AI-derived CT morphometric phenotypes predict survival, functional decline, and surgical morbidity following curative-intent surgical sarcoma resection

BACKGROUND: Outcomes 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. METHODS: In 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. RESULTS: ), myosteatotic (SMI 42.1 ± 5.9; SMD 28.9 ± 5.3; VAT 142 ± 62), sarcopenic (SMI 35.6 ± 4.8; SMD 34.0 ± 5.6; VAT 96 ± 48), and cachexia-like (SMI 31.2 ± 4.4; SMD 26.4 ± 4.9; VAT 64 ± 35). Median OS differed significantly across phenotypes (155 vs. 32 vs. 64 vs. 19 months; p < 0.0001). Postoperative functional status also worsened stepwise (median ECOG at follow-up: 1 ± 0.5 vs. 2.5 ± 1 vs. 3 ± 1 vs. 3 ± 0.5; p < 0.0001). In multivariable Cox regression, cachexia-like (HR 3.28, 95% CI 2.01-5.36; p < 0.001) and sarcopenic phenotypes (HR 1.89, 95% CI 1.26-2.83; p = 0.002) independently predicted mortality, whereas conventional sarcopenia did not. SSI rates increased across phenotypes (6.8% to 21.4%; p = 0.042), cachexia-like (HR 3.21, 95% CI 1.69-6.10; p < 0.001) and sarcopenic phenotypes (HR 2.08, 95% CI 1.17-3.70; p = 0.012) were independently associated with SSI. LOS was independently prolonged in sarcopenic (+ 3.4 days, p = 0.002) and cachexia-like patients (+ 6.2 days, p < 0.001). CONCLUSIONS: AI-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.