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Katal S, McKay MJ, Taubman K. PET Molecular Imaging in Breast Cancer: Current Applications and Future Perspectives. J Clin Med 2024; 13:3459. [PMID: 38929989 PMCID: PMC11205053 DOI: 10.3390/jcm13123459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
Positron emission tomography (PET) plays a crucial role in breast cancer management. This review addresses the role of PET imaging in breast cancer care. We focus primarily on the utility of 18F-fluorodeoxyglucose (FDG) PET in staging, recurrence detection, and treatment response evaluation. Furthermore, we delve into the growing interest in precision therapy and the development of novel radiopharmaceuticals targeting tumor biology. This includes discussing the potential of PET/MRI and artificial intelligence in breast cancer imaging, offering insights into improved diagnostic accuracy and personalized treatment approaches.
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Affiliation(s)
- Sanaz Katal
- Medical Imaging Department, St. Vincent’s Hospital Melbourne, Fitzroy, VIC 3065, Australia;
| | - Michael J. McKay
- Northwest Regional Hospital, University of Tasmania, Burnie, TAS 7320, Australia;
- Northern Cancer Service, Northwest Regional Hospital, Burnie, TAS 7320, Australia
| | - Kim Taubman
- Medical Imaging Department, St. Vincent’s Hospital Melbourne, Fitzroy, VIC 3065, Australia;
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Ulaner GA, Vaz SC, Groheux D. Quarter-Century Transformation of Oncology: Positron Emission Tomography for Patients with Breast Cancer. PET Clin 2024; 19:147-162. [PMID: 38177052 DOI: 10.1016/j.cpet.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
PET radiotracers have become indispensable in the care of patients with breast cancer. 18F-fluorodeoxyglucose has become the preferred method of many oncologists for systemic staging of breast cancer at initial diagnosis, detecting recurrent disease, and for measuring treatment response after therapy. 18F-Sodium Fluoride is valuable for detection of osseous metastases. 18F-fluoroestradiol is now FDA-approved with multiple appropriate clinical uses. There are multiple PET radiotracers in clinical trials, which may add utility of PET imaging for patients with breast cancer in the future. This article will describe the advances during the last quarter century in PET for patients with breast cancer.
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Affiliation(s)
- Gary A Ulaner
- Molecular Imaging and Therapy, Hoag Family Cancer Institute, Irvine, CA, USA; Departments of Radiology and Translational Genomics, University of Southern California, Los Angeles, CA, USA.
| | - Sofia Carrilho Vaz
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Center, Champalimaud Foundation, Lisbon, Portugal; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - David Groheux
- Nuclear Department of Nuclear Medicine, Saint-Louis Hospital, Paris, France; Centre d'Imagerie Radio-Isotopique (CIRI), La Rochelle, France; University Paris-Diderot, INSERM U976, HIPI, Paris, France
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Bartsch SJ, Brožová K, Ehret V, Friske J, Fürböck C, Kenner L, Laimer-Gruber D, Helbich TH, Pinker K. Non-Contrast-Enhanced Multiparametric MRI of the Hypoxic Tumor Microenvironment Allows Molecular Subtyping of Breast Cancer: A Pilot Study. Cancers (Basel) 2024; 16:375. [PMID: 38254864 PMCID: PMC10813988 DOI: 10.3390/cancers16020375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/05/2024] [Accepted: 01/12/2024] [Indexed: 01/24/2024] Open
Abstract
Tumor neoangiogenesis is an important hallmark of cancer progression, triggered by alternating selective pressures from the hypoxic tumor microenvironment. Non-invasive, non-contrast-enhanced multiparametric MRI combining blood-oxygen-level-dependent (BOLD) MRI, which depicts blood oxygen saturation, and intravoxel-incoherent-motion (IVIM) MRI, which captures intravascular and extravascular diffusion, can provide insights into tumor oxygenation and neovascularization simultaneously. Our objective was to identify imaging markers that can predict hypoxia-induced angiogenesis and to validate our findings using multiplexed immunohistochemical analyses. We present an in vivo study involving 36 female athymic nude mice inoculated with luminal A, Her2+, and triple-negative breast cancer cells. We used a high-field 9.4-tesla MRI system for imaging and subsequently analyzed the tumors using multiplex immunohistochemistry for CD-31, PDGFR-β, and Hif1-α. We found that the hyperoxic-BOLD-MRI-derived parameter ΔR2* discriminated luminal A from Her2+ and triple-negative breast cancers, while the IVIM-derived parameter fIVIM discriminated luminal A and Her2+ from triple-negative breast cancers. A comprehensive analysis using principal-component analysis of both multiparametric MRI- and mpIHC-derived data highlighted the differences between triple-negative and luminal A breast cancers. We conclude that multiparametric MRI combining hyperoxic BOLD MRI and IVIM MRI, without the need for contrast agents, offers promising non-invasive markers for evaluating hypoxia-induced angiogenesis.
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Affiliation(s)
- Silvester J. Bartsch
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Structural and Molecular Preclinical Imaging, Medical University of Vienna, 1090 Vienna, Austria
| | - Klára Brožová
- Department of Experimental and Laboratory Animal Pathology, Clinical Institute of Pathology, Medical University of Vienna, 1090 Vienna, Austria
- Unit of Laboratory Animal Pathology, University of Veterinary Medicine Vienna, 1210 Vienna, Austria
| | - Viktoria Ehret
- Department of Internal Medicine III, Division of Endocrinology and Metabolism, Medical University of Vienna, 1090 Vienna, Austria
| | - Joachim Friske
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Structural and Molecular Preclinical Imaging, Medical University of Vienna, 1090 Vienna, Austria
| | - Christoph Fürböck
- Computational Imaging Research Laboratory, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Lukas Kenner
- Department of Experimental and Laboratory Animal Pathology, Clinical Institute of Pathology, Medical University of Vienna, 1090 Vienna, Austria
- Unit of Laboratory Animal Pathology, University of Veterinary Medicine Vienna, 1210 Vienna, Austria
- Comprehensive Cancer Center, Medical University Vienna, 1090 Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Medical University Vienna, 1090 Vienna, Austria
- Center for Biomarker Research in Medicine (CBmed), 8010 Graz, Austria
| | - Daniela Laimer-Gruber
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Structural and Molecular Preclinical Imaging, Medical University of Vienna, 1090 Vienna, Austria
| | - Thomas H. Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Structural and Molecular Preclinical Imaging, Medical University of Vienna, 1090 Vienna, Austria
| | - Katja Pinker
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Cecil K, Huppert L, Mukhtar R, Dibble EH, O'Brien SR, Ulaner GA, Lawhn-Heath C. Metabolic Positron Emission Tomography in Breast Cancer. PET Clin 2023; 18:473-485. [PMID: 37369614 DOI: 10.1016/j.cpet.2023.04.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
Metabolic PET, most commonly 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT), has had a major impact on the imaging of breast cancer and can have important clinical applications in appropriate patients. While limited for screening, FDG PET/CT outperforms conventional imaging in locally advanced breast cancer. FDG PET/CT is more sensitive than conventional imaging in assessing treatment response, accurately predicting complete response or nonresponse in early-stage cases. It also aids in determining disease extent and treatment response in the metastatic setting. Further research, including randomized controlled trials with FDG and other metabolic agents such as fluciclovine, is needed for optimal breast cancer imaging.
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Affiliation(s)
- Katherine Cecil
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Laura Huppert
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Rita Mukhtar
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA; Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Elizabeth H Dibble
- Department of Diagnostic Imaging, The Warren Alpert Medical School of Brown University/Rhode Island Hospital, Providence, RI, USA
| | - Sophia R O'Brien
- Divisions of Molecular Imaging and Therapy Breast Imaging, Department of Radiology, The Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Gary A Ulaner
- Molecular Imaging and Therapy, Hoag Family Cancer Institute, Irvine, CA, USA; Departments of Radiology and Translational Genomics, University of Southern California, Los Angeles, CA, USA
| | - Courtney Lawhn-Heath
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA.
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Jannusch K, Lindemann ME, Bruckmann NM, Morawitz J, Dietzel F, Pomykala KL, Herrmann K, Bittner AK, Hoffmann O, Mohrmann S, Umutlu L, Antoch G, Quick HH, Kirchner J. Towards a fast PET/MRI protocol for breast cancer imaging: maintaining diagnostic confidence while reducing PET and MRI acquisition times. Eur Radiol 2023; 33:6179-6188. [PMID: 37045980 PMCID: PMC10415438 DOI: 10.1007/s00330-023-09580-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/06/2023] [Accepted: 02/14/2023] [Indexed: 04/14/2023]
Abstract
OBJECTIVES To investigate the diagnostic feasibility of a shortened breast PET/MRI protocol in breast cancer patients. METHODS Altogether 90 women with newly diagnosed T1tumor-staged (T1ts) and T2tumor-staged (T2ts) breast cancer were included in this retrospective study. All underwent a dedicated comprehensive breast [18F]FDG-PET/MRI. List-mode PET data were retrospectively reconstructed with 20, 15, 10, and 5 min for each patient to simulate the effect of reduced PET acquisition times. The SUVmax/mean of all malign breast lesions was measured. Furthermore, breast PET data reconstructions were analyzed regarding image quality, lesion detectability, signal-to-noise ratio (SNR), and image noise (IN). The simultaneously acquired comprehensive MRI protocol was then shortened by retrospectively removing sequences from the protocol. Differences in malignant breast lesion detectability between the original and the fast breast MRI protocol were evaluated lesion-based. The 20-min PET reconstructions and the original MRI protocol served as reference. RESULTS In all PET reconstructions, 127 congruent breast lesions could be detected. Group comparison and T1ts vs. T2ts subgroup comparison revealed no significant difference of subjective image quality between 20, 15, 10, and 5 min acquisition times. SNR of qualitative image evaluation revealed no significant difference between different PET acquisition times. A slight but significant increase of IN with decreasing PET acquisition times could be detected. Lesion SUVmax group comparison between all PET acquisition times revealed no significant differences. Lesion-based evaluation revealed no significant difference in breast lesion detectability between original and fast breast MRI protocols. CONCLUSIONS Breast [18F]FDG-PET/MRI protocols can be shortened from 20 to below 10 min without losing essential diagnostic information. KEY POINTS • A highly accurate breast cancer evaluation is possible by the shortened breast [18F]FDG-PET/MRI examination protocol. • Significant time saving at breast [18F]FDG-PET/MRI protocol could increase patient satisfaction and patient throughput for breast cancer patients at PET/MRI.
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Affiliation(s)
- Kai Jannusch
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Moorenstrasse 5, D-40225, Dusseldorf, Germany.
| | - Maike E Lindemann
- High-Field and Hybrid MR Imaging, University Hospital Essen, University Duisburg-Essen, D-45147, Essen, Germany
| | - Nils Martin Bruckmann
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Moorenstrasse 5, D-40225, Dusseldorf, Germany
| | - Janna Morawitz
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Moorenstrasse 5, D-40225, Dusseldorf, Germany
| | - Frederic Dietzel
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Moorenstrasse 5, D-40225, Dusseldorf, Germany
| | - Kelsey L Pomykala
- Department for Artificial Intelligence in Medicine, University Hospital Essen, University of Duisburg-Essen, D-45131, Essen, Germany
| | - Ken Herrmann
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, D-45147, Essen, Germany
| | - Ann-Kathrin Bittner
- Department Gynecology and Obstetrics, University Hospital Essen, University of Duisburg-Essen, D-45147, Essen, Germany
| | - Oliver Hoffmann
- Department Gynecology and Obstetrics, University Hospital Essen, University of Duisburg-Essen, D-45147, Essen, Germany
| | - Svjetlana Mohrmann
- Department of Gynecology, Medical Faculty, University Dusseldorf, D-40225, Dusseldorf, Germany
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, D-45147, Essen, Germany
| | - Gerald Antoch
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Moorenstrasse 5, D-40225, Dusseldorf, Germany
| | - Harald H Quick
- High-Field and Hybrid MR Imaging, University Hospital Essen, University Duisburg-Essen, D-45147, Essen, Germany
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, D-45141, Essen, Germany
| | - Julian Kirchner
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Moorenstrasse 5, D-40225, Dusseldorf, Germany
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Clauser P, Rasul S, Kapetas P, Fueger BJ, Milos RI, Balber T, Berroterán-Infante N, Hacker M, Helbich TH, Baltzer PAT. Prospective validation of 18F-Fluoroethylcholine as a tracer in PET/MRI for the evaluation of breast lesions and prediction of lymph node status. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01633-6. [PMID: 37221356 DOI: 10.1007/s11547-023-01633-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 04/19/2023] [Indexed: 05/25/2023]
Abstract
PURPOSE To assess 18F-Fluoroethylcholine (18F-FEC) as a PET/MRI tracer in the evaluation of breast lesions, breast cancer aggressiveness, and prediction of lymph node status. MATERIALS AND METHODS This prospective, monocentric study was approved by the ethics committee and patients gave written, informed consent. This clinical trial was registered in the EudraCT database (Number 2017-003089-29). Women who presented with suspicious breast lesions were included. Histopathology was used as reference standard. Simultaneous 18F-FEC PET/MRI of the breast was performed in a prone position with a dedicated breast coil. MRI was performed using a standard protocol before and after contrast agent administration. A simultaneous read by nuclear medicine physicians and radiologists collected the imaging data of MRI-detected lesions, including the maximum standardized 18F-FEC-uptake value of breast lesions (SUVmaxT) and axillary lymph nodes (SUVmaxLN). Differences in SUVmax were evaluated with the Mann-Whitney U test. To calculate diagnostic performance, the area under the receiver operating characteristics curve (ROC) was used. RESULTS There were 101 patients (mean age 52.3 years, standard deviation 12.0) with 117 breast lesions included (30 benign, 7 ductal carcinomas in situ, 80 invasive carcinomas). 18F-FEC was well tolerated by all patients. The ROC to distinguish benign from malignant breast lesions was 0.846. SUVmaxT was higher if lesions were malignant (p < 0.001), had a higher proliferation rate (p = 0.011), and were HER2-positive (p = 0.041). SUVmaxLN was higher in metastatic lymph nodes, with an ROC of 0.761 for SUVmaxT and of 0.793 for SUVmaxLN. CONCLUSION: Simultaneous 18F-FEC PET/MRI is safe and has the potential to be used for the evaluation of breast cancer aggressiveness, and prediction of lymph node status.
