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Diagnostic validation of a deep learning nodule detection algorithm in low-dose chest CT: determination of optimized dose thresholds in a virtual screening scenario

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A Correction to this article was published on 08 February 2023

This article has been updated

Abstract

Objectives

This study was conducted to evaluate the effect of dose reduction on the performance of a deep learning (DL)–based computer-aided diagnosis (CAD) system regarding pulmonary nodule detection in a virtual screening scenario.

Methods

Sixty-eight anthropomorphic chest phantoms were equipped with 329 nodules (150 ground glass, 179 solid) with four sizes (5 mm, 8 mm, 10 mm, 12 mm) and scanned with nine tube voltage/current combinations. The examinations were analyzed by a commercially available DL-based CAD system. The results were compared by a comparison of proportions. Logistic regression was performed to evaluate the impact of tube voltage, tube current, nodule size, nodule density, and nodule location.

Results

The combination with the lowest effective dose (E) and unimpaired detection rate was 80 kV/50 mAs (sensitivity: 97.9%, mean false-positive rate (FPR): 1.9, mean CTDIvol: 1.2 ± 0.4 mGy, mean E: 0.66 mSv). Logistic regression revealed that tube voltage and current had the greatest impact on the detection rate, while nodule size and density had no significant influence.

Conclusions

The optimal tube voltage/current combination proposed in this study (80 kV/50 mAs) is comparable to the proposed combinations in similar studies, which mostly dealt with conventional CAD software. Modification of tube voltage and tube current has a significant impact on the performance of DL-based CAD software in pulmonary nodule detection regardless of their size and composition.

Key Points

• Modification of tube voltage and tube current has a significant impact on the performance of deep learning–based CAD software.

• Nodule size and composition have no significant impact on the software’s performance.

• The optimal tube voltage/current combination for the examined software is 80 kV/50 mAs.

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Abbreviations

AI:

Artificial intelligence

CAD system:

Computer-aided diagnosis system

CNR:

Contrast-to-noise ratio

CT:

Computed tomography

CTU:

Clinical trial unit

DL:

Deep learning

DLP:

Dose length product

HFG:

Humanforschungsgesetz

HFV:

Humanforschungsverordnung

KV:

Kilovolt

LCC:

Lung Cancer Center

MIP:

Maximum intensity projection

NLST:

National Lung Cancer Screening Trial

OP:

Operation

PACS:

Picture Archiving and Communication System

PET-CT:

Positron-emission-tomography with computed tomography

SNR:

Signal-to-noise ratio

Sv:

Sievert

T1 (a, b, c):

Tumor stage 1 (a, b, c)

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Acknowledgements

Dr. Peters reports grants from clinical trial unit (CTU) Bern and European School of Radiology (ESOR) outside the submitted work.

Dr. Heverhagen reports grants from Bayer Healthcare AG, grants from Guerbet AG, grants from Siemens Healthineers, and grants from Bracco Imaging Spa outside the submitted work.

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland

    Alan A. Peters, Adrian T. Huber, Verena C. Obmann, Johannes T. Heverhagen, Andreas Christe & Lukas Ebner

  2. Department of BioMedical Research, Experimental Radiology, University of Bern, 3008, Bern, Switzerland

    Johannes T. Heverhagen

  3. Department of Radiology, The Ohio State University, Columbus, OH, USA

    Johannes T. Heverhagen

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  1. Alan A. Peters
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  4. Johannes T. Heverhagen
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  5. Andreas Christe
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Correspondence to Alan A. Peters.

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Guarantor

The scientific guarantor of this publication is Dr. Peters.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise (Dr. Christe).

Informed consent

Written informed consent was waived by the Institutional Review Board (phantom study).

Ethical approval

Institutional Review Board approval was not required for this phantom study.

Study subjects or cohorts overlap

Some study subjects or cohorts (chest phantoms) have been reported in a previous publication:

Huber A, Landau J, Ebner L et al. (2016) Performance of ultralow-dose CT with iterative reconstruction in lung cancer screening: limiting radiation exposure to the equivalent of conventional chest X-ray imaging. Eur Radiol 26:3643–3652.

Methodology

• Phantom study

• Diagnostic or prognostic study

• Performed at one institution

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The original online version of this article was revised: the figures 2 & 3 have been replaced by their correct versions.

The original online version of this article was revised: the figures 1, 2 & 3 have been replaced by their correct versions.

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Peters, A.A., Huber, A.T., Obmann, V.C. et al. Diagnostic validation of a deep learning nodule detection algorithm in low-dose chest CT: determination of optimized dose thresholds in a virtual screening scenario. Eur Radiol 32, 4324–4332 (2022). https://doi.org/10.1007/s00330-021-08511-7

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