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Joy A, Thomas MA. Enhanced spectral resolution for correlated spectroscopic imaging using inner-product and covariance transform: a pilot analysis of metabolites and lipids in breast cancer in vivo. Sci Rep 2023; 13:16809. [PMID: 37798319 PMCID: PMC10556085 DOI: 10.1038/s41598-023-43356-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/22/2023] [Indexed: 10/07/2023] Open
Abstract
Acquisition duration of correlated spectroscopy in vivo can be longer due to a large number of t1 increments along the indirect (F1) dimension. Limited number of t1 increments on the other hand leads to poor spectral resolution along F1. Covariance transformation (CT) instead of Fourier transform along t1 is an alternative way of increasing the resolution of the 2D COSY spectrum. Prospectively undersampled five-dimensional echo-planar correlated spectroscopic imaging (EP-COSI) data from ten malignant patients and ten healthy women were acquired and reconstructed using compressed sensing. The COSY spectrum at each voxel location was then generated using FFT, CT and a variant of CT called Inner Product (IP). Metabolite and lipid ratios were computed with respect to water from unsuppressed one-dimensional spectrum. The effects of t1-ridging artifacts commonly seen with FFT were not observed with CT/IP. Statistically significant differences were observed in the fat cross peaks measured with CT/IP/FFT. Spectral resolution was increased ~ 8.5 times (~ 19.53 Hz in FFT, ~ 2.32 Hz in CT/IP) without affecting the spectral width along F1 was possible with CT/IP. CT and IP enabled substantially increased F1 resolution effectively with significant gain in scan time and reliable measure of unsaturation index as a biomarker for malignant breast cancer.
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Affiliation(s)
- Ajin Joy
- Radiological Sciences, David Geffen School of Medicine at UCLA, 10945 Peter V Ueberroth Building, Suite#1417A, Los Angeles, CA, 90095, USA
- Physics and Biology in Medicine IDP, University of California Los Angeles, Los Angeles, CA, USA
| | - M Albert Thomas
- Radiological Sciences, David Geffen School of Medicine at UCLA, 10945 Peter V Ueberroth Building, Suite#1417A, Los Angeles, CA, 90095, USA.
- Physics and Biology in Medicine IDP, University of California Los Angeles, Los Angeles, CA, USA.
- BioEngineering, University of California Los Angeles, Los Angeles, CA, USA.
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Sunassee ED, Jardim-Perassi BV, Madonna MC, Ordway B, Ramanujam N. Metabolic Imaging as a Tool to Characterize Chemoresistance and Guide Therapy in Triple-Negative Breast Cancer (TNBC). Mol Cancer Res 2023; 21:995-1009. [PMID: 37343066 PMCID: PMC10592445 DOI: 10.1158/1541-7786.mcr-22-1004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 04/07/2023] [Accepted: 06/15/2023] [Indexed: 06/23/2023]
Abstract
After an initial response to chemotherapy, tumor relapse is frequent. This event is reflective of both the spatiotemporal heterogeneities of the tumor microenvironment as well as the evolutionary propensity of cancer cell populations to adapt to variable conditions. Because the cause of this adaptation could be genetic or epigenetic, studying phenotypic properties such as tumor metabolism is useful as it reflects molecular, cellular, and tissue-level dynamics. In triple-negative breast cancer (TNBC), the characteristic metabolic phenotype is a highly fermentative state. However, during treatment, the spatial and temporal dynamics of the metabolic landscape are highly unstable, with surviving populations taking on a variety of metabolic states. Thus, longitudinally imaging tumor metabolism provides a promising approach to inform therapeutic strategies, and to monitor treatment responses to understand and mitigate recurrence. Here we summarize some examples of the metabolic plasticity reported in TNBC following chemotherapy and review the current metabolic imaging techniques available in monitoring chemotherapy responses clinically and preclinically. The ensemble of imaging technologies we describe has distinct attributes that make them uniquely suited for a particular length scale, biological model, and/or features that can be captured. We focus on TNBC to highlight the potential of each of these technological advances in understanding evolution-based therapeutic resistance.
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Affiliation(s)
- Enakshi D. Sunassee
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | | | - Megan C. Madonna
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Bryce Ordway
- Department of Cancer Physiology, Moffitt Cancer Center, Tampa, FL 33612, USA
- Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Nirmala Ramanujam
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27708, USA
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3
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Arponen O, Wodtke P, Gallagher FA, Woitek R. Hyperpolarised 13C-MRI using 13C-pyruvate in breast cancer: A review. Eur J Radiol 2023; 167:111058. [PMID: 37666071 DOI: 10.1016/j.ejrad.2023.111058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 08/14/2023] [Accepted: 08/21/2023] [Indexed: 09/06/2023]
Abstract
Tumour metabolism can be imaged with a novel imaging technique termed hyperpolarised carbon-13 (13C)-MRI using probes, i.e., endogenously found molecules that are labeled with 13C. Hyperpolarisation of the 13C label increases the sensitivity to a level that allows dynamic imaging of the distribution and metabolism of the probes. Dynamic imaging of [1-13C]pyruvate metabolism is of particular biological interest in cancer because of the Warburg effect resulting in the intratumoural accumulation of [1-13C]pyruvate and conversion to [1-13C]lactate. Numerous preclinical studies in breast cancer and other tumours have shown that hyperpolarised 13C-pyruvate has potential for metabolic phenotyping and response assessment at earlier timepoints than the current clinical imaging techniques allow. The clinical feasibility of hyperpolarised 13C-MRI after the injection of pyruvate in patients with breast cancer has now been demonstrated, with increased 13C-label exchange between pyruvate and lactate present in higher grade tumours with associated increased expression of the monocarboxylate transporter 1 (MCT1), the transmembrane transporter mediating intracellular pyruvate uptake, and lactate dehydrogenase (LDH) as the enzyme catalysing the conversion of pyruvate to lactate. Furthermore, a study in patients with breast cancer undergoing neoadjuvant chemotherapy suggested that early changes in 13C-label exchange can distinguish between patients who reach pathologic complete response (pCR) and those who do not. This review summarises the current literature on preclinical and clinical research on hyperpolarised 13C-MRI with [1-13C]-pyruvate in breast cancer imaging.
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Affiliation(s)
- Otso Arponen
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom.
| | - Pascal Wodtke
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Center, Cambridge, United Kingdom
| | - Ferdia A Gallagher
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Center, Cambridge, United Kingdom
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Center, Cambridge, United Kingdom; Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Danube Private University, Krems, Austria
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Joy A, Lin M, Joines M, Saucedo A, Lee-Felker S, Baker J, Chien A, Emir U, Macey PM, Thomas MA. Ensemble Learning for Breast Cancer Lesion Classification: A Pilot Validation Using Correlated Spectroscopic Imaging and Diffusion-Weighted Imaging. Metabolites 2023; 13:835. [PMID: 37512542 PMCID: PMC10385820 DOI: 10.3390/metabo13070835] [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: 05/05/2023] [Revised: 07/04/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
The main objective of this work was to evaluate the application of individual and ensemble machine learning models to classify malignant and benign breast masses using features from two-dimensional (2D) correlated spectroscopy spectra extracted from five-dimensional echo-planar correlated spectroscopic imaging (5D EP-COSI) and diffusion-weighted imaging (DWI). Twenty-four different metabolite and lipid ratios with respect to diagonal fat peaks (1.4 ppm, 5.4 ppm) from 2D spectra, and water and fat peaks (4.7 ppm, 1.4 ppm) from one-dimensional non-water-suppressed (NWS) spectra were used as the features. Additionally, water fraction, fat fraction and water-to-fat ratios from NWS spectra and apparent diffusion coefficients (ADC) from DWI were included. The nine most important features were identified using recursive feature elimination, sequential forward selection and correlation analysis. XGBoost (AUC: 93.0%, Accuracy: 85.7%, F1-score: 88.9%, Precision: 88.2%, Sensitivity: 90.4%, Specificity: 84.6%) and GradientBoost (AUC: 94.3%, Accuracy: 89.3%, F1-score: 90.7%, Precision: 87.9%, Sensitivity: 94.2%, Specificity: 83.4%) were the best-performing models. Conventional biomarkers like choline, myo-Inositol, and glycine were statistically significant predictors. Key features contributing to the classification were ADC, 2D diagonal peaks at 0.9 ppm, 2.1 ppm, 3.5 ppm, and 5.4 ppm, cross peaks between 1.4 and 0.9 ppm, 4.3 and 4.1 ppm, 2.3 and 1.6 ppm, and the triglyceryl-fat cross peak. The results highlight the contribution of the 2D spectral peaks to the model, and they demonstrate the potential of 5D EP-COSI for early breast cancer detection.