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Affiliation(s)
- Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Sazan Rasul
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Barbara J Fueger
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Ruxandra-Iulia Milos
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Theresa Balber
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Neydher Berroterán-Infante
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Thomas Hans Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Vienna, Austria
| | - Pascal Andreas Thomas Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
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Pommranz CM, Schmidt FP, Mannheim JG, Diebold SJ, Tenzer C, Santangelo A, Pichler BJ. Design and performance simulation studies of a breast PET insert integrable into a clinical whole-body PET/MRI scanner. Phys Med Biol 2023; 68. [PMID: 36753773 DOI: 10.1088/1361-6560/acba77] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 02/08/2023] [Indexed: 02/10/2023]
Abstract
Objective. Three different breast positron emission tomography (PET) insert geometries are proposed for integration into an existing magnetic resonance imaging (MRI) breast coil (Breast Biopsy Coil, NORAS MRI products) to be used inside a whole-body PET/MRI scanner (Biograph mMR, Siemens Healthineers) to enhance the sensitivity and spatial resolution of imaging inside the breast.Approach. Monte Carlo simulations were performed to predict and compare the performance characteristics of the three geometries in terms of the sensitivity, spatial resolution, scatter fraction, and noise equivalent count rate (NECR). In addition, the background single count rate due to organ uptake in a clinical scan scenario was predicted using a realistic anthropomorphic phantom.Main results. In the center of the field of view (cFOV), absolute sensitivities of 3.1%, 2.7%, and 2.2% were found for Geometry A (detectors arranged in two cylinders), Geometry B (detectors arranged in two partial cylinders), and Geometry C (detectors arranged in two half cylinders combined with two plates), respectively. The full width at half maximum spatial resolution was determined to be 1.7 mm (Geometry A), 1.8 mm (Geometry B) and 2.0 mm (Geometry C) at 5 mm from the cFOV. Designs with multiple scintillation-crystal layers capable of determining the depth of interaction (DOI) strongly improved the spatial resolution at larger distances from the transaxial cFOV. The system scatter fractions were 33.1% (Geometries A and B) and 32.3% (Geometry C). The peak NECRs occurred at source activities of 300 MBq (Geometry A), 310 MBq (Geometry B) and 340 MBq (Geometry C). The background single-event count rates were 17.1 × 106cps (Geometry A), 15.3 × 106cps (Geometry B) and 14.8 × 106cps (Geometry C). Geometry A in the three-layer DOI variant exhibited the best PET performance characteristics but could be challenging to manufacture. Geometry C had the lowest impact on the spatial resolution and the lowest sensitivity among the investigated geometries.Significance. Geometry B in the two-layer DOI variant represented an effective compromise between the PET performance and manufacturing difficulty and was found to be a promising candidate for the future breast PET insert.
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Affiliation(s)
- C M Pommranz
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, D-72076 Tuebingen, Germany.,Institute for Astronomy and Astrophysics, Eberhard Karls University Tuebingen, Sand 1, D-72076 Tuebingen, Germany
| | - F P Schmidt
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, D-72076 Tuebingen, Germany.,Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, Otfried-Mueller-Strasse 14, D-72076 Tuebingen, Germany
| | - J G Mannheim
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, D-72076 Tuebingen, Germany.,Cluster of Excellence iFIT (EXC 2180) Image Guided and Functionally Instructed Tumor Therapies, University of Tuebingen, Tuebingen, Germany
| | - S J Diebold
- Institute for Astronomy and Astrophysics, Eberhard Karls University Tuebingen, Sand 1, D-72076 Tuebingen, Germany
| | - C Tenzer
- Institute for Astronomy and Astrophysics, Eberhard Karls University Tuebingen, Sand 1, D-72076 Tuebingen, Germany
| | - A Santangelo
- Institute for Astronomy and Astrophysics, Eberhard Karls University Tuebingen, Sand 1, D-72076 Tuebingen, Germany
| | - B J Pichler
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, D-72076 Tuebingen, Germany.,Cluster of Excellence iFIT (EXC 2180) Image Guided and Functionally Instructed Tumor Therapies, University of Tuebingen, Tuebingen, Germany
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Romeo V, Helbich TH, Pinker K. Breast PET/MRI Hybrid Imaging and Targeted Tracers. J Magn Reson Imaging 2023; 57:370-386. [PMID: 36165348 PMCID: PMC10074861 DOI: 10.1002/jmri.28431] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 01/20/2023] Open
Abstract
The recent introduction of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) as a promising imaging modality for breast cancer assessment has prompted fervent research activity on its clinical applications. The current knowledge regarding the possible clinical applications of hybrid PET/MRI is constantly evolving, thanks to the development and clinical availability of hybrid scanners, the development of new PET tracers and the rise of artificial intelligence (AI) techniques. In this state-of-the-art review on the use of hybrid breast PET/MRI, the most promising advanced MRI techniques (diffusion-weighted imaging, dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, and chemical exchange saturation transfer) are discussed. Current and experimental PET tracers (18 F-FDG, 18 F-NaF, choline, 18 F-FES, 18 F-FES, 89 Zr-trastuzumab, choline derivatives, 18 F-FLT, and 68 Ga-FAPI-46) are described in order to provide an overview on their molecular mechanisms of action and corresponding clinical applications. New perspectives represented by the use of radiomics and AI techniques are discussed. Furthermore, the current strengths and limitations of hybrid PET/MRI in the real world are highlighted. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Thomas H Helbich
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Wien, Austria
| | - Katja Pinker
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Wien, Austria.,Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Ruan D, Sun L. Diagnostic Performance of PET/MRI in Breast Cancer: A Systematic Review and Bayesian Bivariate Meta-analysis. Clin Breast Cancer 2023; 23:108-124. [PMID: 36549970 DOI: 10.1016/j.clbc.2022.11.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 11/07/2022] [Accepted: 11/26/2022] [Indexed: 12/04/2022]
Abstract
INTRODUCTION By performing a systematic review and meta-analysis, the diagnostic value of 18F-FDG PET/MRI in breast lesions, lymph nodes, and distant metastases was assessed, and the merits and demerits of PET/MRI in the application of breast cancer were comprehensively reviewed. METHODS Breast cancer-related studies using 18F-FDG PET/MRI as a diagnostic tool published before September 12, 2022 were included. The pooled sensitivity, specificity, log diagnostic odds ratio (LDOR), and area under the curve (AUC) were calculated using Bayesian bivariate meta-analysis in a lesion-based and patient-based manner. RESULTS We ultimately included 24 studies (including 1723 patients). Whether on a lesion-based or patient-based analysis, PET/MRI showed superior overall pooled sensitivity (0.95 [95% CI: 0.92-0.98] & 0.93 [95% CI: 0.88-0.98]), specificity (0.94 [95% CI: 0.90-0.97] & 0.94 [95% CI: 0.92-0.97]), LDOR (5.79 [95% CI: 4.95-6.86] & 5.64 [95% CI: 4.58-7.03]) and AUC (0.98 [95% CI: 0.94-0.99] & 0.98[95% CI: 0.92-0.99]) for diagnostic applications in breast cancer. In the specific subgroup analysis, PET/MRI had high pooled sensitivity and specificity for the diagnosis of breast lesions and distant metastatic lesions and was especially excellent for bone lesions. PET/MRI performed poorly for diagnosing axillary lymph nodes but was better than for lymph nodes at other sites (pooled sensitivity, specificity, LDOR, AUC: 0.86 vs. 0.58, 0.90 vs. 0.82, 4.09 vs. 1.98, 0.89 vs. 0.84). CONCLUSION 18F-FDG PET/MRI performed excellently in diagnosing breast lesions and distant metastases. It can be applied to the initial diagnosis of suspicious breast lesions, accurate staging of breast cancer patients, and accurate restaging of patients with suspected recurrence.
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Affiliation(s)
- Dan Ruan
- Department of Nuclear Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
| | - Long Sun
- Department of Nuclear Medicine and Minnan PET Center, Xiamen Cancer Hospital, The First Affiliated Hospital of Xiamen University, Xiamen, China.
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Pötsch N, Korajac A, Stelzer P, Kapetas P, Milos RI, Dietzel M, Helbich TH, Clauser P, Baltzer PAT. Breast MRI: does a clinical decision algorithm outweigh reader experience? Eur Radiol 2022; 32:6557-6564. [PMID: 35852572 PMCID: PMC9474540 DOI: 10.1007/s00330-022-09015-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/30/2022] [Accepted: 07/02/2022] [Indexed: 11/28/2022]
Abstract
Objectives Due to its high sensitivity, DCE MRI of the breast (MRIb) is increasingly used for both screening and assessment purposes. The Kaiser score (KS) is a clinical decision algorithm, which formalizes and guides diagnosis in breast MRI and is expected to compensate for lesser reader experience. The aim was to evaluate the diagnostic performance of untrained residents using the KS compared to off-site radiologists experienced in breast imaging using only MR BI-RADS. Methods Three off-site, board-certified radiologists, experienced in breast imaging, interpreted MRIb according to the MR BI-RADS scale. The same studies were read by three residents in radiology without prior training in breast imaging using the KS. All readers were blinded to clinical information. Histology was used as the gold standard. Statistical analysis was conducted by comparing the AUC of the ROC curves. Results A total of 80 women (median age 52 years) with 93 lesions (32 benign, 61 malignant) were included. The individual within-group performance of the three expert readers (AUC 0.723–0.742) as well as the three residents was equal (AUC 0.842–0.928), p > 0.05, respectively. But, the rating of each resident using the KS significantly outperformed the experts’ ratings using the MR BI-RADS scale (p ≤ 0.05). Conclusion The KS helped residents to achieve better results in reaching correct diagnoses than experienced radiologists empirically assigning MR BI-RADS categories in a clinical “problem solving MRI” setting. These results support that reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience. Key Points • Reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience in a clinical “problem solving MRI” setting. • The Kaiser score, which provides a clinical decision algorithm for structured reporting, helps residents to reach an expert level in breast MRI reporting and to even outperform experienced radiologists using MR BI-RADS without further formal guidance. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-09015-8.
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Affiliation(s)
- Nina Pötsch
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Aida Korajac
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Philipp Stelzer
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Panagiotis Kapetas
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Ruxandra-Iulia Milos
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Matthias Dietzel
- Institute of Radiology, Erlangen University Hospital, Maximiliansplatz 2, 91054, Erlangen, Germany
| | - Thomas H Helbich
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Paola Clauser
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Pascal A T Baltzer
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria.
