A Revolutionary Biomarker Emerges from CT Scans, Offering New Hope in Gastric Cancer Prognosis
In a groundbreaking study spearheaded by researchers at the State University of Campinas (UNICAMP) in São Paulo, Brazil, a novel biomarker has been identified that could transform prognostic assessment for gastric cancer patients. Gastric cancer—ranked as the fifth most prevalent cancer worldwide—has long posed a challenge in predicting disease progression accurately. This innovative marker, derived from routine computed tomography (CT) imaging data, integrates complex measurements of visceral fat and muscle radiodensity to stratify patient risk more effectively than traditional tumor staging alone.
The multidisciplinary team, drawing expertise from both the Faculty of Medical Sciences (FCM) and the Gleb Wataghin Institute of Physics (IFGW) at UNICAMP, embarked on this research with backing from multiple grants awarded by the São Paulo Research Foundation (FAPESP). Their efforts culminated in the development of what they have termed the Visceral Muscle Difference (VMD) marker—a composite variable that captures metabolic and inflammatory properties of patient body composition using quantitative imaging parameters.
Conventionally, gastric cancer prognosis centers around tumor staging, an approach focusing primarily on the characteristics and spread of the tumor itself. However, this research introduces a paradigm shift by emphasizing the patient’s overall physiological state—not just the malignancy. The investigative team, including Professor José Barreto and co-advisor Jun Takahashi, advocates a holistic perspective that scrutinizes how body composition influences cancer outcomes, highlighting that tailored treatment must address the patient’s systemic condition to improve survival.
Central to the study was the analysis of data collected over a decade from 461 patients treated for gastric cancer at UNICAMP. Researchers meticulously analyzed their CT scans, quantifying radiodensity values of visceral adipose tissue and skeletal muscle. Radiodensity, an indicator of tissue’s capacity to attenuate X-rays during CT scanning, holds clues to underlying biological processes such as inflammation and metabolic health—factors increasingly recognized as critical modifiers of cancer progression.
By integrating these radiodensity values into a single metric, the VMD marker captures the complex interplay between fat and muscle tissue states in cancer patients. Intriguingly, the research reveals an inverse prognostic relationship: elevated radiodensity in adipose tissue correlates with poorer outcomes, possibly signalling inflammatory activation within the fat stores, while higher muscle radiodensity aligns with better survival rates, reflecting preserved muscle quality.
Quantitative analysis demonstrated striking survival disparities based on VMD scores. Patients with elevated VMD—a signifier of detrimental body composition—experienced a median survival of just 13.8 months, starkly contrasted with 58.5 months for those exhibiting healthier VMD profiles. This prognostic ability surpasses traditional staging, offering oncologists a powerful tool to identify high-risk patients who may require intensified or alternative therapeutic approaches.
The robustness of the VMD marker is further enhanced by its design, which strategically utilizes the difference between fat and muscle radiodensity rather than relying on absolute values for each tissue. This approach mitigates variability introduced by different CT scanner calibrations or technical inconsistencies, ensuring more reliable clinical implementation across diverse healthcare settings.
Harnessing advanced artificial intelligence techniques, the team employed machine learning algorithms to sift through the extensive imaging and clinical data. Unlike traditional univariate analyses, this methodology enabled rapid testing of multiple radiodensity combinations, refining the marker until it achieved optimal prognostic precision. “Teaching the machine to align with expert clinical insight while scaling data analysis exponentially was key,” explains Takahashi.
The implications of VMD extend beyond prognostication. Integrating this biomarker into clinical workflows could revolutionize treatment decision-making by unveiling the patient’s metabolic and inflammatory status—critical determinants often overlooked in standard cancer care. Personalized treatment regimens could emerge whereby aggressive chemotherapy is selectively administered to those with high-risk VMD profiles, whereas patients with favorable metrics might avoid unnecessary toxicity post-surgery, fundamentally improving quality of life.
Despite these promising findings, researchers caution that the study’s retrospective nature necessitates validation in prospective, multicenter cohorts encompassing broader demographics. Ensuring reproducibility across different populations and clinical environments is essential before VMD can be fully incorporated into routine practice. Moreover, the potential to modify a patient’s body composition profile therapeutically remains an open question, with ongoing investigations exploring whether nutritional or metabolic interventions can positively impact prognosis.
This study situates itself firmly within the evolving landscape of precision oncology, where understanding the host’s systemic biology complements tumor biology to refine cancer management. By leveraging data from standard CT scans—already integral to patient assessment—the VMD marker offers a cost-effective, readily accessible addition to the oncologist’s toolkit without imposing extra procedural burdens on patients.
Early exploratory studies initiated by the team suggest that the predictive value of the VMD marker may extend to other cancer types, potentially heralding a universal biomarker of cancer-related frailty and inflammation. As these lines of research mature, clinicians may soon navigate cancer treatment armed with unprecedented insights into the intricate interplay between tumor and host, tailoring therapeutics with unparalleled precision.
The diligent efforts of the UNICAMP team, supported by FAPESP, exemplify how interdisciplinary collaboration, cutting-edge technology, and patient-centered philosophy can converge to solve complex medical challenges. Their work opens a new chapter in gastric cancer prognosis, where the narrative shifts from focusing solely on the tumor mass to embracing the multifaceted biological portrait of the patient as a whole—paving the way for a future where personalized medicine is truly realized.
Subject of Research: Biomarker development for prognosis in gastric cancer utilizing CT scan-derived body composition radiodensity variables
Article Title: Determination of a new gastric cancer mortality predictor based on body composition radiodensity variables
News Publication Date: March 21, 2026
Web References:
https://www.fapesp.br/en
https://www.agencia.fapesp.br/en
DOI: 10.1016/j.clnesp.2026.103132
References:
Original article published in Clinical Nutrition ESPEN, 2026
Image Credits: FCM-UNICAMP
Keywords:
Gastric cancer, biomarker, prognosis, radiodensity, visceral fat, muscle, CT scan, body composition, machine learning, personalized medicine, inflammation, metabolic state
Tags: Advanced Imaging Techniques for Cancerbody composition analysis in gastric cancerCT imaging in cancer prognosisgastric cancer prognosis biomarkersgastric cancer risk stratificationinflammatory biomarkers in oncologymetabolic markers for cancer prognosismultidisciplinary cancer researchnovel cancer prognostic toolsquantitative imaging parameterstomography-based cancer markersvisceral muscle difference marker
