Cardiovascular magnetic resonance measurements identified a hemodynamic pattern associated with death or heart-transplant referral or listing in people with Fontan circulation, according to a retrospective study of 182 patients followed at a moderate-altitude center. Patients who later experienced the composite outcome had a lower superior vena cava flow index than those who did not. In time-to-event analysis, each 0.1-liter-per-minute-per-square-meter increase in the indexed flow was associated with a 30% lower hazard of the outcome.

The association remained in an exploratory model that also included ventricular ejection fraction, but the researchers said the estimate requires prospective, multicenter validation before clinical application. The cohort comprised people who had undergone Fontan palliation between 1999 and 2021 at a tertiary institution approximately 5,118 feet above sea level. Fontan circulation passively directs systemic venous blood into the pulmonary arteries without the support of a sub-pulmonary ventricle.

Eligible participants had a clinically indicated CMR examination at least five years after Fontan completion, a threshold selected to match the center’s routine surveillance practice. For patients with multiple examinations, investigators used the last CMR obtained before an outcome or censoring. Most participants, 173 of 182, or 95%, had an extracardiac conduit Fontan circulation, and 76, or 42%, underwent a Damus–Kaye–Stansel anastomosis.

At CMR, the median body surface area was 1.43 square meters, with an interquartile range of 1.16 to 1.69 square meters. The median age at Fontan completion was 3.2 years, and the median interval between surgery and imaging was 10.9 years. Median follow-up after CMR was 4.7 years, while participants’ median home elevation was 5,221 feet above sea level.

Hypoplastic left heart syndrome was the most common underlying diagnosis, accounting for 35% of the cohort, followed by tricuspid atresia at 30%. The systemic ventricle was left-dominant in 53% and right-dominant in 47%. Magnetic resonance examinations were performed on 1.5- or 3.0-tesla scanners, with cine imaging used to assess ventricular function and phase-contrast sequences used to measure blood flow.

The primary endpoint combined death with referral or listing for heart transplantation. Fourteen patients, or 7.7% of the cohort, met that endpoint during follow-up: two died and 12 were referred or listed for transplantation. Among patients who experienced the outcome, the median time from CMR to the first qualifying event was 1.2 years, with an interquartile range of 0.5 to 2.5 years.

The median superior vena cava flow index was 0.54 liters per minute per square meter among patients with the primary outcome, compared with 0.82 liters per minute per square meter among those without it; the difference was statistically significant, with a p-value of 0.011. The superior vena cava-to-ascending-aorta flow ratio was also lower in the event group, with medians of 0.22 and 0.30, respectively. Several other CMR flow measures did not differ significantly between patients with and without the primary outcome.

These included flow indices in the ascending aorta, right pulmonary artery, left pulmonary artery and inferior vena cava, as well as calculated systemic-to-pulmonary collateral flow. Receiver operating characteristic analysis indicated that superior vena cava flow index discriminated between patients who met the endpoint and those who did not with an area under the curve of 0.74 and a 95% confidence interval of 0.56 to 0.92. A data-derived threshold of 0.66 liters per minute per square meter, selected using the Youden index, yielded 70% sensitivity and 75% specificity.

Kaplan–Meier curves showed significantly poorer event-free survival among patients below that threshold. Because the threshold and discrimination measures came from the same cohort, they are exploratory rather than externally validated clinical cutoffs. Univariable Cox proportional hazards regression found a hazard ratio of 0.70 for each 0.1-liter-per-minute-per-square-meter increase in superior vena cava flow index, with a 95% confidence interval of 0.50 to 0.92 and a p-value of 0.008.

Right-dominant ventricular morphology was also associated with a higher hazard than left-ventricular dominance, with a hazard ratio of 3.21, a 95% confidence interval of 1.00 to 10.29 and a p-value of 0.049. The investigators constructed a two-variable Cox model containing superior vena cava flow index and ejection fraction. Each one-percentage-point increase in ejection fraction was associated with an adjusted hazard ratio of 0.92, with a 95% confidence interval of 0.86 to 0.97.

