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A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies

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Abstract

Objectives

To compare unassisted and CAD-assisted detection and time efficiency of radiologists in reporting lung nodules on CT scans taken from patients with extra-thoracic malignancies using a Cloud-based system.

Materials and methods

Three radiologists searched for pulmonary nodules in patients with extra-thoracic malignancy who underwent CT (slice thickness/spacing 2 mm/1.7 mm) between September 2015 and March 2016. All nodules detected by unassisted reading were measured and coordinates were uploaded on a cloud-based system. CAD marks were then reviewed by the same readers using the cloud-based interface. To establish the reference standard all nodules ≥ 3 mm detected by at least one radiologist were validated by two additional experienced radiologists in consensus. Reader detection rate and reporting time with and without CAD were compared. The study was approved by the local ethics committee. All patients signed written informed consent.

Results

The series included 225 patients (age range 21–90 years, mean 62 years), including 75 patients having at least one nodule, for a total of 215 nodules. Stand-alone CAD sensitivity for lesions ≥ 3 mm was 85% (183/215, 95% CI: 82–91); mean false-positive rate per scan was 3.8. Sensitivity across readers in detecting lesions ≥ 3 mm was statistically higher using CAD: 65% (95% CI: 61–69) versus 88% (95% CI: 86–91, p<0.01). Reading time increased by 11% using CAD (296 s vs. 329 s; p<0.05).

Conclusion

In patients with extra-thoracic malignancies, CAD-assisted reading improves detection of ≥ 3-mm lung nodules on CT, slightly increasing reading time.

Key Points

• CAD-assisted reading improves the detection of lung nodules compared with unassisted reading on CT scans of patients with primary extra-thoracic tumour, slightly increasing reading time.

• Cloud-based CAD systems may represent a cost-effective solution since CAD results can be reviewed while a separated cloud back-end is taking care of computations.

• Early identification of lung nodules by CAD-assisted interpretation of CT scans in patients with extra-thoracic primary tumours is of paramount importance as it could anticipate surgery and extend patient life expectancy.

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Abbreviations

CAD:

Computer-aided detection

CT:

Computed tomography

MIP:

Maximum intensity projection

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Funding

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

Author information

Authors and Affiliations

  1. Department of Radiology at Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, Turin, Italy

    Lorenzo Vassallo, Delia Campanella, Gabriele Chiara, Antonio Manca, Valentina Giannini, Simone Mazzetti & Daniele Regge

  2. Department of Surgical Sciences, University of Turin, A.O.U. Città della Salute e della Scienza, Via Genova 3, 10126, Turin, Italy

    Lorenzo Vassallo, Valentina Giannini, Simone Mazzetti & Daniele Regge

  3. Department of Applied Science and Technology, Polytechnic University of Turin, Corso Duca Degli Abruzzi 24, 10129, Turin, Italy

    Alberto Traverso & Michelangelo Agnello

  4. Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Dr Tanslaan 12, 6229 ET, Maastricht, The Netherlands

    Alberto Traverso

  5. Medical Physics Unit, Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, Turin, Italy

    Christian Bracco & Michele Stasi

  6. Pisa Section of INFN, Largo Bruno Pontecorvo 3, 56127, Pisa, Italy

    Maria Evelina Fantacci

  7. Turin Section of INFN, Via Pietro Giuria 1, 10125, Turin, Italy

    Ernesto Lopez Torres, Marco Saletta & Piergiorgio Cerello

Authors
  1. Lorenzo Vassallo
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  2. Alberto Traverso
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  3. Michelangelo Agnello
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  4. Christian Bracco
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  5. Delia Campanella
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  6. Gabriele Chiara
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  7. Maria Evelina Fantacci
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  8. Ernesto Lopez Torres
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  9. Antonio Manca
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  10. Marco Saletta
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  11. Valentina Giannini
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  12. Simone Mazzetti
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  13. Michele Stasi
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  14. Piergiorgio Cerello
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  15. Daniele Regge
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Corresponding author

Correspondence to Lorenzo Vassallo.

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Guarantor

The scientific guarantor of this publication is Alberto Traverso.

Conflict of interest

The authors of this article 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.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• observational

• performed at one institution

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Vassallo, L., Traverso, A., Agnello, M. et al. A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies. Eur Radiol 29, 144–152 (2019). https://doi.org/10.1007/s00330-018-5528-6

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  • DOI: https://doi.org/10.1007/s00330-018-5528-6

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