia Nazarian, Ara Vaziri, Ashkan affil: https://ror.org/01s2ng935 BioSensics, LLC, 57 Chapel Street, 02458, Newton, MA, USA sug: subj: Deep Learning Body Composition Cancer Patients Tomography, X-Ray Computed Malnutrition Diagnosis Abdomen Pelvis Human Male Female Adult Middle Age Aged Aged, 80 and Over Convolutional Neural Networks Retrospective Design Descriptive Statistics Post Hoc Analysis Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Malnutrition is a commonly observed side effect in cancer patients, with a 30–85% worldwide prevalence in this population. Existing malnutrition screening tools miss ~ 20% of at-risk patients at initial screening and do not capture the abnormal body composition phenotype. Meanwhile, the gold-standard clinical criteria to diagnose malnutrition use changes in body composition as key parameters, particularly body fat and skeletal muscle mass loss. Diagnostic imaging, such as computed tomography (CT), is the gold-standard in analyzing body composition and typically accessible to cancer patients as part of the standard of care. In this study, we developed a deep learning-based body composition analysis approach over a diverse dataset of 200 abdominal/pelvic CT scans from cancer patients. The proposed approach segments adipose tissue and skeletal muscle using Swin UNEt TRansformers (Swin UNETR) at the third lumbar vertebrae (L3) level and automatically localizes L3 before segmentation. The proposed approach involves the first transformer-based deep learning model for body composition analysis and heatmap regression-based vertebra localization in cancer patients. Swin UNETR attained 0.92 Dice score in adipose tissue and 0.87 Dice score in skeletal muscle segmentation, significantly outperforming convolutional benchmarks including the 2D U-Net by 2–12% Dice score (p-values < 0.033). Moreover, Swin UNETR predictions showed high agreement with ground-truth areas of skeletal muscle and adipose tissue by 0.7–0.93 R2, highlighting its potential for accurate body composition analysis. We have presented an accurate body composition analysis based on CT imaging, which can enable the early detection of malnutrition in cancer patients and support timely interventions. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|