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Affiliation(s)
- Ajin Joy
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
| | - Marlene Lin
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
| | - Melissa Joines
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
| | - Andres Saucedo
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
- Physics and Biology in Medicine-Inter-Departmental Graduate Program, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Stephanie Lee-Felker
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
| | - Jennifer Baker
- Surgery, University of California Los Angeles, Los Angeles, CA 90095, USA;
| | - Aichi Chien
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
| | - Uzay Emir
- School of Health Sciences, College of Health and Human Sciences, Purdue University, West Lafayette, IN 47907, USA;
| | - Paul M. Macey
- School of Nursing, University of California Los Angeles, Los Angeles, CA 90095, USA;
| | - M. Albert Thomas
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
- Physics and Biology in Medicine-Inter-Departmental Graduate Program, University of California Los Angeles, Los Angeles, CA 90095, USA
- BioEngineering, University of California Los Angeles, Los Angeles, CA 90095, USA
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Woitek R, Brindle KM. Hyperpolarized Carbon-13 MRI in Breast Cancer. Diagnostics (Basel) 2023; 13:2311. [PMID: 37443703 DOI: 10.3390/diagnostics13132311] [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: 03/31/2023] [Revised: 06/29/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023] Open
Abstract
One of the hallmarks of cancer is metabolic reprogramming, including high levels of aerobic glycolysis (the Warburg effect). Pyruvate is a product of glucose metabolism, and 13C-MR imaging of the metabolism of hyperpolarized (HP) [1-13C]pyruvate (HP 13C-MRI) has been shown to be a potentially versatile tool for the clinical evaluation of tumor metabolism. Hyperpolarization of the 13C nuclear spin can increase the sensitivity of detection by 4-5 orders of magnitude. Therefore, following intravenous injection, the location of hyperpolarized 13C-labeled pyruvate in the body and its subsequent metabolism can be tracked using 13C-MRI. Hyperpolarized [13C]urea and [1,4-13C2]fumarate are also likely to translate to the clinic in the near future as tools for imaging tissue perfusion and post-treatment tumor cell death, respectively. For clinical breast imaging, HP 13C-MRI can be combined with 1H-MRI to address the need for detailed anatomical imaging combined with improved functional tumor phenotyping and very early identification of patients not responding to standard and novel neoadjuvant treatments. If the technical complexity of the hyperpolarization process and the relatively high associated costs can be reduced, then hyperpolarized 13C-MRI has the potential to become more widely available for large-scale clinical trials.
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Affiliation(s)
- Ramona Woitek
- Research Centre for Medical Image Analysis and AI, Danube Private University, 3500 Krems, Austria
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Kevin M Brindle
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, UK
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK
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Nissan N, Kulpanovich A, Agassi R, Allweis T, Haas I, Carmon E, Furman-Haran E, Anaby D, Sklair-Levy M, Tal A. Probing lipids relaxation times in breast cancer using magnetic resonance spectroscopic fingerprinting. Eur Radiol 2023; 33:3744-3753. [PMID: 36976338 DOI: 10.1007/s00330-023-09560-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 01/06/2023] [Accepted: 02/14/2023] [Indexed: 03/29/2023]
Abstract
OBJECTIVES To investigate the clinical relevance of the relaxation times of lipids within breast cancer and normal fibroglandular tissue in vivo, using magnetic resonance spectroscopic fingerprinting (MRSF). METHODS Twelve patients with biopsy-confirmed breast cancer and 14 healthy controls were prospectively scanned at 3 T using a protocol consisting of diffusion tensor imaging (DTI), MRSF, and dynamic contrast-enhanced (DCE) MRI. Single-voxel MRSF data was recorded from the tumor (patients) - identified using DTI - or normal fibroglandular tissue (controls), in under 20 s. MRSF data was analyzed using in-house software. Linear mixed model analysis was used to compare the relaxation times of lipids in breast cancer VOIs vs. normal fibroglandular tissue. RESULTS Seven distinguished lipid metabolite peaks were identified and their relaxation times were recorded. Of them, several exhibited statistically significant changes between controls and patients, with strong significance (p < 10-3) recorded for several of the lipid resonances at 1.3 ppm (T1 = 355 ± 17 ms vs. 389 ± 27 ms), 4.1 ppm (T1 = 255 ± 86 ms vs. 127 ± 33 ms), 5.22 ppm (T1 = 724 ± 81 ms vs. 516 ± 62 ms), and 5.31 ppm (T2 = 56 ± 5 ms vs. 44 ± 3.5 ms, respectively). CONCLUSIONS The application of MRSF to breast cancer imaging is feasible and achievable in clinically relevant scan time. Further studies are required to verify and comprehend the underling biological mechanism behind the differences in lipid relaxation times in cancer and normal fibroglandular tissue. KEY POINTS •The relaxation times of lipids in breast tissue are potential markers for quantitative characterization of the normal fibroglandular tissue and cancer. •Lipid relaxation times can be acquired rapidly in a clinically relevant manner using a single-voxel technique, termed MRSF. •Relaxation times of T1 at 1.3 ppm, 4.1 ppm, and 5.22 ppm, as well as of T2 at 5.31 ppm, were significantly different between measurements within breast cancer and the normal fibroglandular tissue.
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Affiliation(s)
- Noam Nissan
- Department of Radiology, Sheba Medical Center, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Alexey Kulpanovich
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Ravit Agassi
- Department of General Surgery, Soroka Medical Center, Beersheba, Israel
| | - Tanir Allweis
- Department of General Surgery, Kaplan Medical Center, Rehovot, Israel
| | - Ilana Haas
- Department of General Surgery, Meir Medical Center, Kefar Sava, Israel
| | - Einat Carmon
- Department of General Surgery, Hadassah Medical Center, Jerusalem, Israel
| | | | - Debbie Anaby
- Department of Radiology, Sheba Medical Center, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Miri Sklair-Levy
- Department of Radiology, Sheba Medical Center, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Assaf Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel.
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Panico C, Ferrara F, Woitek R, D’Angelo A, Di Paola V, Bufi E, Conti M, Palma S, Cicero SL, Cimino G, Belli P, Manfredi R. Staging Breast Cancer with MRI, the T. A Key Role in the Neoadjuvant Setting. Cancers (Basel) 2022; 14:cancers14235786. [PMID: 36497265 PMCID: PMC9739275 DOI: 10.3390/cancers14235786] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 11/27/2022] Open
Abstract
Breast cancer (BC) is the most common cancer among women worldwide. Neoadjuvant chemotherapy (NACT) indications have expanded from inoperable locally advanced to early-stage breast cancer. Achieving a pathological complete response (pCR) has been proven to be an excellent prognostic marker leading to better disease-free survival (DFS) and overall survival (OS). Although diagnostic accuracy of MRI has been shown repeatedly to be superior to conventional methods in assessing the extent of breast disease there are still controversies regarding the indication of MRI in this setting. We intended to review the complex literature concerning the tumor size in staging, response and surgical planning in patients with early breast cancer receiving NACT, in order to clarify the role of MRI. Morphological and functional MRI techniques are making headway in the assessment of the tumor size in the staging, residual tumor assessment and prediction of response. Radiomics and radiogenomics MRI applications in the setting of the prediction of response to NACT in breast cancer are continuously increasing. Tailored therapy strategies allow considerations of treatment de-escalation in excellent responders and avoiding or at least postponing breast surgery in selected patients.
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Affiliation(s)
- Camilla Panico
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
- Correspondence:
| | - Francesca Ferrara
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Ramona Woitek
- Medical Image Analysis and AI (MIAAI), Danube Private University, 3500 Krems, Austria
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, Cambridge CB2 0RE, UK
| | - Anna D’Angelo
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Valerio Di Paola
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Enida Bufi
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Marco Conti
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Simone Palma
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Stefano Lo Cicero
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Giovanni Cimino
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Paolo Belli
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Riccardo Manfredi
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
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8
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Joy A, Saucedo A, Joines M, Lee-Felker S, Kumar S, Sarma MK, Sayre J, DiNome M, Thomas MA. Correlated MR spectroscopic imaging of breast cancer to investigate metabolites and lipids: acceleration and compressed sensing reconstruction. BJR Open 2022; 4:20220009. [PMID: 36860693 PMCID: PMC9969076 DOI: 10.1259/bjro.20220009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 08/23/2022] [Accepted: 08/25/2022] [Indexed: 11/05/2022] Open
Abstract
Objectives The main objective of this work was to detect novel biomarkers in breast cancer by spreading the MR spectra over two dimensions in multiple spatial locations using an accelerated 5D EP-COSI technology. Methods The 5D EP-COSI data were non-uniformly undersampled with an acceleration factor of 8 and reconstructed using group sparsity-based compressed sensing reconstruction. Different metabolite and lipid ratios were then quantified and statistically analyzed for significance. Linear discriminant models based on the quantified metabolite and lipid ratios were generated. Spectroscopic images of the quantified metabolite and lipid ratios were also reconstructed. Results The 2D COSY spectra generated using the 5D EP-COSI technique showed differences among healthy, benign, and malignant tissues in terms of their mean values of metabolite and lipid ratios, especially the ratios of potential novel biomarkers based on unsaturated fatty acids, myo-inositol, and glycine. It is further shown the potential of choline and unsaturated lipid ratio maps, generated from the quantified COSY signals across multiple locations in the breast, to serve as complementary markers of malignancy that can be added to the multiparametric MR protocol. Discriminant models using metabolite and lipid ratios were found to be statistically significant for classifying benign and malignant tumor from healthy tissues. Conclusions Accelerated 5D EP-COSI technique demonstrates the potential to detect novel biomarkers such as glycine, myo-inositol, and unsaturated fatty acids in addition to commonly reported choline in breast cancer, and facilitates metabolite and lipid ratio maps which have the potential to play a significant role in breast cancer detection. Advances in knowledge This study presents the first evaluation of a multidimensional MR spectroscopic imaging technique for the detection of potentially novel biomarkers based on glycine, myo-inositol, and unsaturated fatty acids, in addition to commonly reported choline. Spatial mapping of choline and unsaturated fatty acid ratios with respect to water in malignant and benign breast masses are also shown. These metabolic characteristics may serve as additional biomarkers for improving the diagnostic and therapeutic evaluation of breast cancer.
