Literature DB >> 24300380

How to assess background activity: introducing a histogram-based analysis as a first step for accurate one-step PET quantification.

Irene A Burger1, Hebert A Vargas, Brad J Beattie, Debra A Goldman, Junting Zheng, Steven M Larson, John L Humm, Charles R Schmidtlein.   

Abstract

Many common PET segmentation methods for malignant lesions use surrounding background activity as a reference. To date, background has to be measured by drawing a second volume of interest (VOI) in nearby, undiseased tissue. This is time consuming as two VOIs have to be determined for each lesion. The aim of our study was to analyse whether background activity in different organs and body regions could be calculated from the tumour VOI by histogram analyses. The institutional review board waived informed consent for this retrospective study. For each of the following tumour types and areas - head and neck (neck), lung, hepatic metastasis (liver), melanoma (skin), and cervix (pelvis) - 10 consecutive patients with biopsy-proven tumours who underwent (18)F-fluorodeoxyglucose-PET in January 2012 were retrospectively selected. One lesion was selected and two readers drew a cubical VOI around the lesion (VOItumour) and over the background (VOIBG). The mean value of VOIBG was compared with the mode of the histogram, using equivalence testing with an equivalence margin of ±0.5 SUV. Inter-reader agreement was analysed for the mean background, and the mode of the VOItumour histogram was assessed using the concordance correlation coefficient. For both readers, the mode of VOItumour was equivalent to the mean of VOIBG (P<0.0001 for R1 and R2). The inter-reader agreement was almost perfect, with a concordance correlation coefficient of greater than 0.92 for both the mode of VOItumour and the mean of VOIBG. Background activity determined within a tumour VOI using histogram analysis is equivalent to separately measured mean background values, with an almost perfect inter-reader agreement. This could facilitate PET quantification methods based on background values without increasing workload.

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Year:  2014        PMID: 24300380     DOI: 10.1097/MNM.0000000000000045

Source DB:  PubMed          Journal:  Nucl Med Commun        ISSN: 0143-3636            Impact factor:   1.690


  8 in total

1.  Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211.

Authors:  Mathieu Hatt; John A Lee; Charles R Schmidtlein; Issam El Naqa; Curtis Caldwell; Elisabetta De Bernardi; Wei Lu; Shiva Das; Xavier Geets; Vincent Gregoire; Robert Jeraj; Michael P MacManus; Osama R Mawlawi; Ursula Nestle; Andrei B Pugachev; Heiko Schöder; Tony Shepherd; Emiliano Spezi; Dimitris Visvikis; Habib Zaidi; Assen S Kirov
Journal:  Med Phys       Date:  2017-05-18       Impact factor: 4.071

2.  Renal Masses Detected on FDG PET/CT in Patients With Lymphoma: Imaging Features Differentiating Primary Renal Cell Carcinomas From Renal Lymphomatous Involvement.

Authors:  Carlos Nicolau; Evis Sala; Anita Kumar; Debra A Goldman; Heiko Schoder; Hedvig Hricak; Hebert Alberto Vargas
Journal:  AJR Am J Roentgenol       Date:  2017-01-17       Impact factor: 3.959

3.  Volume-based quantitative FDG PET/CT metrics and their association with optimal debulking and progression-free survival in patients with recurrent ovarian cancer undergoing secondary cytoreductive surgery.

Authors:  H A Vargas; I A Burger; D A Goldman; M Miccò; R E Sosa; W Weber; D S Chi; H Hricak; E Sala
Journal:  Eur Radiol       Date:  2015-04-28       Impact factor: 5.315

4.  A novel metric for quantification of homogeneous and heterogeneous tumors in PET for enhanced clinical outcome prediction.

Authors:  Arman Rahmim; C Ross Schmidtlein; Andrew Jackson; Sara Sheikhbahaei; Charles Marcus; Saeed Ashrafinia; Madjid Soltani; Rathan M Subramaniam
Journal:  Phys Med Biol       Date:  2015-12-07       Impact factor: 3.609

5.  PET quantification with a histogram derived total activity metric: superior quantitative consistency compared to total lesion glycolysis with absolute or relative SUV thresholds in phantoms and lung cancer patients.

Authors:  Irene A Burger; Hebert Alberto Vargas; Aditya Apte; Bradley J Beattie; John L Humm; Mithat Gonen; Steven M Larson; C Ross Schmidtlein
Journal:  Nucl Med Biol       Date:  2014-02-28       Impact factor: 2.408

6.  18F-FDG PET/CT of Non-Small Cell Lung Carcinoma Under Neoadjuvant Chemotherapy: Background-Based Adaptive-Volume Metrics Outperform TLG and MTV in Predicting Histopathologic Response.

Authors:  Irene A Burger; Ruben Casanova; Seraina Steiger; Lars Husmann; Paul Stolzmann; Martin W Huellner; Alessandra Curioni; Sven Hillinger; C Ross Schmidtlein; Alex Soltermann
Journal:  J Nucl Med       Date:  2016-01-28       Impact factor: 10.057

7.  Test-retest repeatability and interobserver variation of healthy tissue metabolism using 18F-FDG PET/CT of the thorax among lung cancer patients.

Authors:  Afnan A Malaih; Joel T Dunn; Lotte Nygård; David G Kovacs; Flemming L Andersen; Sally F Barrington; Barbara M Fischer
Journal:  Nucl Med Commun       Date:  2022-05-01       Impact factor: 1.698

8.  Relationship between 18F-FDG PET/CT Semi-Quantitative Parameters and International Association for the Study of Lung Cancer, American Thoracic Society/European Respiratory Society Classification in Lung Adenocarcinomas.

Authors:  Lihong Bu; Ning Tu; Ke Wang; Ying Zhou; Xinli Xie; Xingmin Han; Huiqin Lin; Hongyan Feng
Journal:  Korean J Radiol       Date:  2022-01       Impact factor: 3.500

  8 in total

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