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. 2024 Oct:147:107221.
doi: 10.1016/j.ijid.2024.107221. Epub 2024 Sep 2.

Breaking the threshold: Developing multivariable models using computer-aided chest X-ray analysis for tuberculosis triage

Coralie Geric  1 , Gamuchirai Tavaziva  2 , Marianne Breuninger  3 , Keertan Dheda  4 , Ali Esmail  5 , Alex Scott  5 , Mary Kagujje  6 , Monde Muyoyeta  7 , Klaus Reither  8 , Aamir J Khan  9 , Andrea Benedetti  10 , Faiz Ahmad Khan  11
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Free article

Breaking the threshold: Developing multivariable models using computer-aided chest X-ray analysis for tuberculosis triage

Coralie Geric et al. Int J Infect Dis. 2024 Oct.
Free article

Abstract

Objectives: Computer-aided detection (CAD) software packages quantify tuberculosis (TB)-compatible chest X-ray (CXR) abnormality as continuous scores. In practice, a threshold value is selected for binary CXR classification. We assessed the diagnostic accuracy of an alternative approach to applying CAD for TB triage: incorporating CAD scores in multivariable modeling.

Methods: We pooled individual patient data from four studies. Separately, for two commercial CAD, we used logistic regression to model microbiologically confirmed TB. Models included CAD score, study site, age, sex, human immunodeficiency virus status, and prior TB. We compared specificity at target sensitivities ≥90% between the multivariable model and the current threshold-based approach for CAD use.

Results: We included 4,733/5,640 (84%) participants with complete covariate data (median age 36 years; 45% female; 22% with prior TB; 22% people living with human immunodeficiency virus). A total of 805 (17%) had TB. Multivariable models demonstrated excellent performance (areas under the receiver operating characteristic curve [95% confidence interval]: software A, 0.91 [0.90-0.93]; software B, 0.92 [0.91-0.93]). Compared with threshold scores, multivariable models increased specificity (e.g., at 90% sensitivity, threshold vs model specificity [95% confidence interval]: software A, 71% [68-74%] vs 75% [74-77%]; software B, 69% [63-75%] vs 75% [74-77%]).

Conclusion: Using CAD scores in multivariable models outperformed the current practice of CAD-threshold-based CXR classification for TB diagnosis.

Keywords: Chest radiography; Deep learning; Prediction modelling; Tuberculosis.

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Conflict of interest statement

Declarations of competing interest FAK currently holds a grant from CIHR to study CAD in Canada. FAK also reports salary support from the Fonds de Recherche du Quebec Santé. FAK reports that the following developers of computer-aided detection software provided his research group with either free or reduced pricing access to their software for evaluative research, governed by contracts with the Research Institute of the McGill University Health Centre that ensure that the groups did not have any role in the study design, analysis, result interpretation, or decision to publish previous research and the submitted work: Delft (Netherlands, makers of CAD4TB), qure.ai (India, makers of qXR), and Lunit (South Korea, makers of LUNIT INSIGHT). MB reports grants from European and Development Countries Clinical Trials Partnership during the conduct of the study.

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