Literature DB >> 24418285

FDG uptake heterogeneity evaluated by fractal analysis improves the differential diagnosis of pulmonary nodules.

Kenta Miwa1, Masayuki Inubushi2, Kei Wagatsuma3, Michinobu Nagao4, Taisuke Murata5, Masamichi Koyama6, Mitsuru Koizumi7, Masayuki Sasaki8.   

Abstract

PURPOSE: The present study aimed to determine whether fractal analysis of morphological complexity and intratumoral heterogeneity of FDG uptake can help to differentiate malignant from benign pulmonary nodules.
MATERIALS AND METHODS: We retrospectively analyzed data from 54 patients with suspected non-small cell lung cancer (NSCLC) who were examined by FDG PET/CT. Pathological assessments of biopsy specimens confirmed 35 and 19 nodules as NSCLC and inflammatory lesions, respectively. The morphological fractal dimension (m-FD), maximum standardized uptake value (SUV(max)) and density fractal dimension (d-FD) of target nodules were calculated from CT and PET images. Fractal dimension is a quantitative index of morphological complexity and tracer uptake heterogeneity; higher values indicate increased complexity and heterogeneity.
RESULTS: The m-FD, SUV(max) and d-FD significantly differed between malignant and benign pulmonary nodules (p<0.05). Although the diagnostic ability was better for d-FD than m-FD and SUV(max), the difference did not reach statistical significance. Tumor size correlated significantly with SUV(max) (r=0.51, p<0.05), but not with either m-FD or d-FD. Furthermore, m-FD combined with either SUV(max) or d-FD improved diagnostic accuracy to 92.6% and 94.4%, respectively.
CONCLUSION: The d-FD of intratumoral heterogeneity of FDG uptake can help to differentially diagnose malignant and benign pulmonary nodules. The SUV(max) and d-FD obtained from FDG-PET images provide different types of information that are equally useful for differential diagnoses. Furthermore, the morphological complexity determined by CT combined with heterogeneous FDG uptake determined by PET improved diagnostic accuracy.
Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  (18)F-FDG PET/CT; Diagnostic accuracy; Non-small cell lung cancer; Texture analysis

Mesh:

Substances:

Year:  2013        PMID: 24418285     DOI: 10.1016/j.ejrad.2013.12.020

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  33 in total

1.  Value of Intratumoral Metabolic Heterogeneity and Quantitative 18F-FDG PET/CT Parameters to Predict Prognosis in Patients With HPV-Positive Primary Oropharyngeal Squamous Cell Carcinoma.

Authors:  Esther Mena; Mehdi Taghipour; Sara Sheikhbahaei; Abhinav K Jha; Arman Rahmim; Lilja Solnes; Rathan M Subramaniam
Journal:  Clin Nucl Med       Date:  2017-05       Impact factor: 7.794

2.  Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions.

Authors:  Margarita Kirienko; Luca Cozzi; Alexia Rossi; Emanuele Voulaz; Lidija Antunovic; Antonella Fogliata; Arturo Chiti; Martina Sollini
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-04-06       Impact factor: 9.236

3.  Integrating manual diagnosis into radiomics for reducing the false positive rate of 18F-FDG PET/CT diagnosis in patients with suspected lung cancer.

Authors:  Fei Kang; Wei Mu; Jie Gong; Shengjun Wang; Guoquan Li; Guiyu Li; Wei Qin; Jie Tian; Jing Wang
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-07-18       Impact factor: 9.236

Review 4.  The Complexity and Fractal Geometry of Nuclear Medicine Images.

Authors:  Fabio Grizzi; Angelo Castello; Dorina Qehajaj; Carlo Russo; Egesta Lopci
Journal:  Mol Imaging Biol       Date:  2019-06       Impact factor: 3.488

Review 5.  Radiomics in Oncological PET/CT: Clinical Applications.

Authors:  Jeong Won Lee; Sang Mi Lee
Journal:  Nucl Med Mol Imaging       Date:  2017-10-20

6.  Multiple metabolic parameters and visual assessment of 18F-FDG uptake heterogeneity of PET/CT in advanced gastric cancer and primary gastric lymphoma.

Authors:  Yixuan Ren; Juan Liu; Ling Wang; Yongjun Luo; Xiaofang Ding; Aiqi Shi; Jiangyan Liu
Journal:  Abdom Radiol (NY)       Date:  2020-11

7.  Prognostic Impact of Intratumoral Heterogeneity Based on Fractal Geometry Analysis in Operated NSCLC Patients.

Authors:  Angelo Castello; Carlo Russo; Fabio Grizzi; Dorina Qehajaj; Egesta Lopci
Journal:  Mol Imaging Biol       Date:  2019-10       Impact factor: 3.488

8.  18F-PSMA-1007 multiparametric, dynamic PET/CT in biochemical relapse and progression of prostate cancer.

Authors:  Christos Sachpekidis; A Afshar-Oromieh; K Kopka; D S Strauss; L Pan; U Haberkorn; A Dimitrakopoulou-Strauss
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-11-14       Impact factor: 9.236

Review 9.  Clinical applications of textural analysis in non-small cell lung cancer.

Authors:  Iain Phillips; Mazhar Ajaz; Veni Ezhil; Vineet Prakash; Sheaka Alobaidli; Sarah J McQuaid; Christopher South; James Scuffham; Andrew Nisbet; Philip Evans
Journal:  Br J Radiol       Date:  2017-10-27       Impact factor: 3.039

10.  Asphericity of pretherapeutic tumour FDG uptake provides independent prognostic value in head-and-neck cancer.

Authors:  Ivayla Apostolova; Ingo G Steffen; Florian Wedel; Alexandr Lougovski; Simone Marnitz; Thorsten Derlin; Holger Amthauer; Ralph Buchert; Frank Hofheinz; Winfried Brenner
Journal:  Eur Radiol       Date:  2014-06-26       Impact factor: 5.315

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