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Digital tomosynthesis improves chest radiograph accuracy and reduces microbiological false negatives in COVID-19 diagnosis

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Abstract

Purpose

Diagnosing pneumonia by radiograph is improvable. We aimed (a) to compare radiograph and digital thoracic tomosynthesis (DTT) performances and agreement for COVID-19 pneumonia diagnosis, and (b) to assess the DTT ability for COVID-19 diagnosis when polymerase chain reaction (PCR) and radiograph are negative.

Methods

Two emergency radiologists with 11 (ER1) and 14 experience-years (ER2) retrospectively evaluated radiograph and DTT images acquired simultaneously in consecutively clinically suspected COVID-19 pneumonia patients in March 2020–January 2021. Considering PCR and/or serology as reference standard, DTT and radiograph diagnostic performance and interobserver agreement, and DTT contributions in unequivocal, equivocal, and absent radiograph opacities were analysed by the area under the curve (AUC), Cohen’s Kappa, Mc-Nemar’s and Wilcoxon tests.

Results

We recruited 480 patients (49 ± 15 years, 277 female). DTT increased ER1 (from 0.76, CI95% 0.7–0.8 to 0.79, CI95% 0.7–0.8; P=.04) and ER2 (from 0.77 CI95% 0.7–0.8 to 0.80 CI95% 0.8–0.8, P=.02) radiograph—AUCs, sensitivity, specificity, predictive values, and positive likelihood ratio. In false negative microbiological cases, DTT suggested COVID-19 pneumonia in 13% (4/30; P=.052, ER1) and 20% (6/30; P=.020, ER2) more than radiograph. DTT showed new or larger opacities in 33–47% of cases with unequivocal opacities in radiograph, new opacities in 2–6% of normal radiographs and reduced equivocal opacities by 13–16%. Kappa increased from 0.64 (CI95% 0.6–0.8) to 0.7 (CI95% 0.7–0.8) for COVID-19 pneumonia probability, and from 0.69 (CI95% 0.6–0.7) to 0.76 (CI95% 0.7–0.8) for pneumonic extension.

Conclusion

DTT improves radiograph performance and agreement for COVID-19 pneumonia diagnosis and reduces PCR false negatives.

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Acknowledgements

We are very grateful for the essential statistical support provided by Mónica Ballesta Ruiz.

Author information

Authors and Affiliations

  1. Hospital Universitario Morales Meseguer, 30008, Murcia, ZC, Spain

    Juana María Plasencia-Martínez, Ana Moreno-Pastor, Marina Lozano-Ros, Cristina Jiménez-Pulido, Ignacio Herves-Escobedo, Gloria Pérez-Hernández & José María García-Santos

  2. Current affiliation: Hospital Clínico, 50009, Zaragoza, ZC, Spain

    Gloria Pérez-Hernández

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  1. Juana María Plasencia-Martínez
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Contributions

Conceptualisation: JMPM, JMGS

Data curation: JMPM, AMP, MLR, CJP, IHE, GPH

Formal analysis: JMPM

Funding acquisition: n/a

Investigation: JMPM

Methodology: JMPM

Project administration: JMPM, JMGS

Resources: n/a

Software: n/a

Supervision: JMPM

Validation: JMPM

Visualisation: n/a

Writing—original draft: JMPM, AMP, MLR, CJP, IHE, GPH, JMGS

Writing—review and editing: JMPM, AMP, MLR, CJP, IHE, GPH, JMGS

All authors contributed to the study conception and design.

The first draft of the manuscript was written by Juana María Plasencia Martínez and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Juana María Plasencia-Martínez.

Ethics declarations

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Ethics Committee of our institution (internal code EST 55/20).

Conflict of interest

The authors Juana María Plasencia Martínez and José María García Santos received a speaking fee by General Electric Healthcare as webinars speakers on the application of digital thoracic tomosynthesis in COVID-19. They have been also engaged with the same company for clinical research to explore the utility in COVID-19 of artificial intelligence tools for chest radiography. The rest of authors have no competing interests to declare that are relevant to the content of this article.

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Plasencia-Martínez, J.M., Moreno-Pastor, A., Lozano-Ros, M. et al. Digital tomosynthesis improves chest radiograph accuracy and reduces microbiological false negatives in COVID-19 diagnosis. Emerg Radiol 30, 465–474 (2023). https://doi.org/10.1007/s10140-023-02153-6

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