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Affiliation(s)
- Ajin Joy
- Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | | | - Melissa Joines
- Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Stephanie Lee-Felker
- Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Sumit Kumar
- Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Manoj K Sarma
- Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - James Sayre
- Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Maggie DiNome
- Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
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9
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Dynamic characterization of breast cancer response to neoadjuvant therapy using biophysical metrics of spatial proliferation. Sci Rep 2022; 12:11718. [PMID: 35810187 PMCID: PMC9271064 DOI: 10.1038/s41598-022-15801-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/29/2022] [Indexed: 11/08/2022] Open
Abstract
Current tools to assess breast cancer response to neoadjuvant chemotherapy cannot reliably predict disease eradication, which if possible, could allow early cessation of therapy. In this work, we assessed the ability of an image data-driven mathematical modeling approach for dynamic characterization of breast cancer response to neoadjuvant therapy. We retrospectively analyzed patients enrolled in the I-SPY 2 TRIAL at the Atrium Health Wake Forest Baptist Comprehensive Cancer Center. Patients enrolled on the study received four MR imaging examinations during neoadjuvant therapy with acquisitions at baseline (T0), 3-weeks/early-treatment (T1), 12-weeks/mid-treatment (T2), and completion of therapy prior to surgery (T3). We use a biophysical mathematical model of tumor growth to generate spatial estimates of tumor proliferation to characterize the dynamics of treatment response. Using histogram summary metrics to quantify estimated tumor proliferation maps, we found strong correlation of mathematical model-estimated tumor proliferation with residual cancer burden, with Pearson correlation coefficients ranging from 0.88 and 0.97 between T0 and T2, representing a significant improvement from conventional assessment methods of change in mean apparent diffusion coefficient and functional tumor volume. This data shows the significant promise of imaging-based biophysical mathematical modeling methods for dynamic characterization of patient-specific response to neoadjuvant therapy with correlation to residual disease outcomes.
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10
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Mercado C, Chhor C, Scheel JR. MRI in the Setting of Neoadjuvant Treatment of Breast Cancer. JOURNAL OF BREAST IMAGING 2022; 4:320-330. [PMID: 38422421 DOI: 10.1093/jbi/wbab059] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Indexed: 03/02/2024]
Abstract
Neoadjuvant therapy may reduce tumor burden preoperatively, allowing breast conservation treatment for tumors previously unresectable or requiring mastectomy without reducing disease-free survival. Oncologists can also use the response of the tumor to neoadjuvant chemotherapy (NAC) to identify treatment likely to be successful against any unknown potential distant metastasis. Accurate preoperative estimations of tumor size are necessary to guide appropriate treatment with minimal delays and can provide prognostic information. Clinical breast examination and mammography are inaccurate methods for measuring tumor size after NAC and can over- and underestimate residual disease. While US is commonly used to measure changes in tumor size during NAC due to its availability and low cost, MRI remains more accurate and simultaneously images the entire breast and axilla. No method is sufficiently accurate at predicting complete pathological response that would obviate the need for surgery. Diffusion-weighted MRI, MR spectroscopy, and MRI-based radiomics are emerging fields that potentially increase the predictive accuracy of tumor response to NAC.
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Affiliation(s)
- Cecilia Mercado
- NYU Grossman School of Medicine, Department of Radiology, New York, NY, USA
| | - Chloe Chhor
- NYU Grossman School of Medicine, Department of Radiology, New York, NY, USA
| | - John R Scheel
- University of Washington, Department of Radiology, Seattle, WA, USA
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11
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Galati F, Rizzo V, Trimboli RM, Kripa E, Maroncelli R, Pediconi F. MRI as a biomarker for breast cancer diagnosis and prognosis. BJR Open 2022; 4:20220002. [PMID: 36105423 PMCID: PMC9459861 DOI: 10.1259/bjro.20220002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 05/01/2022] [Accepted: 05/04/2022] [Indexed: 11/05/2022] Open
Abstract
Breast cancer (BC) is the most frequently diagnosed female invasive cancer in Western countries and the leading cause of cancer-related death worldwide. Nowadays, tumor heterogeneity is a well-known characteristic of BC, since it includes several nosological entities characterized by different morphologic features, clinical course and response to treatment. Thus, with the spread of molecular biology technologies and the growing knowledge of the biological processes underlying the development of BC, the importance of imaging biomarkers as non-invasive information about tissue hallmarks has progressively grown. To date, breast magnetic resonance imaging (MRI) is considered indispensable in breast imaging practice, with widely recognized indications such as BC screening in females at increased risk, locoregional staging and neoadjuvant therapy (NAT) monitoring. Moreover, breast MRI is increasingly used to assess not only the morphologic features of the pathological process but also to characterize individual phenotypes for targeted therapies, building on developments in genomics and molecular biology features. The aim of this review is to explore the role of breast multiparametric MRI in providing imaging biomarkers, leading to an improved differentiation of benign and malignant breast lesions and to a customized management of BC patients in monitoring and predicting response to treatment. Finally, we discuss how breast MRI biomarkers offer one of the most fertile ground for artificial intelligence (AI) applications. In the era of personalized medicine, with the development of omics-technologies, machine learning and big data, the role of imaging biomarkers is embracing new opportunities for BC diagnosis and treatment.
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Affiliation(s)
- Francesca Galati
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | - Veronica Rizzo
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | | | - Endi Kripa
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | - Roberto Maroncelli
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | - Federica Pediconi
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
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12
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Kong X, Zhang Q, Wu X, Zou T, Duan J, Song S, Nie J, Tao C, Tang M, Wang M, Zou J, Xie Y, Li Z, Li Z. Advances in Imaging in Evaluating the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer. Front Oncol 2022; 12:816297. [PMID: 35669440 PMCID: PMC9163342 DOI: 10.3389/fonc.2022.816297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/29/2022] [Indexed: 11/13/2022] Open
Abstract
Neoadjuvant chemotherapy (NAC) is increasingly widely used in breast cancer treatment, and accurate evaluation of its response provides essential information for treatment and prognosis. Thus, the imaging tools used to quantify the disease response are critical in evaluating and managing patients treated with NAC. We discussed the recent progress, advantages, and disadvantages of common imaging methods in assessing the efficacy of NAC for breast cancer.
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Affiliation(s)
- Xianshu Kong
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Qian Zhang
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Xuemei Wu
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Tianning Zou
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jiajun Duan
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Shujie Song
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jianyun Nie
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Chu Tao
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Mi Tang
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Maohua Wang
- First Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jieya Zou
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Yu Xie
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Zhenhui Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Zhen Li
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
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13
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Nissan N, Bauer E, Moss Massasa EE, Sklair-Levy M. Breast MRI during pregnancy and lactation: clinical challenges and technical advances. Insights Imaging 2022; 13:71. [PMID: 35397082 PMCID: PMC8994812 DOI: 10.1186/s13244-022-01214-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/21/2022] [Indexed: 12/12/2022] Open
Abstract
The breast experiences substantial changes in morphology and function during pregnancy and lactation which affects its imaging properties and may reduce the visibility of a concurrent pathological process. The high incidence of benign gestational-related entities may further add complexity to the clinical and radiological evaluation of the breast during the period. Consequently, pregnancy-associated breast cancer (PABC) is often a delayed diagnosis and carries a poor prognosis. This state-of-the-art pictorial review illustrates how despite currently being underutilized, technical advances and new clinical evidence support the use of unenhanced breast MRI during pregnancy and both unenhanced and dynamic-contrast enhanced (DCE) during lactation, to serve as effective supplementary modalities in the diagnostic work-up of PABC.
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Affiliation(s)
- Noam Nissan
- Radiology Department, Sheba Medical Center, 5265601, Tel Hashomer, Israel.
- Sackler Medicine School, Tel Aviv University, Tel Aviv, Israel.
| | - Ethan Bauer
- Sackler Medicine School, New-York Program, Tel Aviv University, Tel Aviv, Israel
| | - Efi Efraim Moss Massasa
- Joint Medicine School Program of Sheba Medical Center, St. George's, University of London and the University of Nicosia, Sheba Medical Center, Tel Hashomer, Israel
| | - Miri Sklair-Levy
- Radiology Department, Sheba Medical Center, 5265601, Tel Hashomer, Israel
- Sackler Medicine School, Tel Aviv University, Tel Aviv, Israel
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14
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Shams Z, Klomp DWJ, Boer VO, Wijnen JP, Wiegers EC. Identifying the source of spurious signals caused by B 0 inhomogeneities in single-voxel 1 H MRS. Magn Reson Med 2022; 88:71-82. [PMID: 35344600 PMCID: PMC9311141 DOI: 10.1002/mrm.29222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 02/04/2022] [Accepted: 02/19/2022] [Indexed: 12/04/2022]
Abstract
Purpose Single‐voxel MRS (SV MRS) requires robust volume localization as well as optimized crusher and phase‐cycling schemes to reduce artifacts arising from signal outside the volume of interest. However, due to local magnetic field gradients (B0 inhomogeneities), signal that was dephased by the crusher gradients during acquisition might rephase, leading to artifacts in the spectrum. Here, we analyzed this mechanism, aiming to identify the source of signals arising from unwanted coherence pathways (spurious signals) in SV MRS from a B0 map. Methods We investigated all possible coherence pathways associated with imperfect localization in a semi‐localized by adiabatic selective refocusing (semi‐LASER) sequence for potential rephasing of signals arising from unwanted coherence pathways by a local magnetic field gradient. We searched for locations in the B0 map where the signal dephasing due to external (crusher) and internal (B0) field gradients canceled out. To confirm the mechanism, SV‐MR spectra (TE = 31 ms) and 3D‐CSI data with the same volume localization as the SV experiments were acquired from a phantom and 2 healthy volunteers. Results Our analysis revealed that potential sources of spurious signals were scattered over multiple locations throughout the brain. This was confirmed by 3D‐CSI data. Moreover, we showed that the number of potential locations where spurious signals could originate from monotonically decreases with crusher strength. Conclusion We proposed a method to identify the source of spurious signals in SV 1H MRS using a B0 map. This can facilitate MRS sequence design to be less sensitive to experimental artifacts.
