Cargando…

Development and Validation of a Radiomics Nomogram for Liver Metastases Originating from Gastric and Colorectal Cancer

The origin of metastatic liver tumours (arising from gastric or colorectal sources) is closely linked to treatment choices and survival prospects. However, in some instances, the primary lesion remains elusive even after an exhaustive diagnostic investigation. Consequently, we have devised and valid...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Yuying, Li, Jingjing, Meng, Mingzhu, Duan, Shaofeng, Shi, Haifeng, Hang, Junjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10528017/
https://www.ncbi.nlm.nih.gov/pubmed/37761304
http://dx.doi.org/10.3390/diagnostics13182937
Descripción
Sumario:The origin of metastatic liver tumours (arising from gastric or colorectal sources) is closely linked to treatment choices and survival prospects. However, in some instances, the primary lesion remains elusive even after an exhaustive diagnostic investigation. Consequently, we have devised and validated a radiomics nomogram for ascertaining the primary origin of liver metastases stemming from gastric cancer (GCLMs) and colorectal cancer (CCLMs). This retrospective study encompassed patients diagnosed with either GCLMs or CCLMs, comprising a total of 277 GCLM cases and 278 CCLM cases. Radiomic characteristics were derived from venous phase computed tomography (CT) scans, and a radiomics signature (RS) was computed. Multivariable regression analysis demonstrated that gender (OR = 3.457; 95% CI: 2.102–5.684; p < 0.001), haemoglobin levels (OR = 0.976; 95% CI: 0.967–0.986; p < 0.001), carcinoembryonic antigen (CEA) levels (OR = 0.500; 95% CI: 0.307–0.814; p = 0.005), and RS (OR = 2.147; 95% CI: 1.127–4.091; p = 0.020) exhibited independent associations with GCLMs as compared to CCLMs. The nomogram, combining RS with clinical variables, demonstrated strong discriminatory power in both the training (AUC = 0.71) and validation (AUC = 0.78) cohorts. The calibration curve, decision curve analysis, and clinical impact curves revealed the clinical utility of this nomogram and substantiated its enhanced diagnostic performance.