Because only 14 primary events occurred, the multivariable analysis was deliberately restricted and was intended to test whether the flow association persisted beyond ventricular function rather than develop a full prediction model. Internal bootstrap analysis using 2,500 resamples produced a median hazard ratio of 0.71 for superior vena cava flow index and 0.92 for ejection fraction, close to the original adjusted estimates. Collinearity between the two variables was negligible, with a Spearman absolute correlation below 0.1, an R-squared value of 0.008 and a variance inflation factor of 1.01.

Tests did not indicate violation of the proportional-hazards assumption. Ventricular measures also differed according to morphology: in the right-dominant subgroup, patients with events had larger indexed ventricular volumes and lower ejection fraction than those without events, whereas comparable differences were not statistically significant in the smaller left-dominant event subgroup. The study does not establish why lower superior vena cava flow was associated with poorer outcomes.

One proposed explanation is that chronic hypobaric exposure at moderate altitude promotes pulmonary vasoconstriction and raises pulmonary vascular resistance, potentially restricting passive flow through the Fontan pathway. However, pulmonary vascular resistance was not directly measured, and the cohort included no sea-level comparator, so the study cannot determine whether altitude caused the observed hemodynamic pattern. Other possible contributors include anatomical differences in the superior vena cava, systemic venovenous collateral vessels that divert blood around the pulmonary vascular bed, and differences in preload or single-ventricle filling.

The findings therefore associate reduced indexed flow with an unfavorable hemodynamic state rather than demonstrate a specific underlying mechanism. Important limitations constrain the results. The retrospective, single-center design creates the possibility of selection bias, and clinically performed CMR examinations were not acquired at standardized surveillance intervals.

Selecting the last scan before an outcome or censoring may introduce reverse-causation bias because patients approaching clinical deterioration could have been imaged nearer the event. Clinically relevant potential confounders—including functional class, cardiopulmonary exercise capacity, resting oxygen saturation, natriuretic peptide levels, catheterization-derived Fontan pressure and pulmonary vascular resistance, pulmonary vasodilator therapy, arrhythmia burden and the severity of liver disease—were unavailable or not incorporated. The small number of events also leaves the multivariable estimates vulnerable to overfitting despite the deliberately parsimonious model and bootstrap resampling.

Furthermore, the predominance of fenestrated extracardiac conduit Fontan circulation limits generalizability to other surgical configurations. The results position superior vena cava flow index as a candidate prognostic marker that may complement ventricular size and ejection fraction in longitudinal assessment of Fontan circulation, but they do not establish a clinical measurement rule or treatment strategy. Prospective, multicenter studies comparing cohorts across elevations would be needed to test the temporal relationship, determine whether altitude modifies the association, and establish external validity.

The authors also identified four-dimensional flow CMR as a possible tool for characterizing three-dimensional flow patterns and energy loss within the Fontan pathway. Further work could compare imaging signatures with exercise physiology, biomarkers of end-organ dysfunction and pulmonary vascular imaging. Until those studies are completed, the association between reduced superior vena cava flow and adverse outcomes should be regarded as an exploratory finding that requires independent validation.

Subject of Research: Association between CMR-derived blood flow and adverse outcomes in people with Fontan circulation

Article Title: Cardiovascular magnetic resonance–derived hemodynamics predict adverse outcomes after Fontan palliation

Article References: Platnick, C., Frank, B., Romanowicz, J., Gonzalez De Alba, C., Chase, M., Umer, A., Chaudhuri, N. R., Awori, J., Chauhan, D., Mascio, C. E., Udassi, J. P., & Hedjazi Moghari, M. (2026). Cardiovascular magnetic resonance–derived hemodynamics predict adverse outcomes after Fontan palliation. Frontiers in Cardiovascular Medicine, 13, Article 1937933. https://doi.org/10.3389/fcvm.2026.1937933

Image Credits: AI Generated

DOI: 10.3389/fcvm.2026.1937933

Keywords: Fontan circulation, cardiovascular magnetic resonance, superior vena cava flow, heart transplantation, congenital heart disease, risk stratification, ventricular function, moderate altitude, flow quantification, retrospective cohort, Cardiovascular, magnetic

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