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15.06.2018 | Computer Applications

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

verfasst von: Lorenzo Vassallo, Alberto Traverso, Michelangelo Agnello, Christian Bracco, Delia Campanella, Gabriele Chiara, Maria Evelina Fantacci, Ernesto Lopez Torres, Antonio Manca, Marco Saletta, Valentina Giannini, Simone Mazzetti, Michele Stasi, Piergiorgio Cerello, Daniele Regge

Erschienen in: European Radiology | Ausgabe 1/2019

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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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Titel: A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies
verfasst von: Lorenzo Vassallo
Alberto Traverso
Michelangelo Agnello
Christian Bracco
Delia Campanella
Gabriele Chiara
Maria Evelina Fantacci
Ernesto Lopez Torres
Antonio Manca
Marco Saletta
Valentina Giannini
Simone Mazzetti
Michele Stasi
Piergiorgio Cerello
Daniele Regge
Publikationsdatum: 15.06.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 1/2019
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-018-5528-6

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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