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Fardanesh R, Thakur SB, Sevilimedu V, Horvat JV, Gullo RL, Reiner JS, Eskreis-Winkler S, Thakur N, Pinker K. Differentiation Between Benign and Metastatic Breast Lymph Nodes Using Apparent Diffusion Coefficients. Front Oncol 2022; 12:795265. [PMID: 35280791 PMCID: PMC8905522 DOI: 10.3389/fonc.2022.795265] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/28/2022] [Indexed: 11/24/2022] Open
Abstract
The aim of this study was to determine the range of apparent diffusion coefficient (ADC) values for benign axillary lymph nodes in contrast to malignant axillary lymph nodes, and to define the optimal ADC thresholds for three different ADC parameters (minimum, maximum, and mean ADC) in differentiating between benign and malignant lymph nodes. This retrospective study included consecutive patients who underwent breast MRI from January 2017–December 2020. Two-year follow-up breast imaging or histopathology served as the reference standard for axillary lymph node status. Area under the receiver operating characteristic curve (AUC) values for minimum, maximum, and mean ADC (min ADC, max ADC, and mean ADC) for benign vs malignant axillary lymph nodes were determined using the Wilcoxon rank sum test, and optimal ADC thresholds were determined using Youden’s Index. The final study sample consisted of 217 patients (100% female, median age of 52 years (range, 22–81), 110 with benign axillary lymph nodes and 107 with malignant axillary lymph nodes. For benign axillary lymph nodes, ADC values (×10−3 mm2/s) ranged from 0.522–2.712 for mean ADC, 0.774–3.382 for max ADC, and 0.071–2.409 for min ADC; for malignant axillary lymph nodes, ADC values (×10−3 mm2/s) ranged from 0.796–1.080 for mean ADC, 1.168–1.592 for max ADC, and 0.351–0.688 for min ADC for malignant axillary lymph nodes. While there was a statistically difference in all ADC parameters (p<0.001) between benign and malignant axillary lymph nodes, boxplots illustrate overlaps in ADC values, with the least overlap occurring with mean ADC, suggesting that this is the most useful ADC parameter for differentiating between benign and malignant axillary lymph nodes. The mean ADC threshold that resulted in the highest diagnostic accuracy for differentiating between benign and malignant lymph nodes was 1.004×10−3 mm2/s, yielding an accuracy of 75%, sensitivity of 71%, specificity of 79%, positive predictive value of 77%, and negative predictive value of 74%. This mean ADC threshold is lower than the European Society of Breast Imaging (EUSOBI) mean ADC threshold of 1.300×10−3 mm2/s, therefore suggesting that the EUSOBI threshold which was recently recommended for breast tumors should not be extrapolated to evaluate the axillary lymph nodes.
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Affiliation(s)
- Reza Fardanesh
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Joao V Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Jeffrey S Reiner
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Nikita Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States.,Touro College of Osteopathic Medicine, Middletown, NY, United States
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
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Bruckmann NM, Morawitz J, Fendler WP, Ruckhäberle E, Bittner AK, Giesel FL, Herrmann K, Antoch G, Umutlu L, Kirchner J. A Role of PET/MR in Breast Cancer? Semin Nucl Med 2022; 52:611-618. [DOI: 10.1053/j.semnuclmed.2022.01.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 01/26/2022] [Indexed: 01/18/2023]
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13
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Fowler AM, Strigel RM. Clinical advances in PET-MRI for breast cancer. Lancet Oncol 2022; 23:e32-e43. [PMID: 34973230 PMCID: PMC9673821 DOI: 10.1016/s1470-2045(21)00577-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/20/2021] [Accepted: 10/01/2021] [Indexed: 01/03/2023]
Abstract
Imaging is paramount for the early detection and clinical staging of breast cancer, as well as to inform management decisions and direct therapy. PET-MRI is a quantitative hybrid imaging technology that combines metabolic and functional PET data with anatomical detail and functional perfusion information from MRI. The clinical applicability of PET-MRI for breast cancer is an active area of research. In this Review, we discuss the rationale and summarise the clinical evidence for the use of PET-MRI in the diagnosis, staging, prognosis, tumour phenotyping, and assessment of treatment response in breast cancer. The continued development and approval of targeted radiopharmaceuticals, together with radiomics and automated analysis tools, will further expand the opportunity for PET-MRI to provide added value for breast cancer imaging and patient care.
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Affiliation(s)
- Amy M Fowler
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; University of Wisconsin Carbone Cancer Center, Madison, WI, USA.
| | - Roberta M Strigel
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; University of Wisconsin Carbone Cancer Center, Madison, WI, USA
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Luengo Morato Y, Ovejero Paredes K, Lozano Chamizo L, Marciello M, Filice M. Recent Advances in Multimodal Molecular Imaging of Cancer Mediated by Hybrid Magnetic Nanoparticles. Polymers (Basel) 2021; 13:2989. [PMID: 34503029 PMCID: PMC8434540 DOI: 10.3390/polym13172989] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/26/2021] [Accepted: 08/27/2021] [Indexed: 02/06/2023] Open
Abstract
Cancer is the second leading cause of death in the world, which is why it is so important to make an early and very precise diagnosis to obtain a good prognosis. Thanks to the combination of several imaging modalities in the form of the multimodal molecular imaging (MI) strategy, a great advance has been made in early diagnosis, in more targeted and personalized therapy, and in the prediction of the results that will be obtained once the anticancer treatment is applied. In this context, magnetic nanoparticles have been positioned as strong candidates for diagnostic agents as they provide very good imaging performance. Furthermore, thanks to their high versatility, when combined with other molecular agents (for example, fluorescent molecules or radioisotopes), they highlight the advantages of several imaging techniques at the same time. These hybrid nanosystems can be also used as multifunctional and/or theranostic systems as they can provide images of the tumor area while they administer drugs and act as therapeutic agents. Therefore, in this review, we selected and identified more than 160 recent articles and reviews and offer a broad overview of the most important concepts that support the synthesis and application of multifunctional magnetic nanoparticles as molecular agents in advanced cancer detection based on the multimodal molecular imaging approach.
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Affiliation(s)
- Yurena Luengo Morato
- Nanobiotechnology for Life Sciences Lab, Department of Chemistry in Pharmaceutical Sciences, Faculty of Pharmacy, Universidad Complutense de Madrid (UCM), Plaza Ramón y Cajal, 28040 Madrid, Spain; (Y.L.M.); (K.O.P.); (L.L.C.)
| | - Karina Ovejero Paredes
- Nanobiotechnology for Life Sciences Lab, Department of Chemistry in Pharmaceutical Sciences, Faculty of Pharmacy, Universidad Complutense de Madrid (UCM), Plaza Ramón y Cajal, 28040 Madrid, Spain; (Y.L.M.); (K.O.P.); (L.L.C.)
- Microscopy and Dynamic Imaging Unit, Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC F.S.P.), Calle Melchor Fernández Almagro 3, 28029 Madrid, Spain
| | - Laura Lozano Chamizo
- Nanobiotechnology for Life Sciences Lab, Department of Chemistry in Pharmaceutical Sciences, Faculty of Pharmacy, Universidad Complutense de Madrid (UCM), Plaza Ramón y Cajal, 28040 Madrid, Spain; (Y.L.M.); (K.O.P.); (L.L.C.)
| | - Marzia Marciello
- Nanobiotechnology for Life Sciences Lab, Department of Chemistry in Pharmaceutical Sciences, Faculty of Pharmacy, Universidad Complutense de Madrid (UCM), Plaza Ramón y Cajal, 28040 Madrid, Spain; (Y.L.M.); (K.O.P.); (L.L.C.)
| | - Marco Filice
- Nanobiotechnology for Life Sciences Lab, Department of Chemistry in Pharmaceutical Sciences, Faculty of Pharmacy, Universidad Complutense de Madrid (UCM), Plaza Ramón y Cajal, 28040 Madrid, Spain; (Y.L.M.); (K.O.P.); (L.L.C.)
- Microscopy and Dynamic Imaging Unit, Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC F.S.P.), Calle Melchor Fernández Almagro 3, 28029 Madrid, Spain
- CIBER de Enfermedades Respiratorias (CIBERES), Melchor Fernández Almagro 3, 28029 Madrid, Spain
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15
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AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis. Eur J Nucl Med Mol Imaging 2021; 49:596-608. [PMID: 34374796 PMCID: PMC8803815 DOI: 10.1007/s00259-021-05492-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 07/06/2021] [Indexed: 12/17/2022]
Abstract
Purpose To assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI can discriminate between benign and malignant breast lesions. Methods A population of 102 patients with 120 breast lesions (101 malignant and 19 benign) detected on ultrasound and/or mammography was prospectively enrolled. All patients underwent hybrid 18F-FDG PET/MRI for diagnostic purposes. Quantitative parameters were extracted from DCE (MTT, VD, PF), DW (mean ADC of breast lesions and contralateral breast parenchyma), PET (SUVmax, SUVmean, and SUVminimum of breast lesions, as well as SUVmean of the contralateral breast parenchyma), and T2-weighted images. Radiomics features were extracted from DCE, T2-weighted, ADC, and PET images. Different diagnostic models were developed using a fine Gaussian support vector machine algorithm which explored different combinations of quantitative parameters and radiomics features to obtain the highest accuracy in discriminating between benign and malignant breast lesions using fivefold cross-validation. The performance of the best radiomics and ML model was compared with that of expert reader review using McNemar’s test. Results Eight radiomics models were developed. The integrated model combining MTT and ADC with radiomics features extracted from PET and ADC images obtained the highest accuracy for breast cancer diagnosis (AUC 0.983), although its accuracy was not significantly higher than that of expert reader review (AUC 0.868) (p = 0.508). Conclusion A radiomics and ML model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI images can accurately discriminate between benign and malignant breast lesions. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05492-z.
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Krajnc D, Papp L, Nakuz TS, Magometschnigg HF, Grahovac M, Spielvogel CP, Ecsedi B, Bago-Horvath Z, Haug A, Karanikas G, Beyer T, Hacker M, Helbich TH, Pinker K. Breast Tumor Characterization Using [ 18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics. Cancers (Basel) 2021; 13:cancers13061249. [PMID: 33809057 PMCID: PMC8000810 DOI: 10.3390/cancers13061249] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/06/2021] [Accepted: 03/09/2021] [Indexed: 12/20/2022] Open
Abstract
Simple Summary Breast cancer is the second most common diagnosed malignancy in women worldwide. In this study, we examine the feasibility of breast tumor characterization based on [18F]FDG-PET/CT images using machine learning (ML) approaches in combination with data-preprocessing techniques. ML prediction models for breast cancer detection and the identification of breast cancer receptor status, proliferation rate, and molecular subtypes were established and evaluated. Furthermore, the importance of most repeatable features was investigated. Results displayed high performance of malignant/benign tumor differentiation and triple negative tumor subtype ML models. We observed high repeatability of radiomic features for both high performing predictive models. Abstract Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [18F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were compared with conventional data analysis using standard uptake value lesion classification. Methods: A cohort of 170 patients with 173 breast cancer tumors (132 malignant, 38 benign) was examined with [18F]FDG-PET/CT. Breast tumors were segmented and radiomic features were extracted following the imaging biomarker standardization initiative (IBSI) guidelines combined with optimized feature extraction. Ensemble learning including five supervised ML algorithms was utilized in a 100-fold Monte Carlo (MC) cross-validation scheme. Data pre-processing methods were incorporated prior to machine learning, including outlier and borderline noisy sample detection, feature selection, and class imbalance correction. Feature importance in each model was assessed by calculating feature occurrence by the R-squared method across MC folds. Results: Cross validation demonstrated high performance of the cancer detection model (80% sensitivity, 78% specificity, 80% accuracy, 0.81 area under the curve (AUC)), and of the triple negative tumor identification model (85% sensitivity, 78% specificity, 82% accuracy, 0.82 AUC). The individual receptor status and luminal A/B subtype models yielded low performance (0.46–0.68 AUC). SUVmax model yielded 0.76 AUC in cancer detection and 0.70 AUC in predicting triple negative subtype. Conclusions: Predictive models based on [18F]FDG-PET/CT images in combination with advanced data pre-processing steps aid in breast cancer diagnosis and in ML-based prediction of the aggressive triple negative breast cancer subtype.
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Affiliation(s)
- Denis Krajnc
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
| | - Laszlo Papp
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
| | - Thomas S. Nakuz
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
| | - Heinrich F. Magometschnigg
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (H.F.M.); (T.H.H.); or (K.P.)
| | - Marko Grahovac
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, 1090 Vienna, Austria
| | - Clemens P. Spielvogel
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, 1090 Vienna, Austria
| | - Boglarka Ecsedi
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
| | | | - Alexander Haug
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, 1090 Vienna, Austria
| | - Georgios Karanikas
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
| | - Thomas Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
- Correspondence:
| | - Marcus Hacker
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
| | - Thomas H. Helbich
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (H.F.M.); (T.H.H.); or (K.P.)
| | - Katja Pinker
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (H.F.M.); (T.H.H.); or (K.P.)
