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CT colonography: effect of computer-aided detection of colonic polyps as a second and concurrent reader for general radiologists with moderate experience in CT colonography

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Abstract

Objectives

To assess the effectiveness of computer-aided detection (CAD) as a second reader or concurrent reader in helping radiologists who are moderately experienced in computed tomographic colonography (CTC) to detect colorectal polyps.

Methods

Seventy CTC datasets (34 patients: 66 polyps ≥6 mm; 36 patients: no abnormalities) were retrospectively reviewed by seven radiologists with moderate CTC experience. After primary unassisted evaluation, a CAD second read and, after a time interval of ≥4 weeks, a CAD concurrent read were performed. Areas under the receiver operating characteristic (ROC) curve (AUC), along with per-segment, per-polyp and per-patient sensitivities, and also reading times, were calculated for each reader with and without CAD.

Results

Of seven readers, 86 % and 71 % achieved a higher accuracy (segment-level AUC) when using CAD as second and concurrent reader respectively. Average segment-level AUCs with second and concurrent CAD (0.853 and 0.864) were significantly greater (p < 0.0001) than average AUC in the unaided evaluation (0.781). Per-segment, per-polyp, and per-patient sensitivities for polyps ≥6 mm were significantly higher in both CAD reading paradigms compared with unaided evaluation. Second-read CAD reduced readers’ average segment and patient specificity by 0.007 and 0.036 (p = 0.005 and 0.011), respectively.

Conclusions

CAD significantly improves the sensitivities of radiologists moderately experienced in CTC for polyp detection, both as second reader and concurrent reader.

Key Points

• CAD helps radiologists with moderate CTC experience to detect polyps ≥6 mm.

• Second and concurrent read CAD increase the radiologist’s sensitivity for detecting polyps ≥6 mm.

• Second read CAD slightly decreases specificity compared with an unassisted read.

• Concurrent read CAD is significantly more time-efficient than second read CAD.

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Acknowledgements

The scientific guarantor of this publication is Thomas Mang MD (Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria).

The authors of this manuscript declare that six study contributors (L.B., V.A., G.H., V.R., M.S., M.W.) are employees of Siemens Medical Solutions USA. The study coordinator (non-author), as well as all seven readers (D.C., A.J.C, A.S.L.T, R.N., R.S., S.S., R.F.R.T) received financial research support, as well as reimbursement of travel expenses, from Siemens Medical Solutions USA, Inc.

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

Siemens Medical Solutions USA (Malvern, PA) provided equipment (hardware and software) and technical assistance during the study.

Nancy A. Obuchowski, PhD (Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA) and Michael Weber, PhD (Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria) kindly provided statistical advice for this manuscript. A contract with a large statistical group was established to have an independent statistician (N.O.) perform the analysis.

Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board. Some study subjects or cohorts have been previously reported by Pickhardt et al. [1]; Mang et al. [31] and Mang et al. [36]

Methodology: retrospective (from multiple sites), multi-reader multi-case study, diagnostic study, performed at one institution.

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Authors and Affiliations

  1. Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria

    Thomas Mang, Helmut Ringel & Michael Weber

  2. H IM SY US, Siemens Medical Solutions, 19355, Malvern, PA, USA

    Luca Bogoni, Vikram X. Anand, Gerardo Hermosillo, Vikas Raykar, Marcos Salganicoff & Matthias Wolf

  3. Temple University, Philadelphia, PA, USA

    Dass Chandra

  4. Tristate Imaging Consultants, Abington, PA, USA

    Andrew J. Curtin

  5. University of Pennsylvania, Philadelphia, PA, USA

    Anna S. Lev-Toaff

  6. Diagnostic Imaging Associates, Wilmington, DE, USA

    Ralph Noah

  7. The Chester County Hospital, West Chester, PA, USA

    Robert Shaw

  8. Albert Einstein Medical Center, Philadelphia, PA, USA

    Susan Summerton

  9. Wake Forest University School of Medicine, Winston-Salem, NC, USA

    Rafel F.R. Tappouni

  10. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA

    Nancy A. Obuchowski

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  1. Thomas Mang
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Correspondence to Thomas Mang.

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Mang, T., Bogoni, L., Anand, V.X. et al. CT colonography: effect of computer-aided detection of colonic polyps as a second and concurrent reader for general radiologists with moderate experience in CT colonography. Eur Radiol 24, 1466–1476 (2014). https://doi.org/10.1007/s00330-014-3158-1

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  • DOI: https://doi.org/10.1007/s00330-014-3158-1

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