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Affiliation(s)
- Zahra Shams
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Dennis W J Klomp
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Vincent O Boer
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Jannie P Wijnen
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Evita C Wiegers
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
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15
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Magnetic Resonance Imaging (MRI) and MR Spectroscopic Methods in Understanding Breast Cancer Biology and Metabolism. Metabolites 2022; 12:metabo12040295. [PMID: 35448482 PMCID: PMC9030399 DOI: 10.3390/metabo12040295] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 02/01/2023] Open
Abstract
A common malignancy that affects women is breast cancer. It is the second leading cause of cancer-related death among women. Metabolic reprogramming occurs during cancer growth, invasion, and metastases. Functional magnetic resonance (MR) methods comprising an array of techniques have shown potential for illustrating physiological and molecular processes changes before anatomical manifestations on conventional MR imaging. Among these, in vivo proton (1H) MR spectroscopy (MRS) is widely used for differentiating breast malignancy from benign diseases by measuring elevated choline-containing compounds. Further, the use of hyperpolarized 13C and 31P MRS enhanced the understanding of glucose and phospholipid metabolism. The metabolic profiling of an array of biological specimens (intact tissues, tissue extracts, and various biofluids such as blood, urine, nipple aspirates, and fine needle aspirates) can also be investigated through in vitro high-resolution NMR spectroscopy and high-resolution magic angle spectroscopy (HRMAS). Such studies can provide information on more metabolites than what is seen by in vivo MRS, thus providing a deeper insight into cancer biology and metabolism. The analysis of a large number of NMR spectral data sets through multivariate statistical methods classified the tumor sub-types. It showed enormous potential in the development of new therapeutic approaches. Recently, multiparametric MRI approaches were found to be helpful in elucidating the pathophysiology of cancer by quantifying structural, vasculature, diffusion, perfusion, and metabolic abnormalities in vivo. This review focuses on the applications of NMR, MRS, and MRI methods in understanding breast cancer biology and in the diagnosis and therapeutic monitoring of breast cancer.
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16
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Juchem C, Cudalbu C, de Graaf RA, Gruetter R, Henning A, Hetherington HP, Boer VO. B 0 shimming for in vivo magnetic resonance spectroscopy: Experts' consensus recommendations. NMR IN BIOMEDICINE 2021; 34:e4350. [PMID: 32596978 DOI: 10.1002/nbm.4350] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 05/14/2020] [Accepted: 05/19/2020] [Indexed: 05/07/2023]
Abstract
Magnetic resonance spectroscopy (MRS) and spectroscopic imaging (MRSI) allow the chemical analysis of physiological processes in vivo and provide powerful tools in the life sciences and for clinical diagnostics. Excellent homogeneity of the static B0 magnetic field over the object of interest is essential for achieving high-quality spectral results and quantitative metabolic measurements. The experimental minimization of B0 variation is performed in a process called B0 shimming. In this article, we summarize the concepts of B0 field shimming using spherical harmonic shimming techniques, specific strategies for B0 homogenization and crucial factors to consider for implementation and use in both brain and body. In addition, experts' recommendations are provided for minimum requirements for B0 shim hardware and evaluation criteria for the primary outcome of adequate B0 shimming for MRS and MRSI, such as the water spectroscopic linewidth.
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Affiliation(s)
- Christoph Juchem
- Departments of Biomedical Engineering and Radiology, Columbia University, New York, New York
| | - Cristina Cudalbu
- Centre d'Imagerie Biomedicale (CIBM), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Robin A de Graaf
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut
| | - Rolf Gruetter
- Laboratory for Functional and Metabolic Imaging, Center for Biomedical Imaging, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Anke Henning
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
| | | | - Vincent O Boer
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
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17
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Woitek R, Gallagher FA. The use of hyperpolarised 13C-MRI in clinical body imaging to probe cancer metabolism. Br J Cancer 2021; 124:1187-1198. [PMID: 33504974 PMCID: PMC8007617 DOI: 10.1038/s41416-020-01224-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Revised: 11/19/2020] [Accepted: 12/02/2020] [Indexed: 01/30/2023] Open
Abstract
Metabolic reprogramming is one of the hallmarks of cancer and includes the Warburg effect, which is exhibited by many tumours. This can be exploited by positron emission tomography (PET) as part of routine clinical cancer imaging. However, an emerging and alternative method to detect altered metabolism is carbon-13 magnetic resonance imaging (MRI) following injection of hyperpolarised [1-13C]pyruvate. The technique increases the signal-to-noise ratio for the detection of hyperpolarised 13C-labelled metabolites by several orders of magnitude and facilitates the dynamic, noninvasive imaging of the exchange of 13C-pyruvate to 13C-lactate over time. The method has produced promising preclinical results in the area of oncology and is currently being explored in human imaging studies. The first translational studies have demonstrated the safety and feasibility of the technique in patients with prostate, renal, breast and pancreatic cancer, as well as revealing a successful response to treatment in breast and prostate cancer patients at an earlier stage than multiparametric MRI. This review will focus on the strengths of the technique and its applications in the area of oncological body MRI including noninvasive characterisation of disease aggressiveness, mapping of tumour heterogeneity, and early response assessment. A comparison of hyperpolarised 13C-MRI with state-of-the-art multiparametric MRI is likely to reveal the unique additional information and applications offered by the technique.
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Affiliation(s)
- Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge, UK.
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
- Cancer Research UK Cambridge Centre, Cambridge, UK.
| | - Ferdia A Gallagher
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, Cambridge, UK
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18
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Chhetri A, Li X, Rispoli JV. Current and Emerging Magnetic Resonance-Based Techniques for Breast Cancer. Front Med (Lausanne) 2020; 7:175. [PMID: 32478083 PMCID: PMC7235971 DOI: 10.3389/fmed.2020.00175] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 04/15/2020] [Indexed: 01/10/2023] Open
Abstract
Breast cancer is the most commonly diagnosed cancer among women worldwide, and early detection remains a principal factor for improved patient outcomes and reduced mortality. Clinically, magnetic resonance imaging (MRI) techniques are routinely used in determining benign and malignant tumor phenotypes and for monitoring treatment outcomes. Static MRI techniques enable superior structural contrast between adipose and fibroglandular tissues, while dynamic MRI techniques can elucidate functional characteristics of malignant tumors. The preferred clinical procedure-dynamic contrast-enhanced MRI-illuminates the hypervascularity of breast tumors through a gadolinium-based contrast agent; however, accumulation of the potentially toxic contrast agent remains a major limitation of the technique, propelling MRI research toward finding an alternative, noninvasive method. Three such techniques are magnetic resonance spectroscopy, chemical exchange saturation transfer, and non-contrast diffusion weighted imaging. These methods shed light on underlying chemical composition, provide snapshots of tissue metabolism, and more pronouncedly characterize microstructural heterogeneity. This review article outlines the present state of clinical MRI for breast cancer and examines several research techniques that demonstrate capacity for clinical translation. Ultimately, multi-parametric MRI-incorporating one or more of these emerging methods-presently holds the best potential to afford improved specificity and deliver excellent accuracy to clinics for the prediction, detection, and monitoring of breast cancer.