- Memorial Sloan Kettering Cancer Center, Breast Imaging Service, Department of Radiology, New York, NY 10065, USA
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Fowler AM, Kumar M, Bancroft LH, Salem K, Johnson JM, Karow J, Perlman SB, Bradshaw TJ, Hurley SA, McMillan AB, Strigel RM. Measuring Glucose Uptake in Primary Invasive Breast Cancer Using Simultaneous Time-of-Flight Breast PET/MRI: A Method Comparison Study with Prone PET/CT. Radiol Imaging Cancer 2021; 3:e200091. [PMID: 33575660 PMCID: PMC7850238 DOI: 10.1148/rycan.2021200091] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/24/2020] [Accepted: 10/28/2020] [Indexed: 12/26/2022]
Abstract
Purpose To compare the measurement of glucose uptake in primary invasive breast cancer using simultaneous, time-of-flight breast PET/MRI with prone time-of-flight PET/CT. Materials and Methods In this prospective study, women with biopsy-proven invasive breast cancer undergoing preoperative breast MRI from 2016 to 2018 were eligible. Participants who had fasted underwent prone PET/CT of the breasts approximately 60 minutes after injection of 370 MBq (10 mCi) fluorine 18 fluorodeoxyglucose (18F-FDG) followed by prone PET/MRI using standard clinical breast MRI sequences performed simultaneously with PET acquisition. Volumes of interest were drawn for tumors and contralateral normal breast fibroglandular tissue to calculate standardized uptake values (SUVs). Spearman correlation, Wilcoxon signed ranked test, Mann-Whitney test, and Bland-Altman analyses were performed. Results Twenty-three women (mean age, 50 years; range, 33-70 years) were included. Correlation between tumor uptake values measured with PET/MRI and PET/CT was strong (r s = 0.95-0.98). No difference existed between modalities for tumor maximum SUV (SUVmax) normalized to normal breast tissue SUVmean (normSUVmax) (P = .58). The least amount of measurement bias was observed with normSUVmax, +3.86% (95% limits of agreement: -28.92, +36.64). Conclusion These results demonstrate measurement agreement between PET/CT, the current reference standard for tumor glucose uptake quantification, and simultaneous time-of-flight breast 18F-FDG PET/MRI.Keywords: Breast, Comparative Studies, PET/CT, PET/MR Supplemental material is available for this article. © RSNA, 2021See also the commentary by Mankoff and Surti in this issue.
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Affiliation(s)
- Amy M. Fowler
- From the Departments of Radiology (A.M.F., M.K., L.H.B., K.S., J.M.J., J.K., S.B.P., T.J.B., S.A.H., A.B.M., R.M.S.) and Medical Physics (A.M.F., R.M.S.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252; and University of Wisconsin Carbone Cancer Center, Madison, Wis (A.M.F., R.M.S.)
| | - Manoj Kumar
- From the Departments of Radiology (A.M.F., M.K., L.H.B., K.S., J.M.J., J.K., S.B.P., T.J.B., S.A.H., A.B.M., R.M.S.) and Medical Physics (A.M.F., R.M.S.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252; and University of Wisconsin Carbone Cancer Center, Madison, Wis (A.M.F., R.M.S.)
| | - Leah Henze Bancroft
- From the Departments of Radiology (A.M.F., M.K., L.H.B., K.S., J.M.J., J.K., S.B.P., T.J.B., S.A.H., A.B.M., R.M.S.) and Medical Physics (A.M.F., R.M.S.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252; and University of Wisconsin Carbone Cancer Center, Madison, Wis (A.M.F., R.M.S.)
| | - Kelley Salem
- From the Departments of Radiology (A.M.F., M.K., L.H.B., K.S., J.M.J., J.K., S.B.P., T.J.B., S.A.H., A.B.M., R.M.S.) and Medical Physics (A.M.F., R.M.S.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252; and University of Wisconsin Carbone Cancer Center, Madison, Wis (A.M.F., R.M.S.)
| | - Jacob M. Johnson
- From the Departments of Radiology (A.M.F., M.K., L.H.B., K.S., J.M.J., J.K., S.B.P., T.J.B., S.A.H., A.B.M., R.M.S.) and Medical Physics (A.M.F., R.M.S.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252; and University of Wisconsin Carbone Cancer Center, Madison, Wis (A.M.F., R.M.S.)
| | | | - Scott B. Perlman
- From the Departments of Radiology (A.M.F., M.K., L.H.B., K.S., J.M.J., J.K., S.B.P., T.J.B., S.A.H., A.B.M., R.M.S.) and Medical Physics (A.M.F., R.M.S.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252; and University of Wisconsin Carbone Cancer Center, Madison, Wis (A.M.F., R.M.S.)
| | - Tyler J. Bradshaw
- From the Departments of Radiology (A.M.F., M.K., L.H.B., K.S., J.M.J., J.K., S.B.P., T.J.B., S.A.H., A.B.M., R.M.S.) and Medical Physics (A.M.F., R.M.S.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252; and University of Wisconsin Carbone Cancer Center, Madison, Wis (A.M.F., R.M.S.)
| | - Samuel A. Hurley
- From the Departments of Radiology (A.M.F., M.K., L.H.B., K.S., J.M.J., J.K., S.B.P., T.J.B., S.A.H., A.B.M., R.M.S.) and Medical Physics (A.M.F., R.M.S.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252; and University of Wisconsin Carbone Cancer Center, Madison, Wis (A.M.F., R.M.S.)
| | - Alan B. McMillan
- From the Departments of Radiology (A.M.F., M.K., L.H.B., K.S., J.M.J., J.K., S.B.P., T.J.B., S.A.H., A.B.M., R.M.S.) and Medical Physics (A.M.F., R.M.S.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252; and University of Wisconsin Carbone Cancer Center, Madison, Wis (A.M.F., R.M.S.)
| | - Roberta M. Strigel
- From the Departments of Radiology (A.M.F., M.K., L.H.B., K.S., J.M.J., J.K., S.B.P., T.J.B., S.A.H., A.B.M., R.M.S.) and Medical Physics (A.M.F., R.M.S.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252; and University of Wisconsin Carbone Cancer Center, Madison, Wis (A.M.F., R.M.S.)
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Ming Y, Wu N, Qian T, Li X, Wan DQ, Li C, Li Y, Wu Z, Wang X, Liu J, Wu N. Progress and Future Trends in PET/CT and PET/MRI Molecular Imaging Approaches for Breast Cancer. Front Oncol 2020; 10:1301. [PMID: 32903496 PMCID: PMC7435066 DOI: 10.3389/fonc.2020.01301] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 06/23/2020] [Indexed: 12/13/2022] Open
Abstract
Breast cancer is a major disease with high morbidity and mortality in women worldwide. Increased use of imaging biomarkers has been shown to add more information with clinical utility in the detection and evaluation of breast cancer. To date, numerous studies related to PET-based imaging in breast cancer have been published. Here, we review available studies on the clinical utility of different PET-based molecular imaging methods in breast cancer diagnosis, staging, distant-metastasis detection, therapeutic and prognostic prediction, and evaluation of therapeutic responses. For primary breast cancer, PET/MRI performed similarly to MRI but better than PET/CT. PET/CT and PET/MRI both have higher sensitivity than MRI in the detection of axillary and extra-axillary nodal metastases. For distant metastases, PET/CT has better performance in the detection of lung metastasis, while PET/MRI performs better in the liver and bone. Additionally, PET/CT is superior in terms of monitoring local recurrence. The progress in novel radiotracers and PET radiomics presents opportunities to reclassify tumors by combining their fine anatomical features with molecular characteristics and develop a beneficial pathway from bench to bedside to predict the treatment response and prognosis of breast cancer. However, further investigation is still needed before application of these modalities in clinical practice. In conclusion, PET-based imaging is not suitable for early-stage breast cancer, but it adds value in identifying regional nodal disease and distant metastases as an adjuvant to standard diagnostic imaging. Recent advances in imaging techniques would further widen the comprehensive and convergent applications of PET approaches in the clinical management of breast cancer.
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Affiliation(s)
- Yue Ming
- PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Nan Wu
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.,Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing, China.,Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing, China
| | - Tianyi Qian
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiao Li
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - David Q Wan
- Department of Diagnostic and Interventional Imaging, McGovern Medical School, Health and Science Center at Houston, University of Texas, Houston, TX, United States
| | - Caiying Li
- Department of Medical Imaging, Second Hospital of Hebei Medical University, Hebei, China
| | - Yalun Li
- Department of Breast Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Zhihong Wu
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing, China.,Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing, China.,Department of Central Laboratory, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Xiang Wang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiaqi Liu
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing, China.,Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ning Wu
- PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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19
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PET/MRI in breast cancer patients: Added value, barriers to implementation, and solutions. Clin Imaging 2020; 68:24-28. [PMID: 32562923 DOI: 10.1016/j.clinimag.2020.06.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 05/18/2020] [Accepted: 06/01/2020] [Indexed: 11/21/2022]
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20
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Whisenant JG, Williams JM, Kang H, Arlinghaus LR, Abramson RG, Abramson VG, Fakhoury K, Chakravarthy AB, Yankeelov TE. Quantitative Comparison of Prone and Supine PERCIST Measurements in Breast Cancer. ACTA ACUST UNITED AC 2020; 6:170-176. [PMID: 32548293 PMCID: PMC7289244 DOI: 10.18383/j.tom.2020.00002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Positron emission tomography (PET) is typically performed in the supine position. However, breast magnetic resonance imaging (MRI) is performed in prone, as this improves visibility of deep breast tissues. With the emergence of hybrid scanners that integrate molecular information from PET and functional information from MRI, it is of great interest to determine if the prognostic utility of prone PET is equivalent to supine. We compared PERCIST (PET Response Criteria in Solid Tumors) measurements between prone and supine FDG-PET in patients with breast cancer and the effect of orientation on predicting pathologic complete response (pCR). In total, 47 patients were enrolled and received up to 6 cycles of neoadjuvant therapy. Prone and supine FDG-PET were performed at baseline (t0; n = 46), after cycle 1 (t1; n = 1) or 2 (t2; n = 10), or after all neoadjuvant therapy (t3; n = 19). FDG uptake was quantified by maximum and peak standardized uptake value (SUV) with and without normalization to lean body mass; that is, SUVmax, SUVpeak, SULmax, and SULpeak. PERCIST measurements were performed for each paired baseline and post-treatment scan. Receiver operating characteristic analysis for the prediction of pCR was performed using logistic regression that included age and tumor size as covariates. SUV and SUL metrics were significantly different between orientation (P < .001), but were highly correlated (P > .98). Importantly, no differences were observed with the PERCIST measurements (P > .6). Overlapping 95% confidence intervals for the receiver operating characteristic analysis suggested no difference at predicting pCR. Therefore, prone and supine PERCIST in this data set were not statistically different.
