The atlas of abdominal organ remodeling in hepatocellular carcinoma patients: An artificial intelligence-based multicenter imaging study.

BACKGROUND: Morphological changes of abdominal organs in hepatocellular carcinoma (HCC) remain uncharacterized. This study aimed to automatically quantify these changes using deep learning, assess their treatment-related outcome associations, and evaluate prognostic value. METHODS: Abdominal computed tomography (CT) images from 2,747 patients with HCC and 2,869 healthy controls across 15 cohorts from 10 centers (8 Chinese and 2 international) were analyzed. The patients included those receiving immune checkpoint inhibitors (ICIs), transarterial chemoembolization (TACE), or surgical resection. A deep learning algorithm automatically segmented the spleen, liver, kidneys, pancreas, and adrenal glands. Organ volumes and height-normalized indexes (volume/height2) were calculated. Propensity score matching balanced baseline differences. Cox regression assessed associations with overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS), with subgroup and interaction analyses. FINDINGS: Compared with healthy controls, patients with HCC showed significant organ remodeling, with enlarged spleen, liver, kidneys, and adrenal glands (all p < 0.001). Multivariable analysis showed that in the ICI cohort, left kidney volume/index, right kidney index, and left adrenal gland volume/index predicted longer OS and PFS; in the TACE cohort, spleen volume/index and left kidney volume predicted OS and PFS; and in the surgical cohort, spleen volume, left kidney volume, left adrenal gland volume/index, and liver index were independent predictors. Restricted cubic spline analysis suggested nonlinear relationships between adrenal and kidney volumes and survival. CONCLUSIONS: HCC is associated with systemic abdominal organ remodeling. Automated CT-based multi-organ quantification offers reproducible, non-invasive prognostic biomarkers, particularly in cases involving adrenal glands, spleen, and kidneys, supporting personalized treatment and prognosis assessment. FUNDING: This work was funded by the National Natural Science Foundation of China.