Literature DB >> 27261515

Multiscale Texture Analysis: From 18F-FDG PET Images to Histologic Images.

Fanny Orlhac1, Benoit Thézé2, Michaël Soussan2,3, Raphaël Boisgard2, Irène Buvat2.   

Abstract

Characterizing tumor heterogeneity using texture indices derived from PET images has shown promise in predicting treatment response and patient survival in some types of cancer. Yet, the relationship between PET-derived texture indices, precise tracer distribution, and biologic heterogeneity needs to be clarified. We investigated this relationship using PET images, autoradiographic images, and histologic images.
METHODS: Three mice bearing orthotopically implanted mammary tumors derived from transgenic MMTV-PyMT mice were scanned with 18F-FDG PET/CT. The tumors were then sliced, and the slices were imaged with autoradiography and stained with hematoxylin and eosin. Six texture indices derived from the PET images, autoradiographic images, and histologic images were compared for their ability to capture heterogeneity on different scales.
RESULTS: The PET-derived indices correlated significantly with the autoradiography-derived ones (R = 0.57-0.85), but the values differed in magnitude. The histology-derived indices correlated poorly with the autoradiography- and PET-derived ones (R = 0.06-0.54). All indices were slightly to moderately influenced by the difference in voxel size and spatial resolution in the autoradiographic images. The autoradiography-derived indices differed significantly (P < 0.05) between regions with a high density of cells and regions with a low density and between regions with different spatial arrangements of cells.
CONCLUSION: Heterogeneity derived in vivo from PET images accurately reflects the heterogeneity of tracer uptake derived ex vivo from autoradiographic images. Various tumor-cell densities and spatial cell distributions seen on histologic images can be distinguished using texture indices derived from autoradiographic images despite the difference in voxel size and spatial resolution. Yet, tumor texture derived from PET images only coarsely reflects the spatial distribution and density of tumor cells.
© 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Entities:  

Keywords:  PET; autoradiography; histology; texture analysis; tumor heterogeneity

Mesh:

Substances:

Year:  2016        PMID: 27261515     DOI: 10.2967/jnumed.116.173708

Source DB:  PubMed          Journal:  J Nucl Med        ISSN: 0161-5505            Impact factor:   10.057


  18 in total

1.  Correlation between tumour characteristics, SUV measurements, metabolic tumour volume, TLG and textural features assessed with 18F-FDG PET in a large cohort of oestrogen receptor-positive breast cancer patients.

Authors:  Charles Lemarignier; Antoine Martineau; Luis Teixeira; Laetitia Vercellino; Marc Espié; Pascal Merlet; David Groheux
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-02-10       Impact factor: 9.236

2.  Use of 18F-FDG PET/CT texture analysis to diagnose cardiac sarcoidosis.

Authors:  Osamu Manabe; Hiroshi Ohira; Kenji Hirata; Souichiro Hayashi; Masanao Naya; Ichizo Tsujino; Tadao Aikawa; Kazuhiro Koyanagawa; Noriko Oyama-Manabe; Yuuki Tomiyama; Keiichi Magota; Keiichiro Yoshinaga; Nagara Tamaki
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-10-16       Impact factor: 9.236

3.  Spatially coherent modeling of 3D FDG-PET data for assessment of intratumoral heterogeneity and uptake gradients.

Authors:  Eric Wolsztynski; Finbarr O'Sullivan; Janet F Eary
Journal:  J Med Imaging (Bellingham)       Date:  2022-07-29

4.  Experimental Multicenter and Multivendor Evaluation of the Performance of PET Radiomic Features Using 3-Dimensionally Printed Phantom Inserts.

Authors:  Elisabeth Pfaehler; Joyce van Sluis; Bram B J Merema; Peter van Ooijen; Ralph C M Berendsen; Floris H P van Velden; Ronald Boellaard
Journal:  J Nucl Med       Date:  2019-08-16       Impact factor: 11.082

5.  Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer.

Authors:  Kieran G Foley; Robert K Hills; Beatrice Berthon; Christopher Marshall; Craig Parkinson; Wyn G Lewis; Tom D L Crosby; Emiliano Spezi; Stuart Ashley Roberts
Journal:  Eur Radiol       Date:  2017-08-02       Impact factor: 5.315

6.  Characterisation of malignant peripheral nerve sheath tumours in neurofibromatosis-1 using heterogeneity analysis of 18F-FDG PET.

Authors:  Gary J R Cook; Eitan Lovat; Muhammad Siddique; Vicky Goh; Rosalie Ferner; Victoria S Warbey
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-06-07       Impact factor: 9.236

7.  Repeatability of 18 F-FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method.

Authors:  Elisabeth Pfaehler; Roelof J Beukinga; Johan R de Jong; Riemer H J A Slart; Cornelis H Slump; Rudi A J O Dierckx; Ronald Boellaard
Journal:  Med Phys       Date:  2018-12-28       Impact factor: 4.071

8.  Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners.

Authors:  Sylvain Reuzé; Fanny Orlhac; Cyrus Chargari; Christophe Nioche; Elaine Limkin; François Riet; Alexandre Escande; Christine Haie-Meder; Laurent Dercle; Sébastien Gouy; Irène Buvat; Eric Deutsch; Charlotte Robert
Journal:  Oncotarget       Date:  2017-06-27

9.  Clinical Utility of FDG PET/CT in Patients with Autoimmune Pancreatitis: a Case-Control Study.

Authors:  Mei-Fang Cheng; Yue Leon Guo; Ruoh-Fang Yen; Yi-Chieh Chen; Chi-Lun Ko; Yu-Wen Tien; Wei-Chih Liao; Chia-Ju Liu; Yen-Wen Wu; Hsiu-Po Wang
Journal:  Sci Rep       Date:  2018-02-26       Impact factor: 4.379

10.  Selected PET radiomic features remain the same.

Authors:  Tetsuya Tsujikawa; Hideaki Tsuyoshi; Masafumi Kanno; Shizuka Yamada; Masato Kobayashi; Norihiko Narita; Hirohiko Kimura; Shigeharu Fujieda; Yoshio Yoshida; Hidehiko Okazawa
Journal:  Oncotarget       Date:  2018-04-17
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.