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Affiliation(s)
- Jennifer G Whisenant
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.,Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Jason M Williams
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
| | - Hakmook Kang
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN.,Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Lori R Arlinghaus
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
| | - Richard G Abramson
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Vandana G Abramson
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.,Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Kareem Fakhoury
- Department of Radiation Oncology, University of Colorado Cancer Center-Anschutz Medical Campus, Aurora, CO
| | - A Bapsi Chakravarthy
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.,Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN; and
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences; Livestrong Cancer Institutes; Department of Biomedical Engineering; Department of Diagnostic Medicine; and Department of Oncology, The University of Texas, Austin, TX
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21
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Magometschnigg H, Pinker K, Helbich T, Brandstetter A, Rudas M, Nakuz T, Baltzer P, Wadsak W, Hacker M, Weber M, Dubsky P, Filipits M. PIK3CA Mutational Status Is Associated with High Glycolytic Activity in ER+/HER2- Early Invasive Breast Cancer: a Molecular Imaging Study Using [ 18F]FDG PET/CT. Mol Imaging Biol 2020; 21:991-1002. [PMID: 30652258 DOI: 10.1007/s11307-018-01308-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
PURPOSE In PIK3CA mutant breast cancer, downstream hyperactivation of the PI3K/AKT/mTOR pathway may be associated with increased glycolysis of cancer cells. The purpose of this study was to investigate the functional association of PIK3CA mutational status and tumor glycolysis in invasive ER+/HER2- early breast cancer. PROCEDURES This institutional review board-approved retrospective study included a dataset of 67 ER+/HER2- early breast cancer patients. All patients underwent 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/X-ray computed tomography ([18F]FDG PET/CT) and clinico-pathologic assessments as part of a prospective study. For this retrospective analysis, pyrosequencing was used to detect PIK3CA mutations of exons 4, 7, 9, and 20. Tumor glucose metabolism was assessed semi-quantitatively with [18F]FDG PET/CT using maximum standardized uptake values (SUVmax). SUVmax values were corrected for the partial volume effect, and metabolic tumor volume was calculated using the volume of interest automated lesion growing function 2D tumor size, i.e., maximum tumor diameter was assessed on concurrent pre-treatment contrast-enhanced magnetic resonance imaging. RESULTS PIK3CA mutations were present in 45 % of all tumors. Mutations were associated with a small tumor diameter (p < 0.01) and with low nuclear grade (p = 0.04). Glycolytic activity was positively associated with nuclear grade (p = 0.01), proliferation (p = 0.002), regional lymph node metastasis (p = 0.015), and metabolic tumor volume (p = 0.001) but not with tumor size/T-stage. In invasive ductal carcinomas, median SUVmax was increased in PIK3CA-mutated compared to wild-type tumors; however, this increase did not reach statistical significance (p = 0.05). Multivariate analysis of invasive ductal carcinomas revealed [18F]FDG uptake to be independently associated with PIK3CA status (p = 0.002) and nuclear tumor grade (p = 0.046). Size, volume, and regional nodal status had no influence on glycolytic activity. PIK3CA mutational status did not influence glycolytic metabolism in lobular carcinomas. Glycolytic activity and PIK3CA mutational status had no significant influence on recurrence-free survival or disease-specific survival. CONCLUSIONS In ER+/HER2- invasive ductal carcinomas of the breast, glucose uptake is independently associated with PIK3CA mutations. Initial data suggest that [18F]FDG uptake reflects complex genomic alterations and may have the potential to be used as candidate biomarker for monitoring therapeutic response and resistance mechanisms in emerging therapies that target the PI3K/AKT/mTOR pathway.
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Affiliation(s)
- Heinrich Magometschnigg
- Department of Biomedical Imaging and Image-guided Therapy, Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Thomas Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - Anita Brandstetter
- Institute of Cancer Research, Department of Medicine I, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Margaretha Rudas
- Department of Pathology, Medical University of Vienna, Vienna, Austria
| | - Thomas Nakuz
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Pascal Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Wadsak
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Michael Weber
- Department of Biomedical Imaging and Image-guided Therapy, Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - Peter Dubsky
- Department of Surgery, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria.
- Department of Surgery, Breast Centre Clinic St. Anna, Lucerne, Switzerland.
| | - Martin Filipits
- Institute of Cancer Research, Department of Medicine I, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
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22
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Leithner D, Helbich TH, Bernard-Davila B, Marino MA, Avendano D, Martinez DF, Jochelson MS, Kapetas P, Baltzer PAT, Haug A, Hacker M, Tanyildizi Y, Morris EA, Pinker K. Multiparametric 18F-FDG PET/MRI of the Breast: Are There Differences in Imaging Biomarkers of Contralateral Healthy Tissue Between Patients With and Without Breast Cancer? J Nucl Med 2020; 61:20-25. [PMID: 31253745 PMCID: PMC6954464 DOI: 10.2967/jnumed.119.230003] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 05/16/2019] [Indexed: 02/06/2023] Open
Abstract
The rationale was to assess whether there are differences in multiparametric 18F-FDG PET/MRI biomarkers of contralateral healthy breast tissue in patients with benign and malignant breast tumors. Methods: In this institutional review board-approved prospective single-institution study, 141 women with imaging abnormalities on mammography or sonography (BI-RADS 4/5) underwent combined 18F-FDG PET/MRI of the breast at 3T with dynamic contrast-enhanced MRI, diffusion-weighted imaging, and the radiotracer 18F-FDG. In all patients, the following imaging biomarkers were recorded for the contralateral (tumor-free) breast: breast parenchymal uptake (BPU) (from 18F-FDG PET), mean apparent diffusion coefficient (from diffusion-weighted imaging), background parenchymal enhancement (BPE), and amount of fibroglandular tissue (FGT) (from MRI). Appropriate statistical tests were used to assess differences in 18F-FDG PET/MRI biomarkers between patients with benign and malignant lesions. Results: There were 100 malignant and 41 benign lesions. BPE was minimal in 61 patients, mild in 56, moderate in 19, and marked in 5. BPE differed significantly (P < 0.001) between patients with benign and malignant lesions, with patients with cancer demonstrating decreased BPE in the contralateral tumor-free breast. FGT approached but did not reach significance (P = 0.055). BPU was 1.5 for patients with minimal BPE, 1.9 for mild BPE, 2.2 for moderate BPE, and 1.9 for marked BPE. BPU differed significantly between patients with benign lesions (mean, 1.9) and patients with malignant lesions (mean, 1.8) (P < 0.001). Mean apparent diffusion coefficient did not differ between groups (P = 0.19). Conclusion: Differences in multiparametric 18F-FDG PET/MRI biomarkers, obtained from contralateral tumor-free breast tissue, exist between patients with benign and patients with malignant breast tumors. Contralateral BPE, BPU, and FGT are decreased in breast cancer patients and may potentially serve as imaging biomarkers for the presence of malignancy.
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Affiliation(s)
- Doris Leithner
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Thomas H Helbich
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Blanca Bernard-Davila
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Maria Adele Marino
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Biomedical Sciences and Morphologic and Functional Imaging, University of Messina, Messina, Italy
| | - Daly Avendano
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Nuevo Leon, Mexico
| | - Danny F Martinez
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Maxine S Jochelson
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Panagiotis Kapetas
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Pascal A T Baltzer
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Alexander Haug
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, Vienna, Austria; and
| | - Marcus Hacker
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Yasemin Tanyildizi
- Department of Neuroradiology, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
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23
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Quantitative Multiparametric Breast Ultrasound: Application of Contrast-Enhanced Ultrasound and Elastography Leads to an Improved Differentiation of Benign and Malignant Lesions. Invest Radiol 2019; 54:257-264. [PMID: 30632985 DOI: 10.1097/rli.0000000000000543] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
OBJECTIVES The aim of this study was to evaluate breast multiparametric ultrasound (mpUS) and its potential to reduce unnecessary breast biopsies with 1, 2, or 3 additional quantitative parameters (Doppler, elastography, and contrast-enhanced ultrasound [CEUS]) to B-mode and investigate possible variations with different reader experience. MATERIALS AND METHODS This prospective study included 124 women (age range, 18-82 years; mean, 52 years), each with 1 new breast lesion, scheduled for ultrasound-guided biopsy between October 2015 and September 2016. Each lesion was examined with B-mode, elastography (Virtual Touch IQ [VTIQ]), Doppler, and CEUS, and different quantitative parameters were recorded for each modality. Four readers (2 experienced breast radiologists and 2 in-training) independently evaluated B-mode images of each lesion and assigned a BI-RADS (Breast Imaging Reporting and Data System) score. Using the area under the receiver operating characteristic curve (AUC), the most accurate quantitative parameter for each modality was chosen. These were then combined with the BI-RADS scores of all readers. Descriptive statistics and AUC were used to evaluate the diagnostic performance of mpUS. RESULTS Sixty-five lesions were malignant. MpUS with B-mode and 2 additional quantitative parameters (VTIQ and CEUS or Doppler) showed the highest diagnostic performance for all readers (averaged AUCs, 0.812-0.789 respectively vs 0.683 for B-mode, P = 0.0001). Both combinations significantly reduced the number of false-positive findings up to 46.9% (P < 0.0001). CONCLUSIONS Quantitative mpUS with 2 different triple assessment modalities (B-mode, VTIQ elastography, CEUS, or Doppler) shows the best diagnostic performance for breast cancer diagnosis and leads to a significant reduction of false-positive biopsy recommendations, for both experienced and inexperienced readers.
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24
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Advanced approaches to imaging primary breast cancer: an update. Clin Transl Imaging 2019. [DOI: 10.1007/s40336-019-00346-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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25
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PET/CT for Patients With Breast Cancer: Where Is the Clinical Impact? AJR Am J Roentgenol 2019; 213:254-265. [DOI: 10.2214/ajr.19.21177] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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26
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Leithner D, Horvat JV, Bernard-Davila B, Helbich TH, Ochoa-Albiztegui RE, Martinez DF, Zhang M, Thakur SB, Wengert GJ, Staudenherz A, Jochelson MS, Morris EA, Baltzer PAT, Clauser P, Kapetas P, Pinker K. A multiparametric [ 18F]FDG PET/MRI diagnostic model including imaging biomarkers of the tumor and contralateral healthy breast tissue aids breast cancer diagnosis. Eur J Nucl Med Mol Imaging 2019; 46:1878-1888. [PMID: 31197455 PMCID: PMC6647078 DOI: 10.1007/s00259-019-04331-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 04/03/2019] [Indexed: 02/03/2023]
Abstract
Purpose To develop a multiparametric [18F]FDG positron emission tomography/magnetic resonance imaging (PET/MRI) model for breast cancer diagnosis incorporating imaging biomarkers of breast tumors and contralateral healthy breast tissue. Methods In this prospective study and retrospective data analysis, 141 patients (mean 57 years) with an imaging abnormality detected on mammography and/or ultrasound (BI-RADS 4/5) underwent combined multiparametric [18F]FDG PET/MRI with PET/computed tomography and multiparametric MRI of the breast at 3 T. Images were evaluated and the following were recorded: for the tumor, BI-RADS descriptors on dynamic contrast-enhanced (DCE)-MRI, mean apparent diffusion co-efficient (ADCmean) on diffusion-weighted imaging (DWI), and maximum standard uptake value (SUVmax) on [18F]FDG-PET; and for the contralateral healthy breast, background parenchymal enhancement (BPE) and amount of fibroglandular tissue (FGT) on DCE-MRI, ADCmean on DWI, and SUVmax. Histopathology served as standard of reference. Uni-, bi-, and multivariate logistic regression analyses were performed to assess the relationships between malignancy and imaging features. Predictive discrimination of benign and malignant breast lesions was examined using area under the receiver operating characteristic curve (AUC). Results There were 100 malignant and 41 benign lesions (size: median 1.9, range 0.5–10 cm). The multivariate regression model incorporating significant univariate predictors identified tumor enhancement kinetics (P = 0.0003), tumor ADCmean (P < 0.001), and BPE of the contralateral healthy breast (P = 0.0019) as independent predictors for breast cancer diagnosis. Other biomarkers did not reach significance. Combination of the three significant biomarkers achieved an AUC value of 0.98 for breast cancer diagnosis. Conclusion A multiparametric [18F]FDG PET/MRI diagnostic model incorporating both qualitative and quantitative parameters of the tumor and the healthy contralateral tissue aids breast cancer diagnosis.
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Affiliation(s)
- Doris Leithner
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
| | - Joao V Horvat
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Blanca Bernard-Davila
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - R Elena Ochoa-Albiztegui
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Danny F Martinez
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Michelle Zhang
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Sunitha B Thakur
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Georg J Wengert
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - Anton Staudenherz
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Maxine S Jochelson
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria.
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Diffusion-Weighted Imaging With Apparent Diffusion Coefficient Mapping for Breast Cancer Detection as a Stand-Alone Parameter: Comparison With Dynamic Contrast-Enhanced and Multiparametric Magnetic Resonance Imaging. Invest Radiol 2019; 53:587-595. [PMID: 29620604 DOI: 10.1097/rli.0000000000000465] [Citation(s) in RCA: 114] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PURPOSE The aims of this study were to compare dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) with apparent diffusion coefficient mapping as a stand-alone parameter without any other supportive sequence for breast cancer detection and to assess its combination as multiparametric MRI (mpMRI) of the breast. MATERIALS AND METHODS In this institutional review board-approved single-center study, prospectively acquired data of 106 patients who underwent breast MRI from 12/2010 to 09/2014 for an imaging abnormality (Breast Imaging Reporting and Data System 0, 4/5) were retrospectively analyzed. Four readers independently assessed DWI and DCE as well as combined as mpMRI. Breast Imaging Reporting and Data System categories, lesion size, and mean apparent diffusion coefficient values were recorded. Histopathology was used as the gold standard. Appropriate statistical tests were used to compare diagnostic values. RESULTS There were 69 malignant and 41 benign tumors in 106 patients. Four patients presented with bilateral lesions. Dynamic contrast-enhanced MRI was the most sensitive test for breast cancer detection, with an average sensitivity of 100%. Diffusion-weighted imaging alone was less sensitive (82%; P < 0.001) but more specific than DCE-MRI (86.8% vs 76.6%; P = 0.002). Diagnostic accuracy was 83.7% for DWI and 90.6% for DCE-MRI. Multiparametric MRI achieved a sensitivity of 96.8%, not statistically different from DCE-MRI (P = 0.12) and with a similar specificity as DWI (83.8%; P = 0.195), maximizing diagnostic accuracy to 91.9%. There was almost perfect interreader agreement for DWI (κ = 0.864) and DCE-MRI (κ = 0.875) for differentiation of benign and malignant lesions. CONCLUSION Dynamic contrast-enhanced MRI is most sensitive for breast cancer detection and thus still indispensable. Multiparametric MRI using DCE-MRI and DWI maintains a high sensitivity, increases specificity, and maximizes diagnostic accuracy, often preventing unnecessary breast biopsies. Diffusion-weighted imaging should not be used as a stand-alone parameter because it detects significantly fewer cancers in comparison with DCE-MRI and mpMRI.
