The Use of Chest Radiography in Detecting Early TB in Children and Youth

About This Grant

Summary Tuberculosis (TB) remains a significant global health challenge, with the World Health Organization (WHO) estimating 10.8 million new cases and 1.3 million deaths in 2022. Despite advances in TB treatment, the disease continuum includes TB infection, several subclinical stages, and active TB disease, with early TB stages often going unrecognized. Individuals in these early stages can still transmit TB, even without the classic symptoms, complicating control efforts. Traditional TB control strategies focus on diagnosing and treating active TB disease to reduce transmission. However, growing evidence shows that individuals can spread TB before developing recognizable symptoms of active TB disease. This underscores the need for strategies to identify and intervene during the early stages of TB. Chest radiography (CXR) is a standard tool for diagnosing active TB disease, and recent studies suggest it could also detect early stages of TB, particularly in young individuals who often present CXR abnormalities atypical of active TB disease. This proposal aims to investigate the use of CXR to identify young individuals with early stages of TB within a community-based active case-finding (ACF) program in Lima, Peru. We will use existing data from an ACF program initiated in 2019, where 19,950 young individuals were screened for active TB disease but were ruled out from having it. We will use data from the Peruvian National TB surveillance system to identify individuals who developed active TB disease within a year after completion of the ACF program. In Aim 1a, we will evaluate whether among these young individuals, CXR abnormalities were associated with later incident TB. In Aim 1b, we will conduct a nested case-control study based on the cohort of Aim 1a to identify specific CXR abnormal features associated with subsequent incident active TB disease. Artificial intelligence-based computer-aided detection (AI-CAD) of CXRs has expedited the diagnosis of active TB disease, but its focus on typical TB abnormalities may miss individuals at early stages of TB, who usually have atypical abnormalities. Therefore, in Aim 2, we will develop an AI-based CXR reading algorithm tailored for detecting individuals at early TB stages, differing from those designed for active TB diagnosis. This algorithm will be trained on CXR images from individuals in Aim 1b. This study could provide robust evidence supporting CXR as a standard tool for detecting individuals at early stages of TB, enabling timely non-therapeutic or therapeutic interventions to help interrupt further transmission of TB.