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Affiliation(s)
- Apekshya Chhetri
- Magnetic Resonance Biomedical Engineering Laboratory, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
- Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States
| | - Xin Li
- Magnetic Resonance Biomedical Engineering Laboratory, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - Joseph V. Rispoli
- Magnetic Resonance Biomedical Engineering Laboratory, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
- Center for Cancer Research, Purdue University, West Lafayette, IN, United States
- School of Electrical & Computer Engineering, Purdue University, West Lafayette, IN, United States
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19
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Reig B, Heacock L, Lewin A, Cho N, Moy L. Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer. J Magn Reson Imaging 2020; 52. [DOI: 10.1002/jmri.27145] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 03/01/2020] [Accepted: 03/02/2020] [Indexed: 12/25/2022] Open
Affiliation(s)
- Beatriu Reig
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
| | - Laura Heacock
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
| | - Alana Lewin
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
| | - Nariya Cho
- Department of Radiology Seoul National University Hospital Seoul Republic of Korea
- Department of Radiology Seoul National University College of Medicine Seoul Republic of Korea
| | - Linda Moy
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
- Bernard and Irene Schwartz Center for Biomedical Imaging Department of Radiology, New York University Grossman School of Medicine New York New York USA
- Center for Advanced Imaging Innovation and Research (CAI2 R) New York University Grossman School of Medicine New York New York USA
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20
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The additive role of 1H-magnetic resonance spectroscopic imaging to ensure pathological complete response after neoadjuvant chemotherapy in breast cancer patients. Pol J Radiol 2020; 84:e570-e580. [PMID: 32082456 PMCID: PMC7016493 DOI: 10.5114/pjr.2019.92282] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 11/12/2019] [Indexed: 12/27/2022] Open
Abstract
Purpose To assess the role of 1H-magnetic resonance spectroscopy (1H-MRS) in the confirmation of pathological complete response after neoadjuvant chemotherapy in breast cancer. Material and methods Forty-seven cases (53.72 ± 8.53 years) were evaluated using magnetic resonance imaging (MRI) and 1H-MRS with choline (Cho) signal-to-noise ratio (SNR) measured followed by histopathology and ROC analyses. Results Twelve patients had complete response, and 35 patients had residual disease. Mean age was 53.72 ± 8.53 years. The mean tumour size before neoadjuvant chemotherapy (NAC) was 4.21 ± 0.99 cm and after NAC was 0.9 ± 0.44 cm.Positive total choline signal (tCho) was detected in all cases. The mean Cho SNR before NAC was 9.53 ± 1.7 and after NAC was 2.53 ± 1.3. The Cho SNR cut-off point differentiating between pathologic complete response (pCR) and the non pCR was 1.95. Dynamic MRI showed 83.3% sensitivity, 65.7% specificity, 45.5% positive predictive value, 92.0% negative predictive value, and 70.2% diagnostic accuracy. Combined evaluation done by using the dynamic MRI and 1H-MRS showed 91.5% diagnostic accuracy with 75.0% sensitivity, 97.1% specificity, 75% positive predictive value, and 91.9% negative predictive value. ROC curves of Cho SNR showed statistically significant differences between non pCR and pCR with AUC was 0.955, 82.9% sensitivity, 91.7% specificity, 96.7% positive predictive value, 64.7% negative predictive value, and 85.11% diagnostic accuracy. Conclusions 1H-MRS improves the diagnostic accuracy in the prediction of the pCR after NAC.
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21
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Gallagher FA, Woitek R, McLean MA, Gill AB, Manzano Garcia R, Provenzano E, Riemer F, Kaggie J, Chhabra A, Ursprung S, Grist JT, Daniels CJ, Zaccagna F, Laurent MC, Locke M, Hilborne S, Frary A, Torheim T, Boursnell C, Schiller A, Patterson I, Slough R, Carmo B, Kane J, Biggs H, Harrison E, Deen SS, Patterson A, Lanz T, Kingsbury Z, Ross M, Basu B, Baird R, Lomas DJ, Sala E, Wason J, Rueda OM, Chin SF, Wilkinson IB, Graves MJ, Abraham JE, Gilbert FJ, Caldas C, Brindle KM. Imaging breast cancer using hyperpolarized carbon-13 MRI. Proc Natl Acad Sci U S A 2020; 117:2092-2098. [PMID: 31964840 PMCID: PMC6995024 DOI: 10.1073/pnas.1913841117] [Citation(s) in RCA: 111] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Our purpose is to investigate the feasibility of imaging tumor metabolism in breast cancer patients using 13C magnetic resonance spectroscopic imaging (MRSI) of hyperpolarized 13C label exchange between injected [1-13C]pyruvate and the endogenous tumor lactate pool. Treatment-naïve breast cancer patients were recruited: four triple-negative grade 3 cancers; two invasive ductal carcinomas that were estrogen and progesterone receptor-positive (ER/PR+) and HER2/neu-negative (HER2-), one grade 2 and one grade 3; and one grade 2 ER/PR+ HER2- invasive lobular carcinoma (ILC). Dynamic 13C MRSI was performed following injection of hyperpolarized [1-13C]pyruvate. Expression of lactate dehydrogenase A (LDHA), which catalyzes 13C label exchange between pyruvate and lactate, hypoxia-inducible factor-1 (HIF1α), and the monocarboxylate transporters MCT1 and MCT4 were quantified using immunohistochemistry and RNA sequencing. We have demonstrated the feasibility and safety of hyperpolarized 13C MRI in early breast cancer. Both intertumoral and intratumoral heterogeneity of the hyperpolarized pyruvate and lactate signals were observed. The lactate-to-pyruvate signal ratio (LAC/PYR) ranged from 0.021 to 0.473 across the tumor subtypes (mean ± SD: 0.145 ± 0.164), and a lactate signal was observed in all of the grade 3 tumors. The LAC/PYR was significantly correlated with tumor volume (R = 0.903, P = 0.005) and MCT 1 (R = 0.85, P = 0.032) and HIF1α expression (R = 0.83, P = 0.043). Imaging of hyperpolarized [1-13C]pyruvate metabolism in breast cancer is feasible and demonstrated significant intertumoral and intratumoral metabolic heterogeneity, where lactate labeling correlated with MCT1 expression and hypoxia.
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Affiliation(s)
- Ferdia A Gallagher
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom;
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Mary A McLean
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Andrew B Gill
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Raquel Manzano Garcia
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Elena Provenzano
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cambridge Breast Cancer Research Unit, Addenbrooke's Hospital, Cambridge University Hospital National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
- Department of Histopathology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Frank Riemer
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Joshua Kaggie
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Anita Chhabra
- Pharmacy Department, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, United Kingdom
| | - Stephan Ursprung
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - James T Grist
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Charlie J Daniels
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Fulvio Zaccagna
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | | | - Matthew Locke
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Sarah Hilborne
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Amy Frary
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Turid Torheim
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Chris Boursnell
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Amy Schiller
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Ilse Patterson
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Rhys Slough
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Bruno Carmo
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Justine Kane
- Cambridge Breast Cancer Research Unit, Addenbrooke's Hospital, Cambridge University Hospital National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Heather Biggs
- Cambridge Breast Cancer Research Unit, Addenbrooke's Hospital, Cambridge University Hospital National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Emma Harrison
- Cambridge Breast Cancer Research Unit, Addenbrooke's Hospital, Cambridge University Hospital National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Surrin S Deen
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Andrew Patterson
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Titus Lanz
- RAPID Biomedical GmbH, 97222 Rimpar, Germany
| | - Zoya Kingsbury
- Medical Genomics Research, Illumina, Great Abington, Cambridge CB21 6DF, United Kingdom
| | - Mark Ross
- Medical Genomics Research, Illumina, Great Abington, Cambridge CB21 6DF, United Kingdom
| | - Bristi Basu
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Richard Baird
- Cambridge Breast Cancer Research Unit, Addenbrooke's Hospital, Cambridge University Hospital National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - David J Lomas
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - James Wason
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Institute of Health and Society, Newcastle University, Newcastle-upon-Tyne NE2 4AX, United Kingdom
| | - Oscar M Rueda
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Suet-Feung Chin
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Ian B Wilkinson
- Department of Experimental Medicine and Immunotherapeutics, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Martin J Graves
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Jean E Abraham
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cambridge Breast Cancer Research Unit, Addenbrooke's Hospital, Cambridge University Hospital National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Carlos Caldas
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cambridge Breast Cancer Research Unit, Addenbrooke's Hospital, Cambridge University Hospital National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Kevin M Brindle
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Department of Biochemistry, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
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22
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Window of opportunity clinical trial designs to study cancer metabolism. Br J Cancer 2019; 122:45-51. [PMID: 31819180 PMCID: PMC6964681 DOI: 10.1038/s41416-019-0621-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Revised: 10/16/2019] [Accepted: 10/18/2019] [Indexed: 02/06/2023] Open
Abstract
Window of opportunity trials exploit the ‘window’ of time after cancer diagnosis, typically prior to initiation of cancer therapy. In recent years this study design has become a more regular feature of drug development, as this ‘window’ provides an opportunity to carry out a thorough pharmacodynamic assessment of a therapy of interest in tumours that are unperturbed by prior treatment. Many of the first window trials interrogated the bioactivity of drugs being repurposed for cancer treatment, in particular the anti-mitochondrial agent, metformin. In this review, we describe examples of window study designs that have been used to assess drugs that target cancer metabolism with a focus on metformin. In addition, we discuss how window studies may aid the development of molecular metabolic cancer imaging.
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Bhardwaj V, Kaushik A, Khatib ZM, Nair M, McGoron AJ. Recalcitrant Issues and New Frontiers in Nano-Pharmacology. Front Pharmacol 2019; 10:1369. [PMID: 31849645 PMCID: PMC6897283 DOI: 10.3389/fphar.2019.01369] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 10/29/2019] [Indexed: 12/13/2022] Open
Abstract
Packaging of old pharma drugs into new packaging "nanoparticles" is called nano-pharmacology and the products are called nano-based drugs. The inception of nano-pharmacology research and development (R&D) is marked by the approval of the first nano-based drug Doxil® in 1995 by the Food and Drug Administration. However, even after more than two decades, today, there are only ∼20 nano-based drugs in the market to treat cancers and brain diseases. In this article we share the perspectives of nanotechnology scientists, engineers, and clinicians on the roadblocks in nano-pharmacology R&D. Also, we share our opinion on new frontiers in the field of nano-pharmacology R&D that may allow rapid and efficient transfer of nano-pharma technologies from R&D to market.
