publication . Article . Other literature type . 2019

Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features

Stephan Gruber; Günther Grabner; Zsuzsanna Bago-Horvath; Hubert Bickel; Peter Dubsky; Katja Pinker; Wolf-Dieter Vogl; Georg Langs; Thomas H. Helbich; Wolfgang Bogner;
Open Access English
  • Published: 01 Apr 2019 Journal: European Radiology Experimental, volume 3 (eissn: 2509-9280, Copyright policy)
  • Publisher: Springer International Publishing
Abstract
Background Multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) shows clinical potential for detection and classification of breast lesions. Yet, the contribution of features for computer-aided segmentation and diagnosis (CAD) need to be better understood. We proposed a data-driven machine learning approach for a CAD system combining dynamic contrast-enhanced (DCE)-MRI, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose (18F-FDG)-PET. Methods The CAD incorporated a random forest (RF) classifier combined with mpPET/MRI intensity-based features for lesion segmentation and shape features, kinetic and spatio-temporal texture ...
Subjects
free text keywords: Original Article, Diagnosis (computer-assisted), Breast neoplasms, Magnetic resonance imaging, Machine learning, Positron-emission tomography, Medical physics. Medical radiology. Nuclear medicine, R895-920, Pattern recognition, Artificial intelligence, business.industry, business, Positron emission tomography, medicine.diagnostic_test, medicine, Segmentation, Receiver operating characteristic, Neuroradiology, Random forest, Feature selection, CAD, Computer science
Funded by
FWF| Whole body image analysis for diagnosing patients with monoclonal plasma cell disorders
Project
  • Funder: Austrian Science Fund (FWF) (FWF)
  • Project Code: I 2714
  • Funding stream: Internationale Projekte
,
NIH| MOUSE GENETICS
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 2P30CA008748-43
  • Funding stream: NATIONAL CANCER INSTITUTE
,
EC| HYPMED
Project
HYPMED
Digital Hybrid Breast PET/MRI for Enhanced Diagnosis of Breast Cancer
  • Funder: European Commission (EC)
  • Project Code: 667211
  • Funding stream: H2020 | RIA
45 references, page 1 of 3

Ferlay, J, Shin, HR, Bray, F, Forman, D, Mathers, C, Parkin, DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010; 127: 2893-2917 [PubMed]

Baum, M. The curability of breast cancer. BMJ. 1976; 1: 439-442 [OpenAIRE] [PubMed]

Pinker, K, Bogner, W, Baltzer, P. Improved differentiation of benign and malignant breast tumors with multiparametric 18fluorodeoxyglucose positron emission tomography magnetic resonance imaging: a feasibility study. Clin Cancer Res. 2014; 20: 3540-3549 [PubMed]

Ayer, T, Ayvaci, MU, Liu, ZX, Alagoz, O, Burnside, ES. Computer-aided diagnostic models in breast cancer screening. Imaging Med. 2010; 2: 313-323 [OpenAIRE] [PubMed]

5.Woods BJ (2008) Computer-aided detection of malignant lesions in dynamic contrast enhanced MRI breast and prostate cancer datasets. Dissertation, Ohio State University Available via http://rave.ohiolink.edu/etdc/view?acc_num=osu1218155270

Doi, K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph. 2007; 31: 198-211 [OpenAIRE] [PubMed]

7.Vyborny CJ, Giger ML, Nishikawa RM (2000) Computer-aided detection and diagnosis of breast cancer. Radiol Clin North Am 38:725–740

Morris, E, Comstock, C, Lee, C, Lehman, C, Ikeda, D, Newstead, G. ACR BI-RADS® magnetic resonance imaging. ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System Reston. 2013

9.Stoutjesdijk MJ, Fütterer JJ, Boetes C, van Die LE, Jager G, Barentsz JO (2005) Variability in the description of morphologic and contrast enhancement characteristics of breast lesions on magnetic resonance imaging. Investig Radiol 40:355–362

Breiman, L. Random forests. Mach Learn. 2001; 45: 5-32

11.Pinker K, Grabner G, Bogner W et al (2009) A combined high temporal and high spatial resolution 3 Tesla MR imaging protocol for the assessment of breast lesions: initial results. Invest Radiol 44:553–558

Bogner, W, Pinker-Domenig, K, Bickel, H. Readout-segmented echo-planar imaging improves the diagnostic performance of diffusion-weighted MR breast examinations at 3.0 T. Radiology. 2012; 263: 64-76 [PubMed]

13.Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54:2033–2044

Somer, EJ, Benatar, NA, O'Doherty, MJ, Smith, MA, Marsden, PK. Use of the CT component of PET-CT to improve PET-MR registration: demonstration in soft-tissue sarcoma. Phys Med Biol. 2007; 52: 6991-7006 [PubMed]

