Multi-center, multi-vendor validation of deep learning-based attenuation correction in SPECT MPI: data from the international flurpiridaz-301 trial.
Purpose : Although SPECT myocardial perfusion imaging (MPI) is susceptible to artifacts from soft tissue attenuation, most scans are performed without attenuation correction. Deep learning-based attenuation corrected (DLAC) polar maps improved diagnostic accuracy for detection of coronary artery dis...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 50; no. 4; pp. 1028 - 1034 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2023
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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=161898982&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161898982 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Mar2023 vid: 50 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 161898982 10.1007/s00259-022-06045-8 161898982 ppf: 1028 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multi-center, multi-vendor validation of deep learning-based attenuation correction in SPECT MPI: data from the international flurpiridaz-301 trial. aug: au: Hagio, Tomoe Moody, Jonathan B. Poitrasson-Rivière, Alexis Renaud, Jennifer M. Pierce, Lora Buckley, Christopher Ficaro, Edward P. Murthy, Venkatesh L. affil: INVIA Medical Imaging Solutions, Ann Arbor, MI, USA sug: ab: Purpose : Although SPECT myocardial perfusion imaging (MPI) is susceptible to artifacts from soft tissue attenuation, most scans are performed without attenuation correction. Deep learning-based attenuation corrected (DLAC) polar maps improved diagnostic accuracy for detection of coronary artery disease (CAD) beyond non-attenuation-corrected (NAC) polar maps in a large single center study. However, the generalizability of this approach to other institutions with different scanner models and protocols is uncertain. In this study, we evaluated the diagnostic performance of DLAC compared to NAC for detection of CAD as defined by invasive coronary angiography (ICA) in a large multi-center trial. Methods: During the phase 3 flurpiridaz multi-center diagnostic clinical trial, conducted over 74 international sites, patients with known or suspected CAD who were referred for a clinically indicated ICA were enrolled. Using receiver operating characteristic (ROC) analysis, we evaluated the detectability of obstructive CAD, defined by quantitative coronary angiography by a core laboratory, using total perfusion deficit (TPD) as an integrated measure of defect extent and severity on DLAC polar maps compared to NAC polar maps. This was also compared against the visual scoring of three expert core lab readers. Results: Out of 755 patients, 722 (69% male) had evaluable SPECT and ICA for this study. ROC analysis demonstrated significant improvement in detecting per-patient obstructive CAD with DLAC over NAC with area under the curve (AUC) of 0.752 (95% CI: 0.711–0.792) for DLAC compared to 0.717 (0.675–0.759) for NAC (p value = 0.016). Compared to the consensus of expert readers AUC = 0.743 (0.701–0.784), DLAC was comparable (p value = 0.913), whereas NAC underperformed (p value = 0.051). Conclusion: DL-based attenuation correction improves diagnostic performance of SPECT MPI for detecting CAD in data from a large multi-center clinical trial regardless of SPECT camera model or protocol. Trial registration: A Phase 3 Multi-center Study to Assess PET Imaging of Flurpiridaz F 18 Injection in Patients With CAD, ClinicalTrials.gov Identifier: NCT01347710, registered on 4 May 2011. https://clinicaltrials.gov/ct2/show/study/NCT01347710 pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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