Diagnostic Performance of Chest Radiography for Pediatric Tuberculosis Across High-and Low-Burden Settings

Alicia Hernanz Lobo^1,2,3,4Juan José Gómez Valverde^5,6Ángel Lancharro⁷Ramón Sánchez-Jacob^8,9José Luis Ribó¹⁰H. Simon Schaaf¹¹Lara García Delgado^12,13Daniel Capellán-Martín^12,13David Aguilera-Alonso^1,14,3,4Daniel Blázquez-Gamero^15,16,17,18Antoni Noguera-Julian^19,20,21,22PAULA RODRIGUEZ MOLINO^16,23,24Minguell Laura²⁵Matilde Bustillo-Alonso²⁶Antoni Soriano-Arandes^27,28David Gómez-Pastrana²⁹Alberto L García-Basteriro^16,30,31Orvalho Augusto^30,32María J. Ledesma-Carbayo^13,33Elisa López-Varela³⁰Begoña Santiago-Garcia^1,2,3,4*

• ¹Instituto de Salud Carlos III (ISCIII), Centro de Investigacion Biomedica en Red Enfermedades Infecciosas, Madrid, Spain
• ²Sección Enfermedades Infecciosas Pediátricas, Hospital General Universitario Gregorio Maranon, Madrid, Spain
• ³Instituto de Investigacion Sanitaria Gregorio Maranon, Madrid, Spain
• ⁴Translational Research Network in Pediatric Infectious Diseases (RITIP), Madrid, Spain
• ⁵Biomedical Image Technologies group (BIT), Universidad Politecnica de Madrid Escuela Tecnica Superior de Ingenieros de Telecomunicacion, Madrid, Spain
• ⁶Instituto de Salud Carlos III (ISCIII), Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina CIBER BBN, Madrid, Spain
• ⁷Sección de Radiología Pediátrica, Hospital General Universitario Gregorio Maranon, Madrid, Spain
• ⁸Department of Radiology and Medical Imaging, Children's National Hospital, Washington, United States
• ⁹George Washington School of Medicine, Washington, United States
• ¹⁰Department of Radiology, Hospital Sant Joan de Deu, Barcelona, Spain
• ¹¹Department of Pediatrics and Child Health, Stellenbosch University Desmond Tutu Tuberculosis Centre, Cape Town, South Africa
• ¹²Biomedical Image Technologies Group, Universidad Politecnica de Madrid Escuela Tecnica Superior de Ingenieros de Telecomunicacion, Madrid, Spain
• ¹³Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina CIBER BBN, Madrid, Spain
• ¹⁴Sección de Infectología Pediátrica, Hospital General Universitario Gregorio Maranon, Madrid, Spain
• ¹⁵Sección Enfermedades Infecciosas Pediátricas, Hospital Universitario 12 de Octubre, Madrid, Spain
• ¹⁶Centro de Investigacion Biomedica en Red Enfermedades Infecciosas, Madrid, Spain
• ¹⁷Instituto de Investigacion Hospital 12 de Octubre, Madrid, Spain
• ¹⁸Universidad Complutense de Madrid, Madrid, Spain
• ¹⁹Infectious Diseases and Systemic Inflammatory Response in Pediatrics, Infectious Diseases Department, Hospital Sant Joan de Deu, Barcelona, Spain
• ²⁰Department of Surgery and Medico-surgical Specialties, Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
• ²¹Centro de Investigacion Biomedica en Red de Epidemiologia y Salud Publica, Madrid, Spain
• ²²Translational Research Network in Pediatric Infectious Diseases (RITIP), Spain, Madrid, Spain
• ²³Department of Infectious Diseases and Tropical Pediatrics, La Paz Hospital, Madrid, Spain., Hospital Universitario La Paz, Madrid, Spain
• ²⁴Instituto de Investigacion Hospital Universitario La Paz, Madrid, Spain
• ²⁵Department of Pediatrics, Hospital Universitari Arnau de Vilanova, Lleida, Spain
• ²⁶Sección Enfermedades Infecciosas Pediátricas, Servicio de Pediatría, Hospital Universitario Miguel Servet, Zaragoza, Spain
• ²⁷Pediatric Infectious Diseases and Immunodeficiencies Unit, Hospital Universitari Vall d'Hebron, Barcelona, Spain
• ²⁸Vall d'Hebron Institut de Recerca, Barcelona, Spain
• ²⁹Pediatric Neumology Unit, Pediatrics Department, Hospital de Jerez de la Frontera, Jerez de la Frontera, Spain
• ³⁰Centro de Investigacao em Saude de Manhica, Manhica, Mozambique
• ³¹Instituto de Salud Global de Barcelona, Barcelona, Spain
• ³²Department of Global Health, University of Washington, Seattle, United States
• ³³Biomedical Image Technologies, Universidad Politecnica de Madrid Escuela Tecnica Superior de Ingenieros de Telecomunicacion, Madrid, Spain

Background. Chest radiography (CXR) is the most widely used imaging tool in pediatric tuberculosis (TB) diagnostic pathways, and remains central in current WHO algorithms. However, its standalone diagnostic accuracy has not been well established in standardized multicenter evaluations. This study aimed to determine the diagnostic performance and interobserver agreement of CXR for pediatric TB across two epidemiologically distinct settings, and to assess the added value of clinical information and lateral projections. Methods. We evaluated the diagnostic performance of CXR in two pediatric cohorts from distinct TB-burden settings. The high-burden cohort (Mozambique) included 218 children under 3 years (10 confirmed TB, 95 unconfirmed TB, 113 unlikely TB). The low-burden cohort (Spain) included 674 children under 18 years (145 confirmed TB, 237 unconfirmed TB, 95 with TB infection, 101 with community-acquired pneumonia, and 96 healthy controls). Four independent expert readers (three pediatric radiologists and one pediatric infectious disease specialist), each with over 15 years of experience, interpreted CXRs using a standardized digital platform, blinded to clinical data. In a subset of 75 Spanish cases, re-readings incorporated limited clinical information. Results. Sensitivity for confirmed TB was low in both settings (31.0% in Mozambique, 46.1% in Spain), while specificity was high (94.7% and 96.5%, respectively). In a subset of 75 Spanish cases, adding limited clinical data increased sensitivity from 39.3% to 50.0% (p=0.02) and specificity from 88.1% to 97.4% (p<0.001). Among children with lateral views, sensitivity rose from 39.1% to 53.6% (p=0.01), without significant change in specificity. Interobserver agreement for TB-related findings was only fair (ICC 0.29–0.31). Conclusions. This multicenter analysis confirms the limited sensitivity but high specificity of CXR for pediatric TB, even when interpreted by expert readers. These findings highlight that CXR alone cannot reliably confirm or exclude disease and should be integrated with clinical and microbiological data. Future diagnostic pathways, including artificial intelligence–assisted CXR interpretation, will likely need multimodal approaches to overcome the intrinsic limitations of imaging alone.