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

Peters, Alan A.; Huber, Adrian T.; Obmann, Verena C.; Heverhagen, Johannes T.; Christe, Andreas; Ebner, Lukas (2022). Diagnostic validation of a deep learning nodule detection algorithm in low-dose chest CT: determination of optimized dose thresholds in a virtual screening scenario. European radiology, 32(6), pp. 4324-4332. Springer 10.1007/s00330-021-08511-7

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Radiologie
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Radiologie

UniBE Contributor:

Peters, Alan Arthur; Huber, Adrian Thomas; Obmann, Verena Carola; Heverhagen, Johannes; Christe, Andreas and Ebner, Lukas

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1432-1084

Publisher:

Springer

Language:

English

Submitter:

Maria de Fatima Henriques Bernardo

Date Deposited:

15 Feb 2022 16:17

Last Modified:

22 May 2022 00:12

Publisher DOI:

10.1007/s00330-021-08511-7

PubMed ID:

35059804

Uncontrolled Keywords:

Artificial intelligence Computer-assisted diagnosis Deep learning Lung neoplasms Radiographic phantoms

BORIS DOI:

10.48350/164889

URI:

https://boris.unibe.ch/id/eprint/164889

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