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Vogl WD, Pinker K, Helbich TH, Bickel H, Grabner G, Bogner W, Gruber S, Bago-Horvath Z, Dubsky P, Langs G. Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features. Eur Radiol Exp 2019; 3:18. [PMID: 31030291 PMCID: PMC6486931 DOI: 10.1186/s41747-019-0096-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Accepted: 03/07/2019] [Indexed: 02/08/2023] Open
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 features, for lesion classification. The CAD pipeline detected and segmented suspicious regions and classified lesions as benign or malignant. The inherent feature selection method of RF and alternatively the minimum-redundancy-maximum-relevance feature ranking method were used. Results In 34 patients, we report a detection rate of 10/12 (83.3%) and 22/22 (100%) for benign and malignant lesions, respectively, a Dice similarity coefficient of 0.665 for segmentation, and a classification performance with an area under the curve at receiver operating characteristics analysis of 0.978, a sensitivity of 0.946, and a specificity of 0.936. Segmentation but not classification performance of DCE-MRI improved with information from DWI and FDG-PET. Feature ranking revealed that kinetic and spatio-temporal texture features had the highest contribution for lesion classification. 18F-FDG-PET and morphologic features were less predictive. Conclusion Our CAD enables the assessment of the relevance of mpPET/MRI features on segmentation and classification accuracy. It may aid as a novel computational tool for exploring different modalities/features and their contributions for the detection and classification of breast lesions. Electronic supplementary material The online version of this article (10.1186/s41747-019-0096-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Wolf-Dieter Vogl
- Computational Imaging Research Laboratory, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Katja Pinker
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria.,Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Thomas H Helbich
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria
| | - Hubert Bickel
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria
| | - Günther Grabner
- MR Center of Excellence, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria.,Department of Radiologic Technology, Carinthia University of Applied Sciences, Klagenfurt, Austria
| | - Wolfgang Bogner
- MR Center of Excellence, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria
| | - Stephan Gruber
- MR Center of Excellence, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria
| | | | - Peter Dubsky
- Department of Surgery, Medical University Vienna, 1090, Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Laboratory, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
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Sequential [ 18F]FDG-[ 18F]FMISO PET and Multiparametric MRI at 3T for Insights into Breast Cancer Heterogeneity and Correlation with Patient Outcomes: First Clinical Experience. CONTRAST MEDIA & MOLECULAR IMAGING 2019; 2019:1307247. [PMID: 30728757 PMCID: PMC6341235 DOI: 10.1155/2019/1307247] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 11/27/2018] [Accepted: 12/17/2018] [Indexed: 12/29/2022]
Abstract
The aim of this study was to assess whether sequential multiparametric 18[F]fluoro-desoxy-glucose (18[F]FDG)/[18F]fluoromisonidazole ([18F]FMISO) PET-MRI in breast cancer patients is possible, facilitates information on tumor heterogeneity, and correlates with prognostic indicators. In this pilot study, IRB-approved, prospective study, nine patients with ten suspicious breast lesions (BIRADS 5) and subsequent breast cancer diagnosis underwent sequential combined [18F]FDG/[18F]FMISO PET-MRI. [18F]FDG was used to assess increased glycolysis, while [18F]FMISO was used to detect tumor hypoxia. MRI protocol included dynamic breast contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). Qualitative and quantitative multiparametric imaging findings were compared with pathological features (grading, proliferation, and receptor status) and clinical endpoints (recurrence/metastases and disease-specific death) using multiple correlation analysis. Histopathology was the standard of reference. There were several intermediate to strong correlations identified between quantitative bioimaging markers, histopathologic tumor characteristics, and clinical endpoints. Based on correlation analysis, multiparametric criteria provided independent information. The prognostic indicators proliferation rate, death, and presence/development of recurrence/metastasis correlated positively, whereas the prognostic indicator estrogen receptor status correlated negatively with PET parameters. The strongest correlations were found between disease-specific death and [18F]FDGmean (R=0.83, p < 0.01) and between the presence/development of metastasis and [18F]FDGmax (R=0.79, p < 0.01), respectively. This pilot study indicates that multiparametric [18F]FDG/[18F]FMISO PET-MRI might provide complementary quantitative prognostic information on breast tumors including clinical endpoints and thus might be used to tailor treatment for precision medicine in breast cancer.
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Pujara AC, Kim E, Axelrod D, Melsaether AN. PET/MRI in Breast Cancer. J Magn Reson Imaging 2018; 49:328-342. [DOI: 10.1002/jmri.26298] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 07/30/2018] [Accepted: 07/31/2018] [Indexed: 12/12/2022] Open
Affiliation(s)
- Akshat C. Pujara
- Department of Radiology, Division of Breast Imaging; University of Michigan Health System; Ann Arbor Michigan USA
| | - Eric Kim
- Department of Radiology; NYU School of Medicine; New York New York USA
| | - Deborah Axelrod
- Department of Surgery; Perlmutter Cancer Center, NYU School of Medicine; New York New York USA
| | - Amy N. Melsaether
- Department of Radiology; NYU School of Medicine; New York New York USA
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Lin CY, Lin CL, Kao CH. Staging/restaging performance of F18-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging in breast cancer: A review and meta-analysis. Eur J Radiol 2018; 107:158-165. [DOI: 10.1016/j.ejrad.2018.09.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 07/23/2018] [Accepted: 09/03/2018] [Indexed: 01/04/2023]
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Pinker K, Chin J, Melsaether AN, Morris EA, Moy L. Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment. Radiology 2018; 287:732-747. [PMID: 29782246 DOI: 10.1148/radiol.2018172171] [Citation(s) in RCA: 168] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. © RSNA, 2018.
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Affiliation(s)
- Katja Pinker
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Joanne Chin
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Amy N Melsaether
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Elizabeth A Morris
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Linda Moy
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
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Burt JR, Torosdagli N, Khosravan N, RaviPrakash H, Mortazi A, Tissavirasingham F, Hussein S, Bagci U. Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. Br J Radiol 2018; 91:20170545. [PMID: 29565644 DOI: 10.1259/bjr.20170545] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Deep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the development of computer-aided detection and diagnosis (CAD) systems. These technologies have long been thought of as "second-opinion" tools for radiologists and clinicians. However, with significant improvements in deep neural networks, the diagnostic capabilities of learning algorithms are approaching levels of human expertise (radiologists, clinicians etc.), shifting the CAD paradigm from a "second opinion" tool to a more collaborative utility. This paper reviews recently developed CAD systems based on deep learning technologies for breast cancer diagnosis, explains their superiorities with respect to previously established systems, defines the methodologies behind the improved achievements including algorithmic developments, and describes remaining challenges in breast cancer screening and diagnosis. We also discuss possible future directions for new CAD models that continue to change as artificial intelligence algorithms evolve.
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Affiliation(s)
- Jeremy R Burt
- 1 Department of Radiology, Florida Hospital , Orlando, FL , USA.,2 Department of Computer Science, Center for Research in Computer Vision, University of Central Florida (UCF) , Orlando, FL , USA
| | - Neslisah Torosdagli
- 2 Department of Computer Science, Center for Research in Computer Vision, University of Central Florida (UCF) , Orlando, FL , USA
| | - Naji Khosravan
- 2 Department of Computer Science, Center for Research in Computer Vision, University of Central Florida (UCF) , Orlando, FL , USA
| | - Harish RaviPrakash
- 2 Department of Computer Science, Center for Research in Computer Vision, University of Central Florida (UCF) , Orlando, FL , USA
| | - Aliasghar Mortazi
- 2 Department of Computer Science, Center for Research in Computer Vision, University of Central Florida (UCF) , Orlando, FL , USA
| | | | - Sarfaraz Hussein
- 2 Department of Computer Science, Center for Research in Computer Vision, University of Central Florida (UCF) , Orlando, FL , USA
| | - Ulas Bagci
- 2 Department of Computer Science, Center for Research in Computer Vision, University of Central Florida (UCF) , Orlando, FL , USA
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Mannheim JG, Schmid AM, Schwenck J, Katiyar P, Herfert K, Pichler BJ, Disselhorst JA. PET/MRI Hybrid Systems. Semin Nucl Med 2018; 48:332-347. [PMID: 29852943 DOI: 10.1053/j.semnuclmed.2018.02.011] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Over the last decade, the combination of PET and MRI in one system has proven to be highly successful in basic preclinical research, as well as in clinical research. Nowadays, PET/MRI systems are well established in preclinical imaging and are progressing into clinical applications to provide further insights into specific diseases, therapeutic assessments, and biological pathways. Certain challenges in terms of hardware had to be resolved concurrently with the development of new techniques to be able to reach the full potential of both combined techniques. This review provides an overview of these challenges and describes the opportunities that simultaneous PET/MRI systems can exploit in comparison with stand-alone or other combined hybrid systems. New approaches were developed for simultaneous PET/MRI systems to correct for attenuation of 511 keV photons because MRI does not provide direct information on gamma photon attenuation properties. Furthermore, new algorithms to correct for motion were developed, because MRI can accurately detect motion with high temporal resolution. The additional information gained by the MRI can be employed to correct for partial volume effects as well. The development of new detector designs in combination with fast-decaying scintillator crystal materials enabled time-of-flight detection and incorporation in the reconstruction algorithms. Furthermore, this review lists the currently commercially available systems both for preclinical and clinical imaging and provides an overview of applications in both fields. In this regard, special emphasis has been placed on data analysis and the potential for both modalities to evolve with advanced image analysis tools, such as cluster analysis and machine learning.
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Affiliation(s)
- Julia G Mannheim
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Andreas M Schmid
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Johannes Schwenck
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany; Department of Nuclear Medicine and Clinical Molecular Imaging, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Prateek Katiyar
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Kristina Herfert
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Bernd J Pichler
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany.
| | - Jonathan A Disselhorst
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
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Heller SL, Heacock L, Moy L. Developments in Breast Imaging: Update on New and Evolving MR Imaging and Molecular Imaging Techniques. Magn Reson Imaging Clin N Am 2018; 26:247-258. [PMID: 29622129 DOI: 10.1016/j.mric.2017.12.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
This article reviews new developments in breast imaging. There is growing interest in creating a shorter, less expensive MR protocol with broader applicability. There is an increasing focus on and consideration for the additive impact that functional analysis of breast pathology have on identifying and characterizing lesions. These developments apply to MR imaging and molecular imaging. This article reviews evolving breast imaging techniques with attention to strengths, weaknesses, and applications of these approaches. We aim to give the reader familiarity with the state of current developments in the field and to increase awareness of what to expect in breast imaging.
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Affiliation(s)
- Samantha Lynn Heller
- NYU School of Medicine, NYU Laura and Isaac Perlmutter Cancer Center, 3rd Floor, New York, NY 10016, USA
| | - Laura Heacock
- NYU School of Medicine, NYU Laura and Isaac Perlmutter Cancer Center, 3rd Floor, New York, NY 10016, USA
| | - Linda Moy
- NYU School of Medicine, NYU Laura and Isaac Perlmutter Cancer Center, 3rd Floor, New York, NY 10016, USA.
