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Computer-Aided Detection of Pulmonary Nodules in Computed Tomography Using ClearReadCT

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

This study evaluates the accuracy of a computer-aided detection (CAD) application for pulmonary nodular lesions (PNL) in computed tomography (CT) scans, the ClearReadCT (Riverain Technologies). The study was retrospective for 106 biopsied PNLs from 100 patients. Seventy-five scans were Contrast-Enhanced (CECT) and 25 received no enhancer (NECT). Axial reconstructions in soft-tissue and lung kernel were applied at three different slice thicknesses, 0.75 mm (CECT/NECT n = 25/6), 1.5 mm (n = 18/9) and 3.0 mm (n = 43/18). We questioned the effect of (1) enhancer, (2) kernel and (3) slice thickness on the CAD performance. Our main findings are: (1) Vessel suppression is effective and specific in both NECT and CECT. (2) Contrast enhancement significantly increased the CAD sensitivity from 60% in NECT to 80% in CECT, P = 0.025 Fischer’s exact test. (3) The CAD sensitivity was 84% in 3 mm slices compared to 68% in 0.75 mm slices, P > 0.2 Fischer’s exact test. (4) Small lesions of low attenuation were detected with higher sensitivity. (5) Lung kernel reconstructions increased the false positive rate without affecting the sensitivity (P > 0.05 McNemar’s test). In conclusion, ClearReadCT showed an optimized sensitivity of 84% and a positive predictive value of 67% in enhanced lung scans with thick, soft kernel reconstructions. NECT, thin slices and lung kernel reconstruction were associated with inferior performance.

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Abbreviations

16xDEF:

Somatom definition AS 16-row CT scanner

16xEMO:

Somatom emotion 16-row CT scanner

64xDEF:

Somatom definition AS 64-row CT scanner

CAD:

Computer-aided detection

CECT:

Contrast-enhanced computed tomography

CT:

Computed tomography

FN:

False negative

FP:

False positive

HU:

Hounsfield Units

NECT:

Non-enhanced computed tomography

PACS:

Picture archiving and communication system

PNL:

Pulmonary nodular lesions

PPV:

Positive predictive value

TN:

True negative

TP:

True positive

TPR:

True positive rate (sensitivity)

V1:

CAD version 1

V2:

CAD version 2

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Author information

Authors and Affiliations

  1. Institute of Diagnostic and Interventional Radiology, University Hospital Jena, Am Klinikum 1, 07747, Jena, Germany

    Anne-Kathrin Wagner, Ulf Teichgräber & Ismini Papageorgiou

  2. Institute of Radiology, Südharz Hospital Nordhausen, Dr.-Robert-Koch street 39, 99734, Nordhausen, Germany

    Anne-Kathrin Wagner, Ansgar Malich & Ismini Papageorgiou

  3. Department of Thoracic Surgery, Südharz Hospital Nordhausen, Dr.-Robert-Koch street 39, 99734, Nordhausen, Germany

    Arno Hapich

  4. Institute of Diagnostic and Interventional Neuroradiology, University Medicine Göttingen, Robert Koch street 40, 37075, Göttingen, Germany

    Marios Nikos Psychogios

Authors
  1. Anne-Kathrin Wagner
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  2. Arno Hapich
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  3. Marios Nikos Psychogios
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  4. Ulf Teichgräber
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  5. Ansgar Malich
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  6. Ismini Papageorgiou
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Corresponding author

Correspondence to Ismini Papageorgiou.

Ethics declarations

The study was approved by the Ethics Committee of the University Hospital of Jena and was performed in accordance with the ethical standards of the 1964 Declaration of Helsinki and its amendments, the European Regulation 536/2014 as well as with the good clinical and scientific practice protocols of the University of Jena. For this type of study a formal consent was not required.

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Highlights

• ClearReadCT performs vessel subtraction in both contrasted and non-contrasted scans

• The optimized CAD sensitivity was 84% with a PPV of 67% in thick slab, soft kernel, contrast-enhanced images

• ClearReadCT is designed for the detection of small lesions with low attenuation values

This article is part of the Topical Collection on Image & Signal Processing

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Wagner, AK., Hapich, A., Psychogios, M.N. et al. Computer-Aided Detection of Pulmonary Nodules in Computed Tomography Using ClearReadCT. J Med Syst 43, 58 (2019). https://doi.org/10.1007/s10916-019-1180-1

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  • DOI: https://doi.org/10.1007/s10916-019-1180-1

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