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Affiliation(s)
- Vinay Bhardwaj
- Department of Biomedical Engineering, The College of New Jersey, Ewing, NJ, United States
| | - Ajeet Kaushik
- Department of Natural Sciences, Florida Polytechnic University, Lakeland, FL, United States
| | - Ziad M. Khatib
- Division of Hematology Oncology, Department of Pediatrics, Nicklaus Children’s Hospital, Miami, FL, United States
| | - Madhavan Nair
- Center for Personalized Nanomedicine, Herbert Wertheim College of Medicine, Florida International University, Miami, FL, United States
| | - Anthony J. McGoron
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States
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25
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Greenwood HI, Wilmes LJ, Kelil T, Joe BN. Role of Breast MRI in the Evaluation and Detection of DCIS: Opportunities and Challenges. J Magn Reson Imaging 2019; 52:697-709. [PMID: 31746088 DOI: 10.1002/jmri.26985] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 10/15/2019] [Accepted: 10/16/2019] [Indexed: 12/29/2022] Open
Abstract
Historically, breast magnetic resonance imaging (MRI) was not considered an effective modality in the evaluation of ductal carcinoma in situ (DCIS). Over the past decade this has changed, with studies demonstrating that MRI is the most sensitive imaging tool for detection of all grades of DCIS. It has been suggested that not only is breast MRI the most sensitive imaging tool for detection but it may also detect the most clinically relevant DCIS lesions. The role and outcomes of MRI in the preoperative setting for patients with DCIS remains controversial; however, several studies have shown benefit in the preoperative evaluation of extent of disease as well as predicting an underlying invasive component. The most common presentation of DCIS on MRI is nonmass enhancement (NME) in a linear or segmental distribution pattern. Maximizing breast MRI spatial resolution is therefore beneficial, given the frequent presentation of DCIS as NME on MRI. Emerging MRI techniques, such as diffusion-weighted imaging (DWI), have shown promising potential to discriminate DCIS from benign and invasive lesions. Future opportunities including advanced imaging visual techniques, radiomics/radiogenomics, and machine learning / artificial intelligence may also be applicable to the detection and treatment of DCIS. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:697-709.
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Affiliation(s)
- Heather I Greenwood
- University of California San Francisco, Department of Radiology and Biomedical Imaging, San Francisco, California, USA
| | - Lisa J Wilmes
- University of California San Francisco, Department of Radiology and Biomedical Imaging, San Francisco, California, USA
| | - Tatiana Kelil
- University of California San Francisco, Department of Radiology and Biomedical Imaging, San Francisco, California, USA
| | - Bonnie N Joe
- University of California San Francisco, Department of Radiology and Biomedical Imaging, San Francisco, California, USA
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Arlauckas SP, Browning EA, Poptani H, Delikatny EJ. Imaging of cancer lipid metabolism in response to therapy. NMR IN BIOMEDICINE 2019; 32:e4070. [PMID: 31107583 DOI: 10.1002/nbm.4070] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Revised: 12/21/2018] [Accepted: 12/21/2018] [Indexed: 06/09/2023]
Abstract
Lipids represent a diverse array of molecules essential to the cell's structure, defense, energy, and communication. Lipid metabolism can often become dysregulated during tumor development. During cancer therapy, targeted inhibition of cell proliferation can likewise cause widespread and drastic changes in lipid composition. Molecular imaging techniques have been developed to monitor altered lipid profiles as a biomarker for cancer diagnosis and treatment response. For decades, MRS has been the dominant non-invasive technique for studying lipid metabolite levels. Recent insights into the oncogenic transformations driving changes in lipid metabolism have revealed new mechanisms and signaling molecules that can be exploited using optical imaging, mass spectrometry imaging, and positron emission tomography. These novel imaging modalities have provided researchers with a diverse toolbox to examine changes in lipids in response to a wide array of anticancer strategies including chemotherapy, radiation therapy, signal transduction inhibitors, gene therapy, immunotherapy, or a combination of these strategies. The understanding of lipid metabolism in response to cancer therapy continues to evolve as each therapeutic method emerges, and this review seeks to summarize the current field and areas of unmet needs.
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Affiliation(s)
- Sean Philip Arlauckas
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Systems Biology, Mass General Hospital, Boston, MA, USA
| | - Elizabeth Anne Browning
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Harish Poptani
- Department of Cellular and Molecular Physiology, Institute of Regenerative Medicine, University of Liverpool, Liverpool, UK
| | - Edward James Delikatny
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Sonkar K, Ayyappan V, Tressler CM, Adelaja O, Cai R, Cheng M, Glunde K. Focus on the glycerophosphocholine pathway in choline phospholipid metabolism of cancer. NMR IN BIOMEDICINE 2019; 32:e4112. [PMID: 31184789 PMCID: PMC6803034 DOI: 10.1002/nbm.4112] [Citation(s) in RCA: 91] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 04/16/2019] [Accepted: 04/20/2019] [Indexed: 05/02/2023]
Abstract
Activated choline metabolism is a hallmark of carcinogenesis and tumor progression, which leads to elevated levels of phosphocholine and glycerophosphocholine in all types of cancer tested so far. Magnetic resonance spectroscopy applications have played a key role in detecting these elevated choline phospholipid metabolites. To date, the majority of cancer-related studies have focused on phosphocholine and the Kennedy pathway, which constitutes the biosynthesis pathway for membrane phosphatidylcholine. Fewer and more recent studies have reported on the importance of glycerophosphocholine in cancer. In this review article, we summarize the recent literature on glycerophosphocholine metabolism with respect to its cancer biology and its detection by magnetic resonance spectroscopy applications.
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Affiliation(s)
- Kanchan Sonkar
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Vinay Ayyappan
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Caitlin M. Tressler
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Oluwatobi Adelaja
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Ruoqing Cai
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Menglin Cheng
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kristine Glunde
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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28
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Jagannathan NR. Application of in vivo MR methods in the study of breast cancer metabolism. NMR IN BIOMEDICINE 2019; 32:e4032. [PMID: 30456917 DOI: 10.1002/nbm.4032] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 08/25/2018] [Accepted: 09/26/2018] [Indexed: 06/09/2023]
Abstract
In the last two decades, various in vivo MR methodologies have been evaluated for their potential in the study of cancer metabolism. During malignant transformation, metabolic alterations occur, leading to morphological and functional changes. Among various MR methods, in vivo MRS has been extensively used in breast cancer to study the metabolism of cells, tissues or whole organs. It provides biochemical information at the metabolite level. Altered choline, phospholipid and energy metabolism has been documented using proton (1 H), phosphorus (31 P) and carbon (13 C) isotopes. Increased levels of choline-containing compounds, phosphomonoesters and phosphodiesters in breast cancer, which are indicative of altered choline and phospholipid metabolism, have been reported using in vivo, in vitro and ex vivo NMR studies. These changes are reversed on successful therapy, which depends on the treatment regimen given. Monitoring the various tumor intermediary metabolic pathways using nuclear spin hyperpolarization of 13 C-labeled substrates by dynamic nuclear polarization has also been recently reported. Furthermore, the utility of various methods such as diffusion, dynamic contrast and perfusion MRI have also been evaluated to study breast tumor metabolism. Parameters such as tumor volume, apparent diffusion coefficient, volume transfer coefficient and extracellular volume ratio are estimated. These parameters provide information on the changes in tumor microstructure, microenvironment, abnormal vasculature, permeability and grade of the tumor. Such changes seen during cancer progression are due to alterations in the tumor metabolism, leading to changes in cell architecture. Due to architectural changes, the tissue mechanical properties are altered; this can be studied using magnetic resonance elastography, which measures the elastic properties of tissues. Moreover, these structural MRI methods can be used to investigate the effect of therapy-induced changes in tumor characteristics. This review discusses the potential of various in vivo MR methodologies in the study of breast cancer metabolism.
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Fardanesh R, Marino MA, Avendano D, Leithner D, Pinker K, Thakur SB. Proton MR spectroscopy in the breast: Technical innovations and clinical applications. J Magn Reson Imaging 2019; 50:1033-1046. [PMID: 30848037 DOI: 10.1002/jmri.26700] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 02/20/2019] [Indexed: 01/27/2023] Open
Abstract
Proton magnetic resonance spectroscopy (MRS) is a promising noninvasive diagnostic technique for investigation of breast cancer metabolism. Spectroscopic imaging data may be obtained following contrast-enhanced MRI by applying the point-resolved spectroscopy sequence (PRESS) or the stimulated echo acquisition mode (STEAM) sequence from the MR voxel encompassing the breast lesion. Total choline signal (tCho) measured in vivo using either a qualitative or quantitative approach has been used as a diagnostic test in the workup of malignant breast lesions. In addition to tCho metabolites, other relevant metabolites, including multiple lipids, can be detected and monitored. MRS has been heavily investigated as an adjunct to morphologic and dynamic MRI to improve diagnostic accuracy in breast cancer, obviating unnecessary benign biopsies. Besides its use in the staging of breast cancer, other promising applications have been recently investigated, including the assessment of treatment response and therapy monitoring. This review provides guidance on spectroscopic acquisition and quantification methods and highlights current and evolving clinical applications of proton MRS. Level of Evidence 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019.
