ArticleJournal of orthopaedic surgery and research2026
AI-derived CT morphometric phenotypes predict survival, functional decline, and surgical morbidity following curative-intent surgical sarcoma resection.
Article in Journal of orthopaedic surgery and research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundOutcomes after curative-intent sarcoma surgery vary substantially and are incompletely explained by tumor-centered factors alone. Although CT-based body composition metrics provide objective host-related information, most sarcoma studies rely on isolated parameters or binary sarcopenia definitions. AI-driven analytical approaches offer the opportunity to integrate multidimensional morphometric data into data-driven phenotypes that may better capture clinically relevant heterogeneity.
methodsIn this retrospective cohort study, our institutional sarcoma database (n = 2667) was screened to identify patients with osteosarcoma, myxofibrosarcoma, liposarcoma, or chondrosarcoma who underwent curative-intent surgical resection and had a preoperative CT including mid-L3 vertebral level for morphometric analysis (final cohort n = 234). Skeletal muscle index (SMI), skeletal muscle density (SMD), and visceral adipose tissue area (VAT) were quantified from a single axial mid-L3 slice. Unsupervised k-means clustering of standardized SMI, SMD, and VAT identified AI-derived morphometric phenotypes. Outcomes included overall survival (OS), ECOG performance status at follow-up, surgical site infection (SSI requiring surgical revision), and length of hospital stay (LOS). Multivariable regression models evaluated independent associations between phenotypes and outcomes, adjusting for relevant clinical covariates.
resultsClustering identified four phenotypes: muscle-preserved (n = 88), myosteatotic (n = 62), sarcopenic (n = 56), and cachexia-like (n = 28). Morphometric profiles differed markedly: muscle-preserved (SMI 47.8 ± 6.3 cm
conclusionsAI-derived CT morphometric phenotypes obtained from routine preoperative imaging identify distinct host profiles in sarcoma patients and independently predict survival, postoperative functional decline and postoperative morbidity beyond conventional CT-based sarcopenia assessments. Integrating morphometric phenotyping into preoperative assessment may support risk stratification, counseling, and targeted perioperative optimization in curative-intent sarcoma surgery.
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