18F-FDG PET/CT assessment of histopathologically confirmed mediastinal lymph nodes in non-small cell lung cancer using a penalised likelihood reconstruction.
Purpose: To investigate whether using a Bayesian penalised likelihood reconstruction (BPL) improves signal-to-background (SBR), signal-to-noise (SNR) and SUVmax when evaluating mediastinal nodal disease in non-small cell lung cancer (NSCLC) compared to ordered subset expectation maximum (OSEM) recon...
| Publicado en: | European Radiology Vol. 26; no. 11; pp. 4098 - 4107 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Nov2016
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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=118554737&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118554737 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Nov2016 vid: 26 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 118554737 118554737 NLM26914696 118554737 10.1007/s00330-016-4253-2 NLM26914696 118554737 ppf: 4098 ppct: 9 formats: fmt: @attributes: type: P tig: atl: 18F-FDG PET/CT assessment of histopathologically confirmed mediastinal lymph nodes in non-small cell lung cancer using a penalised likelihood reconstruction. aug: au: Teoh, Eugene McGowan, Daniel Bradley, Kevin Belcher, Elizabeth Black, Edward Moore, Alastair Sykes, Annemarie Gleeson, Fergus Teoh, Eugene J McGowan, Daniel R Bradley, Kevin M Gleeson, Fergus V affil: Department of Radiology, Churchill Hospital , Oxford University Hospitals NHS Foundation Trust , Oxford OX3 7LE UK sug: subj: Carcinoma, Non-Small-Cell Lung Radiopharmaceuticals Lung Neoplasms Fludeoxyglucose F 18 Lymph Nodes Pathology Lung Neoplasms Pathology Neoplasm Metastasis Lymph Nodes Aged, 80 and Over Aged Neoplasm Staging Human Male Adult Middle Age Female Carcinoma, Non-Small-Cell Lung Pathology Diagnostic Imaging Algorithms Epidemiological Research Validation Studies Comparative Studies Evaluation Research Multicenter Studies Aged, 80 & over Aged: 65+ years Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Purpose: To investigate whether using a Bayesian penalised likelihood reconstruction (BPL) improves signal-to-background (SBR), signal-to-noise (SNR) and SUVmax when evaluating mediastinal nodal disease in non-small cell lung cancer (NSCLC) compared to ordered subset expectation maximum (OSEM) reconstruction.Materials and Methods: 18F-FDG PET/CT scans for NSCLC staging in 47 patients (112 nodal stations with histopathological confirmation) were reconstructed using BPL and compared to OSEM. Node and multiple background SUV parameters were analysed semi-quantitatively and visually.Results: Comparing BPL to OSEM, there were significant increases in SUVmax (mean 3.2-4.0, p<0.0001), SBR (mean 2.2-2.6, p<0.0001) and SNR (mean 27.7-40.9, p<0.0001). Mean background SNR on OSEM was 10.4 (range 7.6-14.0), increasing to 12.4 (range 8.2-16.7, p<0.0001). Changes in background SUVs were minimal (largest mean difference 0.17 for liver SUVmean, p<0.001). There was no significant difference between either algorithm on receiver operating characteristic analysis (p=0.26), although on visual analysis, there was an increase in sensitivity and small decrease in specificity and accuracy on BPL.Conclusion: BPL increases SBR, SNR and SUVmax of mediastinal nodes in NSCLC compared to OSEM, but did not improve the accuracy for determining nodal involvement.Key Points: • Penalised likelihood PET reconstruction was applied for assessing mediastinal nodes in NSCLC. • The new reconstruction generated significant increases in signal-to-background, signal-to-noise and SUVmax. • This led to an improvement in visual sensitivity using the new algorithm. • Higher SUV max thresholds may be appropriate for semi-quantitative analyses with penalised likelihood. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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