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Affiliation(s)
- Reza Fardanesh
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Maria Adele Marino
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Biomedical Sciences and Morphologic and Functional Imaging, Policlinico Universitario G. Martino, University of Messina, Italy
| | - Daly Avendano
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Doris Leithner
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - Sunitha B Thakur
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Ter Voert EEGW, Heijmen L, van Asten JJA, Wright AJ, Nagtegaal ID, Punt CJA, de Wilt JHW, van Laarhoven HWM, Heerschap A. Levels of choline-containing compounds in normal liver and liver metastases of colorectal cancer as recorded by 1 H MRS. NMR IN BIOMEDICINE 2019; 32:e4035. [PMID: 30457686 DOI: 10.1002/nbm.4035] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2018] [Revised: 09/07/2018] [Accepted: 09/28/2018] [Indexed: 06/09/2023]
Abstract
PURPOSE A relatively high signal for choline-containing compounds (total choline, tCho) is commonly found in 1 H MR spectra of malignant tumors, but it is unclear if this also occurs in tumors in the liver. We evaluated the potential of the tCho signal in single voxel 1 H MR spectra of the human liver to assess metastases of colorectal cancers. EXPERIMENT MR spectra of an 8 cm3 PRESS-localized voxel were obtained at 3 T from the livers of 12 healthy volunteers and from metastatic lesions in 20 patients in two different sessions. To correct for motion artifacts, sequentially recorded spectra were individually phased and frequency aligned before averaging. Spectra were analyzed using LCModel and tissue levels estimated by water referencing. Repeatability was assessed with Bland-Altman analyses. To estimate tumor necrosis, diffusion-weighted imaging of the liver was performed. High resolution magic angle spinning (HRMAS) spectra of tumor and normal liver samples were obtained at 11.7 T. RESULTS With increasing tumor volumes, tCho levels decreased, indicating a partial volume effect. Mean tCho content in tumors larger than the PRESS voxel (>8 cm3 ) was significantly lower (p < 0.01) than for normal liver: 1.6 (range 0.0-3.4) versus 6.9 (range 4.9-11.1) mmol/kg wet weight, while it was comparable for tumors smaller than 8 cm3 : 7.0 (range 3.8-9.3) mmol/kg. The higher 90th percentile apparent diffusion coefficient value in the larger lesions indicates more necrosis. Measurement repeatability was average in normal livers and poor in tumors. HRMAS did not show substantial differences in choline-containing compounds between normal liver and metastasis. CONCLUSION An increased tCho content was not observed in 1 H MR spectra of liver metastasis of colorectal cancer, compared with normal liver. This may be due to the background of a high tCho signal in spectra of normal liver or to an intrinsic lower tCho content in these tumors, but is most likely the result of necrosis in metastatic tumor tissue.
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Affiliation(s)
- Edwin E G W Ter Voert
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Linda Heijmen
- Department of Medical Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jack J A van Asten
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Alan J Wright
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Iris D Nagtegaal
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Cornelis J A Punt
- Department of Medical Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Medical Oncology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Johannes H W de Wilt
- Department of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Hanneke W M van Laarhoven
- Department of Medical Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Medical Oncology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Arend Heerschap
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
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Abstract
Magnetic resonance spectroscopy (MRS) can be performed in vivo using commercial MRI systems to obtain biochemical information about tissues and cancers. Applications in brain, prostate and breast aid lesion detection and characterisation (differential diagnosis), treatment planning and response assessment. Multi-centre clinical trials have been performed in all these tissues. Single centre studies have been performed in many other tissues including cervix, uterus, musculoskeletal and liver. While generally MRS is used to study endogenous metabolites it has also been used in drug studies, for example those that include 19F as part of their structure. Recently the hyperpolarisation of compounds enriched with 13C such as [1-13C] pyruvate has been demonstrated in animal models and now in preliminary clinical studies, permitting the monitoring of biochemical processes with unprecedented sensitivity. This review briefly introduces the underlying methods and then discusses the current status of these applications.
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Affiliation(s)
- Geoffrey S Payne
- University Hospitals Southampton NHS Foundation Trust, Tremona Road, Southampton SO16 6YD, United Kingdom
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32
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Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:7417126. [PMID: 30344618 PMCID: PMC6174735 DOI: 10.1155/2018/7417126] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 07/24/2018] [Accepted: 09/04/2018] [Indexed: 01/17/2023]
Abstract
Over the years, MR systems have evolved from imaging modalities to advanced computational systems producing a variety of numerical parameters that can be used for the noninvasive preoperative assessment of breast pathology. Furthermore, the combination with state-of-the-art image analysis methods provides a plethora of quantifiable imaging features, termed radiomics that increases diagnostic accuracy towards individualized therapy planning. More importantly, radiomics can now be complemented by the emerging deep learning techniques for further process automation and correlation with other clinical data which facilitate the monitoring of treatment response, as well as the prediction of patient's outcome, by means of unravelling of the complex underlying pathophysiological mechanisms which are reflected in tissue phenotype. The scope of this review is to provide applications and limitations of radiomics towards the development of clinical decision support systems for breast cancer diagnosis and prognosis.
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Sharma U, Agarwal K, Sah RG, Parshad R, Seenu V, Mathur S, Gupta SD, Jagannathan NR. Can Multi-Parametric MR Based Approach Improve the Predictive Value of Pathological and Clinical Therapeutic Response in Breast Cancer Patients? Front Oncol 2018; 8:319. [PMID: 30159254 PMCID: PMC6104482 DOI: 10.3389/fonc.2018.00319] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 07/26/2018] [Indexed: 11/13/2022] Open
Abstract
The potential of total choline (tCho), apparent diffusion coefficient (ADC) and tumor volume, both individually and in combination of all these three parameters (multi-parametric approach), was evaluated in predicting both pathological and clinical responses in 42 patients with locally advanced breast cancer (LABC) enrolled for neoadjuvant chemotherapy (NACT). Patients were sequentially examined by conventional MRI; diffusion weighted imaging and in vivo proton MR spectroscopy at 4 time points (pre-therapy, after I, II, and III NACT) at 1.5 T. Miller Payne grading system was used for pathological assessment of response. Of the 42 patients, 24 were pathological responders (pR) while 18 were pathological non-responders (pNR). Clinical response determination classified 26 patients as responders (cR) while 16 as non-responders (cNR). tCho and ADC showed significant changes after I NACT, however, MR measured tumor volume showed reduction only after II NACT both in pR and cR. After III NACT, the sensitivity to detect responders was highest for MR volume (83.3% for pR and 96.2% for cR) while the specificity was highest for ADC (76.5% for pR and 100% for cR). Combination of all three parameters exhibited lower sensitivity (66.7%) than MR volume for pR prediction, however, a moderate improvement was seen in specificity (58.8%). For the prediction of clinical response, multi-parametric approach showed 84.6% sensitivity with 100% specificity compared to MR volume (sensitivity 96.2%; specificity 80%). Kappa statistics demonstrated substantial agreement of clinical response with MR volume (k = 0.78) and with multi-parametric approach (k = 0.80) while moderate agreement was seen for tCho (k = 0.48) and ADC (k = 0.46). The values of k for tCho, MR volume and ADC were 0.31, 0.38, and 0.18 indicating fair, moderate, and slight agreement, respectively with pathological response. Moderate agreement (k = 0.44) was observed between clinical and pathological responses. Our study demonstrated that both tCho and ADC are strong predictors of assessment of early pathological and clinical responses. Multi-parametric approach yielded 100% specificity in predicting clinical response. Following III NACT, MR volume emerged as highly suitable predictor for both clinical and pathological assessments. PCA demonstrated separate clusters of pR vs. pNR and cR vs. cNR at post-therapy while with some overlap at pre-therapy.
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Affiliation(s)
- Uma Sharma
- Department of NMR and MRI Facility, All India Institute of Medical Sciences, New Delhi, India
| | - Khushbu Agarwal
- Department of NMR and MRI Facility, All India Institute of Medical Sciences, New Delhi, India
| | - Rani G Sah
- Department of NMR and MRI Facility, All India Institute of Medical Sciences, New Delhi, India
| | - Rajinder Parshad
- Department of Surgical Disciplines, All India Institute of Medical Sciences, New Delhi, India
| | - Vurthaluru Seenu
- Department of Surgical Disciplines, All India Institute of Medical Sciences, New Delhi, India
| | - Sandeep Mathur
- Department of Pathology, All India Institute of Medical Sciences, New Delhi, India
| | - Siddhartha D Gupta
- Department of Pathology, All India Institute of Medical Sciences, New Delhi, India
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Fowler AM, Mankoff DA, Joe BN. Imaging Neoadjuvant Therapy Response in Breast Cancer. Radiology 2017; 285:358-375. [DOI: 10.1148/radiol.2017170180] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Amy M. Fowler
- From the Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252 (A.M.F.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (D.A.M.); and Department of Radiology and Biomedical Imaging, University of California–San Francisco School of Medicine, San Francisco, Calif (B.N.J.)
| | - David A. Mankoff
- From the Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252 (A.M.F.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (D.A.M.); and Department of Radiology and Biomedical Imaging, University of California–San Francisco School of Medicine, San Francisco, Calif (B.N.J.)
| | - Bonnie N. Joe
- From the Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252 (A.M.F.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (D.A.M.); and Department of Radiology and Biomedical Imaging, University of California–San Francisco School of Medicine, San Francisco, Calif (B.N.J.)
