Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance.
Objectives: To evaluate the diagnostic performance of a deep learning algorithm for automated detection of small 18F-FDG-avid pulmonary nodules in PET scans, and to assess whether novel block sequential regularized expectation maximization (BSREM) reconstruction affects detection accuracy as compare...
| Publicado en: | European Radiology Vol. 30; no. 4; pp. 2031 - 2041 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2020
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| 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=142141856&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142141856 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Apr2020 vid: 30 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142141856 142141856 NLM31822970 10.1007/s00330-019-06498-w NLM31822970 142141856 ppf: 2031 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance. aug: au: Schwyzer, Moritz Martini, Katharina Benz, Dominik C. Burger, Irene A. Ferraro, Daniela A. Kudura, Ken Treyer, Valerie von Schulthess, Gustav K. Kaufmann, Philipp A. Huellner, Martin W. Messerli, Michael affil: Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland sug: subj: Lung Neoplasms Solitary Pulmonary Nodule Aged, 80 and Over Male Radiopharmaceuticals Retrospective Design Lung Neoplasms Pathology Image Processing, Computer Assisted Methods Artificial Intelligence Algorithms Female Aged Middle Age Sensitivity and Specificity Adult Solitary Pulmonary Nodule Pathology Fludeoxyglucose F 18 Impact of Events Scale Scales Aged, 80 & over Aged: 65+ years Middle Aged: 45-64 years Adult: 19-44 years Male Female ab: Objectives: To evaluate the diagnostic performance of a deep learning algorithm for automated detection of small 18F-FDG-avid pulmonary nodules in PET scans, and to assess whether novel block sequential regularized expectation maximization (BSREM) reconstruction affects detection accuracy as compared to ordered subset expectation maximization (OSEM) reconstruction.Methods: Fifty-seven patients with 92 18F-FDG-avid pulmonary nodules (all ≤ 2 cm) undergoing PET/CT for oncological (re-)staging were retrospectively included and a total of 8824 PET images of the lungs were extracted using OSEM and BSREM reconstruction. Per-slice and per-nodule sensitivity of a deep learning algorithm was assessed, with an expert readout by a radiologist/nuclear medicine physician serving as standard of reference. Receiver-operator characteristic (ROC) curve of OSEM and BSREM were assessed and the areas under the ROC curve (AUC) were compared. A maximum standardized uptake value (SUVmax)-based sensitivity analysis and a size-based sensitivity analysis with subgroups defined by nodule size was performed.Results: The AUC of the deep learning algorithm for nodule detection using OSEM reconstruction was 0.796 (CI 95%; 0.772-0.869), and 0.848 (CI 95%; 0.828-0.869) using BSREM reconstruction. The AUC was significantly higher for BSREM compared to OSEM (p = 0.001). On a per-slice analysis, sensitivity and specificity were 66.7% and 79.0% for OSEM, and 69.2% and 84.5% for BSREM. On a per-nodule analysis, the overall sensitivity of OSEM was 81.5% compared to 87.0% for BSREM.Conclusions: Our results suggest that machine learning algorithms may aid detection of small 18F-FDG-avid pulmonary nodules in clinical PET/CT. AI performed significantly better on images with BSREM than OSEM.Key Points: • The diagnostic value of deep learning for detecting small lung nodules (≤ 2 cm) in PET images using BSREM and OSEM reconstruction was assessed. • BSREM yields higher SUVmaxof small pulmonary nodules as compared to OSEM reconstruction. • The use of BSREM translates into a higher detectability of small pulmonary nodules in PET images as assessed with artificial intelligence. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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