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Bennani-Baiti B, Dietzel M, Baltzer PA. MRI for the assessment of malignancy in BI-RADS 4 mammographic microcalcifications. PLoS One 2017; 12:e0188679. [PMID: 29190656 PMCID: PMC5708819 DOI: 10.1371/journal.pone.0188679] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Accepted: 11/11/2017] [Indexed: 12/11/2022] Open
Abstract
Purpose Assess the performance of breast MRI to diagnose breast cancer in BI-RADS 4 microcalcifications detected by mammography. Materials and methods This retrospective, IRB-approved study included 248 consecutive contrast-enhanced breast MRI (1.5T, protocol in accordance with EUSOBI recommendations) performed to further diagnose BI-RADS 4 microcalcifications detected at mammography during a 3-year period. Standard of reference had to be established by histopathology. Routine consensus reading results by two radiologists were dichotomized as positive or negative and compared with the reference standard (benign vs malignant) to calculate diagnostic parameters. Results There were 107 malignant and 141 benign microcalcifications. Malignancy rates were 18.3% (23/126 BI-RADS 4a), 41.7% (25/60 BI-RADS 4b) and 95% (59/62 BI-RADS 4c). There were 103 true-positive, 116 true-negative, 25 false-positive, and 4 false-negative (one invasive cancer, three DCIS; 2 BI-RADS 4c, 1 BI-RADS 4b on mammography) breast MRI findings, effecting a sensitivity, specificity, PPV, and NPV of 96.3% (95%-CI 90.7–99.0%), 82.3% (95%-CI 75.0–88.2%), 80.5% (95%-CI 72.5–87.0%) and 96.7% (95%-CI 91.7–99.1%), respectively. Conclusion MRI is an accurate tool to further diagnose BI-RADS 4a and 4b microcalcifications and may be helpful to avoid unnecessary biopsies in BI-RADS 4a and 4b lesions. BI-RADS 4c microcalcifications should be biopsied irrespective of MRI findings.
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Affiliation(s)
- Barbara Bennani-Baiti
- Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
- Department of Biomedical Imaging and Image-guided Therapy, Vienna General Hospital (AKH), Medical University of Vienna, Vienna, Austria
| | - Matthias Dietzel
- Department of Radiology, University of Erlangen-Nürnberg, Nürnberg, Germany
| | - Pascal A Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Vienna General Hospital (AKH), Medical University of Vienna, Vienna, Austria
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Clauser P, Mann R, Athanasiou A, Prosch H, Pinker K, Dietzel M, Helbich TH, Fuchsjäger M, Camps-Herrero J, Sardanelli F, Forrai G, Baltzer PAT. A survey by the European Society of Breast Imaging on the utilisation of breast MRI in clinical practice. Eur Radiol 2017; 28:1909-1918. [PMID: 29168005 PMCID: PMC5882636 DOI: 10.1007/s00330-017-5121-4] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 09/08/2017] [Accepted: 10/05/2017] [Indexed: 12/24/2022]
Abstract
OBJECTIVES While magnetic resonance imaging (MRI) is considered a helpful diagnostic tool in breast imaging, discussions are ongoing about appropriate protocols and indications. The European Society of Breast Imaging (EUSOBI) launched a survey to evaluate the utilisation of breast MRI in clinical practice. METHODS An online survey reviewed by the EUSOBI board and committees was distributed amongst members. The questions encompassed: training and experience; annual breast MRI and MRI-guided-intervention workload; examination protocols; indications; reporting habits and preferences. Data were summarised and subgroups compared using χ2 test. RESULTS Of 647 EUSOBI members, 177 (27.4%) answered the survey. The majority were radiologists (90.5%), half of them based in academic centres (51.9%). Common indications for MRI included cancer staging, treatment monitoring, high-risk screening and problem-solving, and differed significantly between countries (p≤0.03). Structured reporting and BI-RADS were mostly used. Breast radiologists with ≤10 years of experience preferred inclusion of additional techniques, such as T2/STIR (p=0.03) and DWI (p=0.08) in the scan protocol. MRI-guided interventions were performed by a minority of participants (35.4%). CONCLUSIONS The utilisation of breast MRI in clinical practice is generally in line with international recommendations. There are substantial differences between countries. MRI-guided interventions and functional MRI parameters are not widely available. KEY POINTS • MRI is commonly used for the detection and characterisation of breast lesions. • Clinical practice standards are generally in line with current recommendations. • Standardised criteria and diagnostic categories (mainly BI-RADS) are widely adopted. • Younger radiologists value additional techniques, such as T2/STIR and DWI. • MRI-guided breast biopsy is not widely available.
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Affiliation(s)
- Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna/General Hospital Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Ritse Mann
- Department of Radiology, Radboud University Nijmegen Medical Centre, Geert Grooteplein Zuid 10, 6525, GA, Nijmegen, The Netherlands
| | - Alexandra Athanasiou
- Department of Radiology, Division of Breast Imaging, "MITERA" Hospital, 6 Erythrou Stavrou Street, 151 23, Athens, Greece
| | - Helmut Prosch
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna/General Hospital Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Katja Pinker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna/General Hospital Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Matthias Dietzel
- Institute of Diagnostic Radiology, University Hospital Erlangen, Maximiliansplatz 1, 91054, Erlangen, Germany
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna/General Hospital Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Michael Fuchsjäger
- Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9/P, 8036, Graz, Austria
| | - Julia Camps-Herrero
- Department of Radiology, Hospital de la Ribera, Carretera de Corbera, Km. 1, 46600, Alzira, Valencia, Spain
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy.,Department of Radiology, IRCCS (Research Hospital) Policlinico San Donato, Via Morandi 30, 20097, San Donato Milanese, Milan, Italy
| | - Gabor Forrai
- Department of Radiology, Duna Medical Center, Lechner Ödön fasor 7, Budapest, 1095, Hungary
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna/General Hospital Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
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Abstract
CLINICAL/METHODICAL ISSUE Magnetic resonance imaging (MRI) of the breast is an indispensable tool in breast imaging for many indications. Several functional parameters with MRI and positron emission tomography (PET) have been assessed for imaging of breast tumors and their combined application is defined as multiparametric imaging. Available data suggest that multiparametric imaging using different functional MRI and PET parameters can provide detailed information about the hallmarks of cancer and may provide additional specificity. STANDARD RADIOLOGICAL METHODS Multiparametric and molecular imaging of the breast comprises established MRI parameters, such as dynamic contrast-enhanced MRI, diffusion-weighted imaging (DWI), MR proton spectroscopy ((1)H-MRSI) as well as combinations of radiological and MRI techniques (e. g. PET/CT and PET/MRI) using radiotracers, such as fluorodeoxyglucose (FDG). METHODICAL INNOVATIONS Multiparametric and molecular imaging of the breast can be performed at different field-strengths (range 1.5-7 T). Emerging parameters comprise novel promising techniques, such as sodium imaging ((23)Na MRI), phosphorus spectroscopy ((31)P-MRSI), chemical exchange saturation transfer (CEST) imaging, blood oxygen level-dependent (BOLD) and hyperpolarized MRI as well as various specific radiotracers. ACHIEVEMENTS Multiparametric and molecular imaging has multiple applications in breast imaging. Multiparametric and molecular imaging of the breast is an evolving field that will enable improved detection, characterization, staging and monitoring for personalized medicine in breast cancer.
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Leithner D, Wengert G, Helbich T, Morris E, Pinker K. MRI in the Assessment of BI-RADS® 4 lesions. Top Magn Reson Imaging 2017; 26:191-199. [PMID: 28961568 DOI: 10.1097/rmr.0000000000000138] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) lexicon, which is used ubiquitously to standardize reporting of breast magnetic resonance imaging (MRI), provides 7 BI-RADS assessment categories to indicate the level of suspicion of malignancy and guide further management. A BI-RADS category 4 assessment is assigned when an imaging abnormality does not fulfill the typical criteria for malignancy, but is suspicious enough to warrant a recommendation for biopsy. The BI-RADS category 4 assessment covers a wide range of probability of malignancy, from >2 to <95%. MRI is an essential noninvasive technique in breast imaging and the role of MRI in the assessment of ACR BI-RADS 4 lesions is manifold. In lesions classified as suspicious on imaging with mammography, digital breast tomosynthesis, and sonography, MRI can aid in the noninvasive differentiation of benign and malignant lesions and obviate unnecessary breast biopsies. When the suspicion of cancer is confirmed with MRI, concurrent staging of disease for treatment planning can be accomplished. This article will provide a comprehensive overview of the role of breast MRI in the assessment of ACR BI-RADS 4 lesions. In addition, we will discuss strategies to decrease false positives and avoid false negative results when reporting MRI of the breast.
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Affiliation(s)
- Doris Leithner
- *Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany †Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria ‡Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
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Dietzel M, Kaiser C, Pinker K, Wenkel E, Hammon M, Uder M, Bennani Baiti B, Clauser P, Schulz-Wendtland R, Baltzer P. Automated Semi-Quantitative Analysis of Breast MRI: Potential Imaging Biomarker for the Prediction of Tissue Response to Neoadjuvant Chemotherapy. Breast Care (Basel) 2017; 12:231-236. [PMID: 29070986 PMCID: PMC5649261 DOI: 10.1159/000480226] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND We aimed to investigate an automated semi-quantitative software as an imaging biomarker for the prediction of tissue response (TR) after completion of neoadjuvant chemotherapy (NAC). METHODS Breast magnetic resonance imaging (MRI) (1.5T, protocol according to international recommendations) of 67 patients with biopsy-proven invasive breast cancer were examined before and after NAC. After completion of NAC, histopathologic assessments of TR were classified according to the Chevallier grading system (CG1/4: full/non-responder; CG2/C3: partial responder). A commercially available fully automatic software (CADstream) extracted MRI parameters of tumor extension (tumor diameter/volume: TD/TV). Pre- versus post-NAC values were compared (ΔTV and ΔTD). Additionally, the software performed volumetric analyses of vascularization (VAV) after NAC. Accuracy of MRI parameters to predict TR were identified (cross-tabs, ROC, AUC, Kruskal-Wallis). RESULTS There were 37 (34.3%) CG1, 7 (6.5%) CG2, 53 (49.1%) CG3, and 11 (10.2%) CG4 lesions. The software reached area under the curve levels of 79.5% (CG1/complete response: ΔTD), 68.6% (CG2, CG3/partial response: VAV), and 88.8% to predict TR (CG4/non-response: ΔTV). CONCLUSION Semi-quantitative automated analysis of breast MRI data enabled the prediction of tissue response to NAC.
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Affiliation(s)
- Matthias Dietzel
- Department of Radiology, University Hospital Erlangen-Nürnberg, Erlangen, Germany
| | - Clemens Kaiser
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Mannheim, Germany
| | - Katja Pinker
- Department of Radiology, Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Medical University of Vienna, Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Vienna, Austria
| | - Evelyn Wenkel
- Department of Radiology, University Hospital Erlangen-Nürnberg, Erlangen, Germany
| | - Matthias Hammon
- Department of Radiology, University Hospital Erlangen-Nürnberg, Erlangen, Germany
| | - Michael Uder
- Department of Radiology, University Hospital Erlangen-Nürnberg, Erlangen, Germany
| | - Barbara Bennani Baiti
- Medical University of Vienna, Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Vienna, Austria
| | - Paola Clauser
- Medical University of Vienna, Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Vienna, Austria
| | | | - Pascal Baltzer
- Medical University of Vienna, Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Vienna, Austria
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Woitek R, Spick C, Schernthaner M, Rudas M, Kapetas P, Bernathova M, Furtner J, Pinker K, Helbich TH, Baltzer PAT. A simple classification system (the Tree flowchart) for breast MRI can reduce the number of unnecessary biopsies in MRI-only lesions. Eur Radiol 2017; 27:3799-3809. [PMID: 28275900 PMCID: PMC5544808 DOI: 10.1007/s00330-017-4755-6] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 01/09/2017] [Accepted: 01/19/2017] [Indexed: 12/20/2022]
Abstract
OBJECTIVES To assess whether using the Tree flowchart obviates unnecessary magnetic resonance imaging (MRI)-guided biopsies in breast lesions only visible on MRI. METHODS This retrospective IRB-approved study evaluated consecutive suspicious (BI-RADS 4) breast lesions only visible on MRI that were referred to our institution for MRI-guided biopsy. All lesions were evaluated according to the Tree flowchart for breast MRI by experienced readers. The Tree flowchart is a decision rule that assigns levels of suspicion to specific combinations of diagnostic criteria. Receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic accuracy. To assess reproducibility by kappa statistics, a second reader rated a subset of 82 patients. RESULTS There were 454 patients with 469 histopathologically verified lesions included (98 malignant, 371 benign lesions). The area under the curve (AUC) of the Tree flowchart was 0.873 (95% CI: 0.839-0.901). The inter-reader agreement was almost perfect (kappa: 0.944; 95% CI 0.889-0.998). ROC analysis revealed exclusively benign lesions if the Tree node was ≤2, potentially avoiding unnecessary biopsies in 103 cases (27.8%). CONCLUSIONS Using the Tree flowchart in breast lesions only visible on MRI, more than 25% of biopsies could be avoided without missing any breast cancer. KEY POINTS • The Tree flowchart may obviate >25% of unnecessary MRI-guided breast biopsies. • This decrease in MRI-guided biopsies does not cause any false-negative cases. • The Tree flowchart predicts 30.6% of malignancies with >98% specificity. • The Tree's high specificity aids in decision-making after benign biopsy results.
