New marker uses tomography to refine gastric cancer prognosis

by Fernanda Bassette, FAPESP

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Researchers at the State University of Campinas (UNICAMP) in São Paulo, Brazil, have identified a new biomarker that may help determine the prognosis for patients with gastric cancer, the fifth most common type of cancer worldwide. The team identified a variable that combines data on the radiodensity of visceral fat and muscle by analyzing images from computed tomography (CT) scans, a routine examination for these patients. This variable can distinguish those at higher risk of unfavorable disease progression.

Conducted at the Department of Radiology and Oncology of the Faculty of Medical Sciences (FCM) at UNICAMP in partnership with the Gleb Wataghin Institute of Physics (IFGW) at the same university, the study concluded that this new marker, called VMD, may complement traditional tumor staging (the process used to determine the location, severity and extent of a disease, especially cancer) in the future. This could pave the way for a more personalized treatment approach.

The results are published in Clinical Nutrition ESPEN.

According to the researchers, the idea arose from a shift in perspective. Rather than solely considering tumor staging, as is currently done to determine prognosis, the group also observed the patient.

"Today, cancer treatment is still very tumor-centric. Our proposal is to look at the patient as a whole. That's a line of research that Professor José Barreto has been developing for years. That's what convinced me to participate. It isn't enough to treat the disease; you have to treat the patient," summarizes Jun Takahashi. Takahashi is a full professor at IFGW-UNICAMP and one of the study's co-advisers.

This approach led the group to investigate body composition, or how visceral fat and muscle are distributed in the body, and its relationship to cancer progression in patients. Previous studies by the same team had already shown that fat and muscle individually could influence prognosis. However, it was unclear whether combining this information could reveal additional insights.

To this end, the researchers analyzed data from 461 gastric cancer patients who were treated at UNICAMP over nearly 10 years. Using images from routine CT scans, the researchers assessed the characteristics of fat and muscle in these patients' bodies.

Using this information, they tested different combinations until identifying an indicator capable of better distinguishing between groups with a higher or lower risk of disease progression. Then they compared survival rates between these groups to understand how this new marker relates to prognosis.

That's where the VMD marker came in. "We combined two pieces of information that we already knew were important: the radiodensity of adipose tissue and the radiodensity of muscle," explains Maria Carolina Santos Mendes, a nutritionist and co-adviser on the study.

But what does this mean in practice? Radiodensity, a measure used in CT scans, indicates how much tissue attenuates (blocks) X-rays. This determines how tissue appears on a scan, ranging from darkest to lightest on a scale of shades. Changes in these patterns may indicate inflammatory and metabolic changes in fat and muscle caused by cancer.

The researchers observed a significant difference in patient outcomes according to VMD. Those with higher values were considered to be at higher risk and had a worse prognosis in terms of both overall and disease-free survival. Practically speaking, this meant a median survival of 13.8 months for patients with the worst indicators, compared with 58.5 months for those with lower VMD values.

"In adipose tissue, higher radiodensity values are associated with a worse prognosis and may indicate inflammation. However, in muscle, the opposite is true: The lower the radiodensity, the worse the prognosis. This highlights the potential of the marker to distinguish different disease progression profiles," Mendes explains.

"This difference in radiodensity between fat and muscle ultimately captures an integrated patient phenotype in which characteristics associated with metabolism and an inflammatory state associated with higher clinical risk emerge," emphasizes José Barreto Campello Carvalheira, a full professor of clinical oncology and one of the leaders of the study.

To develop this biomarker, the team relied on artificial intelligence techniques. Rather than manually analyzing one variable at a time, as is traditionally done, the researchers used machine learning to evaluate large volumes of data, including imaging scans and clinical and laboratory information from the patients in the study.

"I taught the machine to look in the same direction that experts were already looking, but with greater speed and scale. The technology allowed us to test various combinations until we arrived at the formula that best identified patients with the worst prognosis," Takahashi explains.

There was another important consideration as well: minimizing potential variations in the scans themselves, as highlighted by researcher Maria Emília Seren Takahashi. Since CT scans can have slight calibration differences between machines, the researchers chose to use the difference between fat and muscle. This helps "cancel out" these technical variations, making the marker more accurate.

Clinical impact

From a clinical perspective, the researchers said VMD has the potential to guide and even change treatment approaches. Currently, gastric cancer treatment is primarily guided by disease staging, which considers tumor characteristics such as size and the presence of metastases. However, patients with the same stage can progress in very different ways.

"The goal of this line of research, and of this study in particular, is to expand staging beyond the tumor by incorporating an assessment of the patient," Barreto reiterates.

He says that, in the future, VMD could aid in therapeutic stratification by identifying which patients would truly benefit from chemotherapy and which could be spared more toxic and aggressive treatment following surgery. "This marker reflects the patient's metabolic and inflammatory condition," he says.

Despite the promising results, the authors emphasize that the study is retrospective and requires external validation in different populations. Ideally, this validation would come from prospective, multicenter studies with larger patient populations. Only then will it be possible to confirm whether the marker can guide clinical decisions.

The researchers also do not know if this body profile can be modified over the course of treatment. "We believe that nutritional therapy can help improve the patient's condition, but that wasn't evaluated in the study. We still don't know if it's possible to change that profile and impact the prognosis by doing so. We have the question, but not yet the answer. That still needs to be investigated," Mendes says.

Nevertheless, the study moves in an increasingly prevalent direction in oncology: precision medicine, which incorporates the patient's individual characteristics into disease assessment.

Since the marker is obtained from CT scans that are already part of routine care, there is the possibility of expanding available clinical information without requiring new tests. The researchers have begun the next steps and are testing the new marker in other types of cancer. Early results indicate that the approach may follow the same path.

More information

Mariana Caleffi et al, Determination of a new gastric cancer mortality predictor based on body composition radiodensity variables, Clinical Nutrition ESPEN (2026). DOI: 10.1016/j.clnesp.2026.103132

Key medical concepts

Gastric CarcinomaCT ScansArtificial IntelligenceMachine Learning

Clinical categories

OncologyDiagnostic radiology Provided by FAPESP Who's behind this story?

Sadie Harley

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