Patient metabolic profile defined by liver and muscle (18)F-FDG PET avidity is independently associated with overall survival in gastric cancer Journal Article


Authors: Vitiello, G. A.; Jayaprakasam, V. S.; Tang, L. H.; Schattner, M. A.; Janjigian, Y. Y.; Ku, G. Y.; Maron, S. B.; Schoder, H.; Larson, S. M.; Gönen, M.; Datta, J.; Coit, D. G.; Brennan, M. F.; Strong, V. E.
Article Title: Patient metabolic profile defined by liver and muscle (18)F-FDG PET avidity is independently associated with overall survival in gastric cancer
Abstract: Background: PET–CT-based patient metabolic profiling is a novel concept to incorporate patient-specific metabolism into gastric cancer care. Methods: Staging PET–CTs, demographics, and clinicopathologic variables of gastric cancer patients were obtained from a prospectively maintained institutional database. PET–CT avidity was measured in tumor, liver, spleen, four paired muscles, and two paired fat areas in each patient. The liver to rectus femoris (LRF) ratio was defined as the ratio of SUVmean of liver to the average SUVmean of the bilateral rectus femoris muscles. Kaplan–Meier and Cox-proportional hazards models were used to identify the impact of LRF ratio on OS. Results: Two hundred and one patients with distal gastroesophageal (48%) or gastric (52%) adenocarcinoma were included. Median age was 65 years, and 146 (73%) were male. On univariate analysis, rectus femoris PET–CT avidity and LRF ratio were significantly associated with overall survival (p < 0.05). LRF ratio was significantly higher in males, early-stage cancer, patients with an ECOG 0 or 1 performance status, patients with albumin > 3.5 mg/dL, and those with moderately differentiated tumor histology. In multivariable regression, gastric cancer stage, albumin, and LRF ratio were significant independent predictors of overall survival (LRF ratio HR = 0.73 (0.56–0.96); p = 0.024). Survival curves showed that the prognostic impact of LRF was associated with metastatic gastric cancer (p = 0.009). Conclusions: Elevated LRF ratio, a patient-specific PET–CT-based metabolic parameter, was independently associated with an improvement in OS in patients with metastatic gastric cancer. With prospective validation, LRF ratio may be a useful, host-specific metabolic parameter for prognostication in gastric cancer. © The Author(s) under exclusive licence to The International Gastric Cancer Association and The Japanese Gastric Cancer Association 2024.
Keywords: survival; adult; cancer chemotherapy; controlled study; human tissue; aged; cancer surgery; survival rate; retrospective studies; human cell; major clinical study; overall survival; nonhuman; cancer patient; cancer radiotherapy; cancer staging; positron emission tomography; prospective study; radiopharmaceuticals; mouse; disease association; spleen; cohort analysis; pathology; retrospective study; histology; liver; albumin; albumins; proportional hazards model; early cancer; fluorodeoxyglucose f 18; fluorodeoxyglucose f18; radiopharmaceutical agent; insulin; gastrectomy; sex difference; kaplan meier method; stomach adenocarcinoma; univariate analysis; metformin; stomach neoplasms; pet scan; stomach tumor; electrocorticography; gastric cancer; muscle; muscles; metabolic parameters; esophagogastrectomy; demographics; albumin blood level; endoscopic submucosal dissection; cancer prognosis; distal gastrectomy; humans; prognosis; human; male; female; article; positron emission tomography-computed tomography; positron emission tomography computed tomography; metabolome; mean standardized uptake value; metabolic fingerprinting; liver to rectus femoris ratio; patient metabolic profiling; rectus femoris muscle
Journal Title: Gastric Cancer
Volume: 27
Issue: 3
ISSN: 1436-3291
Publisher: Springer  
Date Published: 2024-05-01
Start Page: 548
End Page: 557
Language: English
DOI: 10.1007/s10120-024-01485-7
PUBMED: 38436762
PROVIDER: scopus
DOI/URL:
Notes: Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PubMed and PDF -- Source: Scopus
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors
  1. Murray F Brennan
    1059 Brennan
  2. Mithat Gonen
    1029 Gonen
  3. Geoffrey Yuyat Ku
    231 Ku
  4. Heiko Schoder
    544 Schoder
  5. Yelena Yuriy Janjigian
    395 Janjigian
  6. Laura Hong Tang
    447 Tang
  7. Vivian Strong
    265 Strong
  8. Daniel Coit
    542 Coit
  9. Steven M Larson
    959 Larson
  10. Mark Schattner
    169 Schattner
  11. Steven Maron
    103 Maron