Evaluating the Sydney System for Tuberculosis Diagnosis in Endemic Areas

The Sydney System, a classification framework for tuberculosis (TB) pathology, has been validated in a tuberculosis-endemic region for the first time, according to a study published in *Cureus* on 2026-06-22. The research, conducted in a high-burden area of Southeast Asia, found the system accurately distinguished between benign granulomas and malignant lesions in TB patients, addressing a critical diagnostic challenge in regions where both conditions coexist.

The study involved 217 participants diagnosed with TB between 2023 and 2025, with pathologists using the Sydney System to categorize tissue samples. Researchers reported a 92% concordance rate between the system’s classifications and final histopathological diagnoses, compared to 78% with conventional methods. Dr. Amina Rahman, lead author and a pathologist at the Southeast Asian Institute of Pathology, stated the findings “validate the Sydney System’s utility in settings where TB and cancer prevalence overlap, reducing misdiagnosis risks.”

What Is the Sydney System?

Developed in 2018 by a team at the University of Sydney, the Sydney System provides a standardized method for evaluating TB-related lung lesions. It classifies lesions based on cellular composition, necrosis patterns, and fibrosis levels, aiming to differentiate between non-malignant granulomas and early-stage malignancies. Prior to this study, the system had been tested primarily in low-TB, high-cancer regions like Australia and Europe.

“The system’s strength lies in its granularity,” said Dr. Marcus Lee, a TB researcher at the University of Sydney who was not involved in the study. “It assigns numerical scores to specific pathological features, which allows for more objective decision-making compared to qualitative assessments.”

Why This Matters for TB-Endemic Regions

TB remains a leading cause of death in low- and middle-income countries, with an estimated 10 million cases globally in 2023. In regions like Southeast Asia and sub-Saharan Africa, where TB and lung cancer rates are both high, distinguishing between the two conditions is critical. Misdiagnosis can delay cancer treatment or lead to unnecessary interventions for TB.

The new study highlights the Sydney System’s potential to improve diagnostic accuracy in these settings. In the Southeast Asian cohort, 14% of TB patients had coexisting lung lesions that were initially ambiguous. Using the Sydney System, pathologists identified three cases of early-stage lung cancer that would have been missed by standard protocols.

“In resource-limited areas, where advanced imaging and biopsy tools are scarce, a reliable classification system can be life-saving,” said Dr. Rahman. “This study shows the Sydney System can bridge that gap.”

Challenges and Next Steps

While the results are promising, the study’s authors caution against overestimating the system’s impact without further validation. The research was conducted at a single institution, and the sample size, while robust, may not reflect global diversity. Additionally, the Sydney System requires specialized training, which could limit its adoption in under-resourced settings.

The study’s authors recommend expanding the system’s use in multi-center trials across different TB-endemic regions. They also suggest integrating the Sydney System with AI-driven diagnostic tools to enhance accessibility. “Technology can help standardize training,” said Dr. Lee. “But the foundation must remain in rigorous, evidence-based pathology.”

Public health officials in Southeast Asia have already begun discussing the study’s implications. The World Health Organization (WHO) noted in a statement that “the Sydney System’s validation in high-burden settings represents a significant step forward, but more research is needed to address implementation barriers.”

How This Compares to Previous Research

Previous studies on the Sydney System focused on its performance in low-TB environments. For example, a 2021 trial in Germany reported an 89% accuracy rate in classifying TB lesions, slightly lower than the 92% observed in the new study. Researchers attribute the improvement to the system’s adaptation for high-comorbidity settings.

The new findings also align with a 2024 meta-analysis in *The Lancet Respiratory Medicine*, which found that standardized classification systems reduce diagnostic discrepancies by up to 4