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Affiliation(s)
- Ramona Woitek
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Claudio Spick
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Melanie Schernthaner
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Margaretha Rudas
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Maria Bernathova
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Julia Furtner
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Katja Pinker
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
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Polanec SH, Andrzejewski P, Baltzer PAT, Helbich TH, Stiglbauer A, Georg D, Karanikas G, Susani M, Wadsak W, Margreiter M, Mitterhauser M, Brader P, Pinker K. Multiparametric [11C]Acetate positron emission tomography-magnetic resonance imaging in the assessment and staging of prostate cancer. PLoS One 2017; 12:e0180790. [PMID: 28719629 PMCID: PMC5515396 DOI: 10.1371/journal.pone.0180790] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 06/21/2017] [Indexed: 02/06/2023] Open
Abstract
Background The aim of this study was to evaluate whether MP [11C]Acetate PET-MRI enables an accurate differentiation of benign and malignant prostate tumors as well as local and distant staging. Materials and methods Fifty-six consecutive patients fulfilling the following criteria were included in this IRB-approved prospective study: elevated PSA levels or suspicious findings at digital rectal examination or TRUS; and histopathological verification. All patients underwent MP [11C]Acetate PET-MRI of the prostate performed on separate scanners with PET/CT using [11C]Acetate and 3T MP MR imaging. Appropriate statistical tests were used to determine diagnostic accuracy, local and distant staging. Results MP imaging with two MRI parameters (T2w and DWI) achieved the highest sensitivity, specificity, and diagnostic accuracy of 95%, 68.8%, and 88%, with an AUC of 0.82 for primary PCa detection. Neither assessments with a single parameter (AUC, 0.54–0.79), nor different combinations with up to five parameters (AUC, 0.67–0.79) achieved equally good results. MP [11C]Acetate PET-MRI improved local staging with a sensitivity, specificity, and diagnostic accuracy of 100%, 96%, and 97% compared to MRI alone with 72.2%, 100%, and 95.5%. MP [11C]Acetate PET-MRI correctly detected osseous and liver metastases in five patients. Conclusions MP [11C]Acetate PET-MRI merges morphologic with functional information, and allows insights into tumor biology. MP [11C]Acetate PET-MRI with two MRI-derived parameters (T2 and DWI) yields the highest diagnostic accuracy. The addition of more parameters does not improve diagnostic accuracy of primary PCa detection. MP [11C]Acetate PET-MRI facilitates improved local and distant staging, providing “one-stop” staging in patients with primary PCa, and therefore has the potential to improve therapy. Patient summary In this report we investigated MP [11C]Acetate PET-MRI for detection, local and distant staging of prostate cancer. We demonstrate that MP [11C]Acetate PET-MRI with two MRI-derived parameters (T2 and DWI) achieves the best diagnostic accuracy for primary prostate cancer detection and that MP [11C]Acetate PET-MRI enables an improved local and distant staging.
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Affiliation(s)
- Stephan H. Polanec
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Piotr Andrzejewski
- Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Department of Radiation Oncology, Division of Medical Radiation Physics, Medical University of Vienna, Vienna, Austria
| | - Pascal A. T. Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Thomas H. Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - Alexander Stiglbauer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - Dietmar Georg
- Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Department of Radiation Oncology, Division of Medical Radiation Physics, Medical University of Vienna, Vienna, Austria
| | - Georgios Karanikas
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Martin Susani
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Wadsak
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Markus Margreiter
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - Markus Mitterhauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Peter Brader
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - Katja Pinker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
- * E-mail:
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Folkert MR, Setton J, Apte AP, Grkovski M, Young RJ, Schöder H, Thorstad WL, Lee NY, Deasy JO, Hun Oh J. Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics. Phys Med Biol 2017; 62:5327-5343. [PMID: 28604368 PMCID: PMC5729737 DOI: 10.1088/1361-6560/aa73cc] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
In this study, we investigate the use of imaging feature-based outcomes research ('radiomics') combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted. Using machine learning-based feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. All model building methods were tested by cross validation to avoid overfitting, and final outcome models were validated on an independent dataset from a collaborating institution. Multiparameter models were statistically significant on 5 fold cross validation with the area under the receiver operating characteristic curve (AUC) = 0.65 (p = 0.004), 0.73 (p = 0.026), and 0.66 (p = 0.015) for ACM, LF, and DM, respectively. The model for LF retained significance on the independent validation cohort with AUC = 0.68 (p = 0.029) whereas the models for ACM and DM did not reach statistical significance, but resulted in comparable predictive power to the 5 fold cross validation with AUC = 0.60 (p = 0.092) and 0.65 (p = 0.062), respectively. In the largest study of its kind to date, predictive features including increasing metabolic tumor volume, increasing image heterogeneity, and increasing tumor surface irregularity significantly correlated to mortality, LF, and DM on 5 fold cross validation in a relatively uniform single-institution cohort. The LF model also retained significance in an independent population.
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Affiliation(s)
- Michael R. Folkert
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jeremy Setton
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Aditya P. Apte
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Milan Grkovski
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Robert J. Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Heiko Schöder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Wade L. Thorstad
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Nancy Y. Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Joseph O. Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Marino MA, Helbich T, Baltzer P, Pinker-Domenig K. Multiparametric MRI of the breast: A review. J Magn Reson Imaging 2017. [DOI: 10.1002/jmri.25790] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Maria Adele Marino
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging; Medical University of Vienna; Austria
- Department of Biomedical Sciences and Morphologic and Functional Imaging, Policlinico Universitario G. Martino; University of Messina; Messina Italy
| | - Thomas Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging; Medical University of Vienna; Austria
| | - Pascal Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging; Medical University of Vienna; Austria
| | - Katja Pinker-Domenig
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging; Medical University of Vienna; Austria
- Department of Radiology; Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center; New York New York USA
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Abstract
The future clinical use of the combination of positron emission tomography (PET) with 2-Fluoro[F-18]-2-Deoxy-d-Glucose (FDG)and MRI is still unclear. If a patient requires a PET and breast DCE-MRI for staging purposes, both scans can be done in the same visit. In the breast, DCE-MRI is better at lesion detection (sensitivity), margin evaluation, and has a higher specificity than CT. The potential for multiparametric qualitative and quantitative imaging is also an advantage of PET/MRI which provides opportunity to improve tumor characterization and may ultimately lead to outcome prediction. This review discusses technical and clinical aspects of this emerging technology in breast cancer patients.
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47
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Grueneisen J, Sawicki LM, Wetter A, Kirchner J, Kinner S, Aktas B, Forsting M, Ruhlmann V, Umutlu L. Evaluation of PET and MR datasets in integrated 18F-FDG PET/MRI: A comparison of different MR sequences for whole-body restaging of breast cancer patients. Eur J Radiol 2017; 89:14-19. [DOI: 10.1016/j.ejrad.2016.12.019] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Revised: 12/13/2016] [Accepted: 12/19/2016] [Indexed: 12/15/2022]
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Abstract
Breast and whole-body PET/MR imaging is being used to detect local and metastatic disease and is being investigated for potential imaging biomarkers, which may eventually help personalize treatments and prognoses. This article provides an overview of breast and whole-body PET/MR exam techniques, summarizes PET and MR breast imaging for lesion detection, outlines investigations into multi-parametric breast PET/MR, looks at breast PET/MR in the setting of neo-adjuvant chemotherapy, and reviews the pros and cons of whole-body PET/MR in the setting of metastatic or suspected metastatic breast cancer.
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Affiliation(s)
- Amy Melsaether
- Department of Radiology, New York University School of Medicine, 160 East 34th Street, 3rd Floor, New York, NY 10016, USA.
| | - Linda Moy
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI(2)R), New York University School of Medicine, 160 East 34th Street, 3rd Floor, New York, NY 10016, USA
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Mus RD, Borelli C, Bult P, Weiland E, Karssemeijer N, Barentsz JO, Gubern-Mérida A, Platel B, Mann RM. Time to enhancement derived from ultrafast breast MRI as a novel parameter to discriminate benign from malignant breast lesions. Eur J Radiol 2017; 89:90-96. [PMID: 28267555 DOI: 10.1016/j.ejrad.2017.01.020] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2016] [Revised: 12/01/2016] [Accepted: 01/18/2017] [Indexed: 01/10/2023]
Abstract
OBJECTIVES To investigate time to enhancement (TTE) as novel dynamic parameter for lesion classification in breast magnetic resonance imaging (MRI). METHODS In this retrospective study, 157 women with 195 enhancing abnormalities (99 malignant and 96 benign) were included. All patients underwent a bi-temporal MRI protocol that included ultrafast time-resolved angiography with stochastic trajectory (TWIST) acquisitions (1.0×0.9×2.5mm, temporal resolution 4.32s), during the inflow of contrast agent. TTE derived from TWIST series and relative enhancement versus time curve type derived from volumetric interpolated breath-hold examination (VIBE) series were assessed and combined with basic morphological information to differentiate benign from malignant lesions. Receiver operating characteristic analysis and kappa statistics were applied. RESULTS TTE had a significantly better discriminative ability than curve type (p<0.001 and p=0.026 for reader 1 and 2, respectively). Including morphology, sensitivity of TWIST and VIBE assessment was equivalent (p=0.549 and p=0.344, respectively). Specificity and diagnostic accuracy were significantly higher for TWIST than for VIBE assessment (p<0.001). Inter-reader agreement in differentiating malignant from benign lesions was almost perfect for TWIST evaluation (κ=0.86) and substantial for conventional assessment (κ=0.75). CONCLUSIONS TTE derived from ultrafast TWIST acquisitions is a valuable parameter that allows robust differentiation between malignant and benign breast lesions with high accuracy.
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Affiliation(s)
- Roel D Mus
- Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA Nijmegen, The Netherlands.
| | - Cristina Borelli
- Department of Radiology, Scientific Institute "Casa Sollievo della Sofferenza" Hospital, Viale Cappuccini 1, 71013, San Giovanni Rotondo, Foggia, Italy; Department of Radiology, Radboud University Medical Center (internal address 766), Geert Grooteplein Zuid 10, 6525GA Nijmegen, The Netherlands.
| | - Peter Bult
- Department of Pathology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA Nijmegen, The Netherlands.
| | | | - Nico Karssemeijer
- Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA Nijmegen, The Netherlands.
| | - Jelle O Barentsz
- Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA Nijmegen, The Netherlands.
| | - Albert Gubern-Mérida
- Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA Nijmegen, The Netherlands.
| | - Bram Platel
- Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA Nijmegen, The Netherlands.
| | - Ritse M Mann
- Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA Nijmegen, The Netherlands.
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MRI fused with prone FDG PET/CT improves the primary tumour staging of patients with breast cancer. Eur Radiol 2016; 27:3190-3198. [PMID: 28004161 DOI: 10.1007/s00330-016-4685-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Revised: 11/03/2016] [Accepted: 11/29/2016] [Indexed: 01/08/2023]
Abstract
OBJECTIVE Our aim was to evaluate the diagnostic accuracy of magnetic resonance imaging (MRI) fused with prone 2-[fluorine-18]-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) in primary tumour staging of patients with breast cancer. METHODS This retrospective study evaluated 45 women with 49 pathologically proven breast carcinomas. MRI and prone PET-CT scans with time-of-flight and point-spread-function reconstruction were performed with the same dedicated breast coil. The studies were assessed by a radiologist and a nuclear medicine physician, and evaluation of fused images was made by consensus. The final diagnosis was based on pathology (90 lesions) or follow-up ≥ 24 months (17 lesions). RESULTS The study assessed 72 malignant and 35 benign lesions with a median size of 1.8 cm (range 0.3-8.4 cm): 31 focal, nine multifocal and nine multicentric cases. In lesion-by-lesion analysis, sensitivity, specificity, positive and negative predictive values were 97%, 80%, 91% and 93% for MRI, 96%, 71%, 87%, and 89% for prone PET, and 97%. 94%, 97% and 94% for MRI fused with PET. Areas under the curve (AUC) were 0.953, 0.850, and 0.983, respectively (p < 0.01). CONCLUSIONS MRI fused with FDG-PET is more accurate than FDG-PET in primary tumour staging of breast cancer patients and increases the specificity of MRI. KEY POINTS • FDG PET-CT may improve the specificity of MRI in breast cancer staging. • MRI fused with prone 2-[fluorine-18]-fluoro-2-deoxy-D-glucose PET-CT has better overall diagnostic performance than MRI. • The clinical role of fused PET-MRI has not yet been established.
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