A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies.
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...
| Publicado en: | European Radiology Vol. 29; no. 1; pp. 144 - 153 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2019
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133531095&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133531095 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jan2019 vid: 29 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 133531095 133531095 NLM29948089 133531095 10.1007/s00330-018-5528-6 NLM29948089 133531095 ppf: 144 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies. aug: au: Vassallo, Lorenzo Traverso, Alberto Agnello, Michelangelo Bracco, Christian Campanella, Delia Chiara, Gabriele Fantacci, Maria Evelina Lopez Torres, Ernesto Manca, Antonio Saletta, Marco Giannini, Valentina Mazzetti, Simone Stasi, Michele Cerello, Piergiorgio Regge, Daniele affil: Department of Radiology at Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, Turin, Italy sug: subj: Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted Methods Middle Age Male Early Detection of Cancer Methods Retrospective Design Female Aged Aged, 80 and Over Observer Bias Reproducibility of Results Adult Tomography, X-Ray Computed Methods Young Adult Sensitivity and Specificity Human Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Adult: 19-44 years Male Female ab: 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. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|