Adams, R, Bischof, L. Seeded region growing. IEEE Trans Pattern Anal Mach Intell. 1994; 16: 641-647

45 references, page 1 of 3
Abstract
Background Multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) shows clinical potential for detection and classification of breast lesions. Yet, the contribution of features for computer-aided segmentation and diagnosis (CAD) need to be better understood. We proposed a data-driven machine learning approach for a CAD system combining dynamic contrast-enhanced (DCE)-MRI, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose (18F-FDG)-PET. Methods The CAD incorporated a random forest (RF) classifier combined with mpPET/MRI intensity-based features for lesion segmentation and shape features, kinetic and spatio-temporal texture ...
Subjects
free text keywords: Original Article, Diagnosis (computer-assisted), Breast neoplasms, Magnetic resonance imaging, Machine learning, Positron-emission tomography, Medical physics. Medical radiology. Nuclear medicine, R895-920, Pattern recognition, Artificial intelligence, business.industry, business, Positron emission tomography, medicine.diagnostic_test, medicine, Segmentation, Receiver operating characteristic, Neuroradiology, Random forest, Feature selection, CAD, Computer science
Funded by
FWF| Whole body image analysis for diagnosing patients with monoclonal plasma cell disorders
Project
  • Funder: Austrian Science Fund (FWF) (FWF)
  • Project Code: I 2714
  • Funding stream: Internationale Projekte
,
NIH| MOUSE GENETICS
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 2P30CA008748-43
  • Funding stream: NATIONAL CANCER INSTITUTE
,
EC| HYPMED
Project
HYPMED
Digital Hybrid Breast PET/MRI for Enhanced Diagnosis of Breast Cancer
  • Funder: European Commission (EC)
  • Project Code: 667211
  • Funding stream: H2020 | RIA
45 references, page 1 of 3

Ferlay, J, Shin, HR, Bray, F, Forman, D, Mathers, C, Parkin, DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010; 127: 2893-2917 [PubMed]

Baum, M. The curability of breast cancer. BMJ. 1976; 1: 439-442 [OpenAIRE] [PubMed]

Pinker, K, Bogner, W, Baltzer, P. Improved differentiation of benign and malignant breast tumors with multiparametric 18fluorodeoxyglucose positron emission tomography magnetic resonance imaging: a feasibility study. Clin Cancer Res. 2014; 20: 3540-3549 [PubMed]

Ayer, T, Ayvaci, MU, Liu, ZX, Alagoz, O, Burnside, ES. Computer-aided diagnostic models in breast cancer screening. Imaging Med. 2010; 2: 313-323 [OpenAIRE] [PubMed]

5.Woods BJ (2008) Computer-aided detection of malignant lesions in dynamic contrast enhanced MRI breast and prostate cancer datasets. Dissertation, Ohio State University Available via http://rave.ohiolink.edu/etdc/view?acc_num=osu1218155270

Doi, K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph. 2007; 31: 198-211 [OpenAIRE] [PubMed]

7.Vyborny CJ, Giger ML, Nishikawa RM (2000) Computer-aided detection and diagnosis of breast cancer. Radiol Clin North Am 38:725–740

Morris, E, Comstock, C, Lee, C, Lehman, C, Ikeda, D, Newstead, G. ACR BI-RADS® magnetic resonance imaging. ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System Reston. 2013

9.Stoutjesdijk MJ, Fütterer JJ, Boetes C, van Die LE, Jager G, Barentsz JO (2005) Variability in the description of morphologic and contrast enhancement characteristics of breast lesions on magnetic resonance imaging. Investig Radiol 40:355–362

Breiman, L. Random forests. Mach Learn. 2001; 45: 5-32

11.Pinker K, Grabner G, Bogner W et al (2009) A combined high temporal and high spatial resolution 3 Tesla MR imaging protocol for the assessment of breast lesions: initial results. Invest Radiol 44:553–558

Bogner, W, Pinker-Domenig, K, Bickel, H. Readout-segmented echo-planar imaging improves the diagnostic performance of diffusion-weighted MR breast examinations at 3.0 T. Radiology. 2012; 263: 64-76 [PubMed]

13.Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54:2033–2044

Somer, EJ, Benatar, NA, O'Doherty, MJ, Smith, MA, Marsden, PK. Use of the CT component of PET-CT to improve PET-MR registration: demonstration in soft-tissue sarcoma. Phys Med Biol. 2007; 52: 6991-7006 [PubMed]

Adams, R, Bischof, L. Seeded region growing. IEEE Trans Pattern Anal Mach Intell. 1994; 16: 641-647

45 references, page 1 of 3
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