Multiparametric integrated (18)F-FDG PET/MRI-based radiomics for breast cancer phenotyping and tumor decoding Journal Article

   


Authors: Umutlu, L.; Kirchner, J.; Bruckmann, N. M.; Morawitz, J.; Antoch, G.; Ingenwerth, M.; Bittner, A. K.; Hoffmann, O.; Haubold, J.; Grueneisen, J.; Quick, H. H.; Rischpler, C.; Herrmann, K.; Gibbs, P.; Pinker-Domenig, K.
Article Title: Multiparametric integrated (18)F-FDG PET/MRI-based radiomics for breast cancer phenotyping and tumor decoding
Abstract: Background: This study investigated the performance of simultaneous18F-FDG PET/MRI of the breast as a platform for comprehensive radiomics analysis for breast cancer subtype analysis, hormone receptor status, proliferation rate and lymphonodular and distant metastatic spread. Meth-ods: One hundred and twenty-four patients underwent simultaneous18F-FDG PET/MRI. Breast tumors were segmented and radiomic features were extracted utilizing CERR software following the IBSI guidelines. LASSO regression was employed to select the most important radiomics features prior to model development. Five-fold cross validation was then utilized alongside support vector machines, resulting in predictive models for various combinations of imaging data series. Results: The highest AUC and accuracy for differentiation between luminal A and B was achieved by all MR sequences (AUC 0.98; accuracy 97.3). The best results in AUC for prediction of hormone receptor status and proliferation rate were found based on all MR and PET data (ER AUC 0.87, PR AUC 0.88, Ki-67 AUC 0.997). PET provided the best determination of grading (AUC 0.71), while all MR and PET analyses yielded the best results for lymphonodular and distant metastatic spread (0.81 and 0.99, respectively). Conclusion:18F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for breast cancer phenotyping and tumor decoding, utilizing the perks of simultaneously acquired morphologic, functional and metabolic data. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords: major clinical study; histopathology; area under the curve; nuclear magnetic resonance imaging; positron emission tomography; lymph node metastasis; cancer grading; diagnostic accuracy; sensitivity and specificity; ki 67 antigen; cell proliferation; phenotype; breast cancer; image analysis; tumor volume; differential diagnosis; epidermal growth factor receptor 2; practice guideline; retrospective study; prediction; distant metastasis; image quality; fluorodeoxyglucose f 18; estrogen receptor; progesterone receptor; menopause; receiver operating characteristic; diagnostic test accuracy study; support vector machine; gadoterate meglumine; luminal a breast cancer; human; female; article; luminal b breast cancer; radiomics; positron emission tomography magnetic resonance imaging; cross validation; multiparametric18f-fdg pet/mri; radiomics-based pheno-typing and tumor decoding
Journal Title: Cancers
Volume: 13
Issue: 12
ISSN: 2072-6694
Publisher: MDPI  
Date Published: 2021-06-01
Start Page: 2928
Language: English
DOI: 10.3390/cancers13122928
PROVIDER: scopus
PMCID: PMC8230865
PUBMED: 34208197
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107576510&doi=10.3390%2fcancers13122928&partnerID=40&md5=c46d094ecf271ddd4edacb42fe3d98b3
Notes: Article -- Export Date: 1 July 2021 -- Source: Scopus
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MSK Authors
  1. Katja Pinker-Domenig
    214 Pinker-Domenig
  2. Peter Gibbs
    33 Gibbs
  3. Julian Philipp Kirchner
    7 Kirchner
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