An integrated body composition– immunonutritional signature for predicting immunotherapy response and prognosis in gastric cancer: a multicenter retrospective cohort study - Takeaways - MDSpire

An integrated body composition– immunonutritional signature for predicting immunotherapy response and prognosis in gastric cancer: a multicenter retrospective cohort study

  • By

  • Fang Li

  • Tao Zheng

  • Honghai Guo

  • Peigang Yang

  • Ning Meng

  • Xiaolong Li

  • Zhenjiang Guo

  • Yuan Tian

  • Qun Zhao

  • July 15, 2026

Share

  • 1

    The study developed the Body Composition and Immunonutritional Signature (BCIS) to predict outcomes in gastric cancer patients receiving neoadjuvant therapy.

  • 2

    BCIS classified patients into favorable, intermediate, and unfavorable groups, with major pathological response rates significantly differing among these strata.

  • 3

    Each point increase in BCIS was associated with a 40% reduction in odds of major pathological response and a nearly 50% reduction in odds of pathological complete response.

  • 4

    BCIS improved the area under the curve for predicting major pathological response, indicating enhanced predictive capability beyond traditional tumor biomarkers.

  • 5

    At a median follow-up of 42.5 months, higher BCIS scores correlated with increased hazards of progression and death, demonstrating its prognostic value.

Original Source(s)

An integrated body composition– immunonutritional signature for predicting immunotherapy response and prognosis in gastric cancer: a multicenter retrospective cohort study

  • by Fang Li, Tao Zheng, Honghai Guo, Peigang Yang, Ning Meng, Xiaolong Li, Zhenjiang Guo, Yuan Tian, Qun Zhao

  • July 15, 2026

Related Content

Pancreatic cancer burden in China from 1990 to 2023: an analysis based on the global burden of disease study 2023

Evaluation of an interpretable deep-learning model for the automated plan review of intensity-modulated radiation therapy

Development and clinical validation of an artificial intelligence based model for thyroid nodule malignancy risk assessment using C-TIRADS guidelines