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Article Abstract
Background: Systemic Immune-inflammation Index (SII) and body composition parameters are easily assessed, and can predict overall survival (OS) in various cancers, allowing early intervention. This study aimed to assess the correlation between CT-derived body composition parameters and SII and OS in patients with advanced gastric cancer receiving dual programmed death-1 (PD-1) and human epidermal growth factor receptor 2 (HER2) blockade.
Materials And Methods: This retrospective study enrolled patients with advanced gastric cancer treated with dual PD-1 and HER2 blockade from March 2019 to June 2022. We developed a deep learning model based on nnU-Net to automatically segment skeletal muscle, subcutaneous fat and visceral fat at the third lumbar level, and calculated the corresponding Skeletal Muscle Index, skeletal muscle density, subcutaneous fat area (SFA) and visceral fat area. SII was computed using the formula that total peripheral platelet count×neutrophil/lymphocyte ratio. Univariate and multivariate Cox regression analysis were used to determine the associations between SII, body composition parameters and OS.
Results: The automatic segmentation deep learning model was developed to efficiently segment body composition in 158 patients (0.23 s/image). Multivariate Cox analysis revealed that high SII (HR=2.49 (95% CI 1.54 to 4.01), p<0.001) and high SFA (HR=0.42 (95% CI 0.24 to 0.73), p=0.002) were independently associated with OS, whereas sarcopenia was not an independent prognostic factor for OS (HR=1.41 (95% CI 0.86 to 2.31), p=0.173). In further analysis, patients with high SII and low SFA had worse long-term prognosis compared with those with low SII and high SFA (HR=8.19 (95% CI 3.91 to 17.16), p<0.001).
Conclusion: Pretreatment SFA and SII were significantly associated with OS in patients with advanced gastric cancer. A comprehensive analysis of SII and SFA may improve the prognostic stratification of patients with gastric cancer receiving dual PD-1 and HER2 blockade.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10314655 | PMC |
| http://dx.doi.org/10.1136/jitc-2023-007054 | DOI Listing |
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