Med (New York, N.Y.) · 2026
The atlas of abdominal organ remodeling in hepatocellular carcinoma patients: An artificial intelligence-based multicenter imaging study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Guo Yusheng, Fu Xiaona, Li Tianxiang, Wang Ning, Jiang Shanshan, Li Shanmei, Guo Xiaofang, Wang Rundong, Long Shichao, Li Mengsi, Liu Qiuping, Zhao Kai, Xie Yangyang, Chen Xuejun, Wang Lixia, Zheng Chuansheng, Yang Lian
Аннотация
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.
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.