Blood Test Could Help Fast-Track Lymphoma Diagnosis in Resource-Limited Settings

Lymphoma can be difficult to distinguish from infections and benign conditions, while definitive diagnosis often depends on surgical tissue sampling and specialized pathology that may not be readily available. These delays can postpone treatment for aggressive disease, particularly in resource-limited settings where symptoms may also overlap with tuberculosis. A new study shows that a blood-based assay could help identify patients who need expedited biopsy and specialist evaluation.

At the MUSC Hollings Cancer Center (Charleston, SC, USA), investigators and international collaborators developed the Access to Diagnosis using Liquid Biopsy (ADLiB) platform, a blood-based assay that integrates multiple genomic and infectious signals with machine learning to estimate the likelihood of lymphoma. Intended as a triage tool rather than a replacement for biopsy, ADLiB is designed to shorten time to definitive tissue sampling. The approach targets settings with limited access to hematopathology while remaining relevant across diverse care environments.

Image

ADLiB analyzes cell-free DNA in blood, searching for minute amounts of tumor-derived fragments among background DNA. The platform aggregates several signal classes, including circulating tumor DNA levels, lymphoma-associated mutations, chromosome changes, immune cell receptor patterns, and infectious pathogens that can mimic lymphoma, such as tuberculosis. A machine-learning algorithm weighs these features to produce a probability score that can help clinicians prioritize patients for further testing.

Before evaluating patients, the team confirmed that ADLiB could detect genetic changes associated with lymphoma using laboratory materials and patient samples with known outcomes. The prospective study then assessed 124 adults in Cape Town, South Africa, who presented with enlarged lymph nodes. About three-quarters ultimately had lymphoma, while the remaining patients were diagnosed with tuberculosis, benign conditions, or metastatic solid tumors; approximately one-third were living with HIV.

When the measured signals were combined in the algorithm, the platform correctly identified patients with lymphoma 95% of the time and distinguished them from patients with noncancerous causes of enlarged lymph nodes with 92% accuracy. The assay is not intended to replace tissue biopsy; rather, it is positioned to flag individuals who should proceed rapidly to definitive sampling and follow-up testing. In some cases, the blood test also detected chromosome changes that can assist with lymphoma classification, and it may be useful when lymph nodes are located deep in the chest or abdomen.

Findings were published in HemaSphere on May 29, 2026. The developers note that, although conceived for resource-limited settings, many of the same diagnostic bottlenecks occur in rural and smaller health systems in high‑income countries. Future studies will focus on refining the algorithm, improving discrimination among lymphoma subtypes, and evaluating performance in larger and more diverse populations.

“This would prioritize patients who are at high risk of having lymphoma and need to be expedited for a tissue biopsy. It doesn’t eliminate the need for a biopsy, but it can help us identify those patients much earlier,” said Katherine Antel, M.D., Ph.D., physician-scientist at MUSC Hollings Cancer Center and lead author of the study.

“Access to specialized pathology expertise isn’t universal, and delays in diagnosis still happen. If we can identify the patients who need urgent evaluation and get them to the right care faster, we have a real opportunity to improve outcomes,” added Antel.

Related Links
MUSC Hollings Cancer Center