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Preoperative abnormal bone mineral density as a prognostic indicator in patients undergoing gastrectomy for gastric
Medicine
|May 24, 2024
Summary
Preoperative Hounsfield unit (HU) values, indicating bone mineral density (BMD), predict survival in gastric cancer (GC) patients post-surgery. Lower HU values (≤110) correlate with poorer overall survival (OS) outcomes.
Area of Science:
- Oncology
- Radiology
- Gerontology
Background:
- Gastric cancer (GC) survival prediction is limited by traditional staging systems like TNM.
- Bone mineral density (BMD) may influence outcomes in cancer patients.
- Hounsfield unit (HU) values on CT scans can serve as a surrogate for BMD.
Purpose of the Study:
- To investigate the prognostic value of preoperative Hounsfield unit (HU) values at the L3 vertebra in predicting survival outcomes for gastric cancer (GC) patients undergoing gastrectomy.
- To assess if integrating HU values with existing clinicopathological factors improves survival prediction models.
Main Methods:
- Retrospective analysis of 478 gastric cancer patients who underwent curative-intent gastrectomy.
- Opportunistic measurement of L3 vertebral Hounsfield unit (HU) values from preoperative CT scans.
- Cox regression analysis to identify predictors of overall survival (OS) and compare model performance (baseline vs. full model including HU).
Main Results:
- Lower L3 Hounsfield unit (HU) values (≤110), indicative of abnormal bone mineral density (BMD), were significantly associated with poorer overall survival (OS) (HR=1.51).
- A full model incorporating HU values, modified frailty index, gastrectomy type, TNM stage, anemia, and albumin level showed superior prediction accuracy (C-index=0.807) compared to the TNM-based baseline model (C-index=0.709).
- The enhanced model demonstrated better discrimination for 36- and 60-month survival risks.
Conclusions:
- Preoperative L3 Hounsfield unit (HU) values serve as an independent prognostic factor for overall survival (OS) in gastric cancer (GC) patients post-gastrectomy.
- Integrating HU values with clinicopathological factors significantly improves the predictive accuracy of survival outcomes.
- This approach facilitates individualized risk stratification and personalized treatment strategies for GC patients.