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Mikkelsen M, Barker PB, Bhattacharyya PK, Brix MK, Buur PF, Cecil KM, Chan KL, Chen DYT, Craven AR, Cuypers K, Dacko M, Duncan NW, Dydak U, Edmondson DA, Ende G, Ersland L, Gao F, Greenhouse I, Harris AD, He N, Heba S, Hoggard N, Hsu TW, Jansen JFA, Kangarlu A, Lange T, Lebel RM, Li Y, Lin CYE, Liou JK, Lirng JF, Liu F, Ma R, Maes C, Moreno-Ortega M, Murray SO, Noah S, Noeske R, Noseworthy MD, Oeltzschner G, Prisciandaro JJ, Puts NAJ, Roberts TPL, Sack M, Sailasuta N, Saleh MG, Schallmo MP, Simard N, Swinnen SP, Tegenthoff M, Truong P, Wang G, Wilkinson ID, Wittsack HJ, Xu H, Yan F, Zhang C, Zipunnikov V, Zöllner HJ, Edden RAE. Big GABA: Edited MR spectroscopy at 24 research sites. Neuroimage 2017; 159:32-45. [PMID: 28716717 PMCID: PMC5700835 DOI: 10.1016/j.neuroimage.2017.07.021] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Revised: 06/20/2017] [Accepted: 07/11/2017] [Indexed: 12/14/2022] Open
Abstract
Magnetic resonance spectroscopy (MRS) is the only biomedical imaging method that can noninvasively detect endogenous signals from the neurotransmitter γ-aminobutyric acid (GABA) in the human brain. Its increasing popularity has been aided by improvements in scanner hardware and acquisition methodology, as well as by broader access to pulse sequences that can selectively detect GABA, in particular J-difference spectral editing sequences. Nevertheless, implementations of GABA-edited MRS remain diverse across research sites, making comparisons between studies challenging. This large-scale multi-vendor, multi-site study seeks to better understand the factors that impact measurement outcomes of GABA-edited MRS. An international consortium of 24 research sites was formed. Data from 272 healthy adults were acquired on scanners from the three major MRI vendors and analyzed using the Gannet processing pipeline. MRS data were acquired in the medial parietal lobe with standard GABA+ and macromolecule- (MM-) suppressed GABA editing. The coefficient of variation across the entire cohort was 12% for GABA+ measurements and 28% for MM-suppressed GABA measurements. A multilevel analysis revealed that most of the variance (72%) in the GABA+ data was accounted for by differences between participants within-site, while site-level differences accounted for comparatively more variance (20%) than vendor-level differences (8%). For MM-suppressed GABA data, the variance was distributed equally between site- (50%) and participant-level (50%) differences. The findings show that GABA+ measurements exhibit strong agreement when implemented with a standard protocol. There is, however, increased variability for MM-suppressed GABA measurements that is attributed in part to differences in site-to-site data acquisition. This study's protocol establishes a framework for future methodological standardization of GABA-edited MRS, while the results provide valuable benchmarks for the MRS community.
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Affiliation(s)
- Mark Mikkelsen
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Peter B Barker
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Pallab K Bhattacharyya
- Imaging Institute, Cleveland Clinic Foundation, Cleveland, OH, USA; Radiology, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
| | - Maiken K Brix
- Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Pieter F Buur
- Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands
| | - Kim M Cecil
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kimberly L Chan
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David Y-T Chen
- Department of Radiology, Taipei Medical University Shuang Ho Hospital, New Taipei City, Taiwan
| | - Alexander R Craven
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; NORMENT - Norwegian Center for Mental Disorders Research, University of Bergen, Bergen, Norway
| | - Koen Cuypers
- Department of Kinesiology, KU Leuven, Leuven, Belgium; REVAL Rehabilitation Research Center, Hasselt University, Diepenbeek, Belgium
| | - Michael Dacko
- Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany
| | - Niall W Duncan
- Brain and Consciousness Research Centre, Taipei Medical University, Taipei, Taiwan
| | - Ulrike Dydak
- School of Health Sciences, Purdue University, West Lafayette, IN, USA
| | - David A Edmondson
- School of Health Sciences, Purdue University, West Lafayette, IN, USA
| | - Gabriele Ende
- Department of Neuroimaging, Central Institute of Mental Health, Mannheim, Germany
| | - Lars Ersland
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; NORMENT - Norwegian Center for Mental Disorders Research, University of Bergen, Bergen, Norway; Department of Clinical Engineering, Haukeland University Hospital, Bergen, Norway
| | - Fei Gao
- Shandong Medical Imaging Research Institute, Shandong University, Jinan, China
| | - Ian Greenhouse
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Ashley D Harris
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Stefanie Heba
- Department of Neurology, BG University Hospital Bergmannsheil, Bochum, Germany
| | - Nigel Hoggard
- Academic Unit of Radiology, University of Sheffield, Sheffield, UK
| | - Tun-Wei Hsu
- Department of Radiology, Taipei Veterans General Hospital, National Yang-Ming University School of Medicine, Taipei, Taiwan
| | - Jacobus F A Jansen
- Department of Radiology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Alayar Kangarlu
- Department of Psychiatry, Columbia University, New York, NY, USA; New York State Psychiatric Institute, New York, NY, USA
| | - Thomas Lange
- Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany
| | | | - Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | - Jy-Kang Liou
- Department of Radiology, Taipei Veterans General Hospital, National Yang-Ming University School of Medicine, Taipei, Taiwan
| | - Jiing-Feng Lirng
- Department of Radiology, Taipei Veterans General Hospital, National Yang-Ming University School of Medicine, Taipei, Taiwan
| | - Feng Liu
- New York State Psychiatric Institute, New York, NY, USA
| | - Ruoyun Ma
- School of Health Sciences, Purdue University, West Lafayette, IN, USA
| | - Celine Maes
- Department of Kinesiology, KU Leuven, Leuven, Belgium
| | | | - Scott O Murray
- Department of Psychology, University of Washington, Seattle, WA, USA
| | - Sean Noah
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | | | - Michael D Noseworthy
- Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - James J Prisciandaro
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Nicolaas A J Puts
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Timothy P L Roberts
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Markus Sack
- Department of Neuroimaging, Central Institute of Mental Health, Mannheim, Germany
| | - Napapon Sailasuta
- Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Muhammad G Saleh
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | | | - Nicholas Simard
- School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada
| | - Stephan P Swinnen
- Department of Kinesiology, KU Leuven, Leuven, Belgium; Leuven Research Institute for Neuroscience & Disease (LIND), KU Leuven, Leuven, Belgium
| | - Martin Tegenthoff
- Department of Neurology, BG University Hospital Bergmannsheil, Bochum, Germany
| | - Peter Truong
- Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Guangbin Wang
- Shandong Medical Imaging Research Institute, Shandong University, Jinan, China
| | - Iain D Wilkinson
- Academic Unit of Radiology, University of Sheffield, Sheffield, UK
| | - Hans-Jörg Wittsack
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine-University, Duesseldorf, Germany
| | - Hongmin Xu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chencheng Zhang
- Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Vadim Zipunnikov
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Helge J Zöllner
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine-University, Duesseldorf, Germany; Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University, Duesseldorf, Germany
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
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Cheng M, Rizwan A, Jiang L, Bhujwalla ZM, Glunde K. Molecular Effects of Doxorubicin on Choline Metabolism in Breast Cancer. Neoplasia 2017; 19:617-627. [PMID: 28654865 PMCID: PMC5487306 DOI: 10.1016/j.neo.2017.05.004] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 05/15/2017] [Accepted: 05/22/2017] [Indexed: 12/16/2022]
Abstract
Abnormal choline phospholipid metabolism is a hallmark of cancer. The magnetic resonance spectroscopy (MRS) detected total choline (tCho) signal can serve as an early noninvasive imaging biomarker of chemotherapy response in breast cancer. We have quantified the individual components of the tCho signal, glycerophosphocholine (GPC), phosphocholine (PC) and free choline (Cho), before and after treatment with the commonly used chemotherapeutic drug doxorubicin in weakly metastatic human MCF7 and triple-negative human MDA-MB-231 breast cancer cells. While the tCho concentration did not change following doxorubicin treatment, GPC significantly increased and PC decreased. Of the two phosphatidylcholine-specific PLD enzymes, only PLD1, but not PLD2, mRNA was down-regulated by doxorubicin treatment. For the two reported genes encoding GPC phosphodiesterase, the mRNA of GDPD6, but not GDPD5, decreased following doxorubicin treatment. mRNA levels of choline kinase α (ChKα), which converts Cho to PC, were reduced following doxorubicin treatment. PLD1 and ChKα protein levels decreased following doxorubicin treatment in a concentration dependent manner. Treatment with the PLD1 specific inhibitor VU0155069 sensitized MCF7 and MDA-MB-231 breast cancer cells to doxorubicin-induced cytotoxicity. Low concentrations of 100 nM of doxorubicin increased MDA-MB-231 cell migration. GDPD6, but not PLD1 or ChKα, silencing by siRNA abolished doxorubicin-induced breast cancer cell migration. Doxorubicin induced GPC increase and PC decrease are caused by reductions in PLD1, GDPD6, and ChKα mRNA and protein expression. We have shown that silencing or inhibiting these genes/proteins can promote drug effectiveness and reduce adverse drug effects. Our findings emphasize the importance of detecting PC and GPC individually.
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Affiliation(s)
- Menglin Cheng
- Division of Cancer Imaging Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Asif Rizwan
- Division of Cancer Imaging Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Lu Jiang
- Division of Cancer Imaging Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zaver M Bhujwalla
- Division of Cancer Imaging Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kristine Glunde
- Division of Cancer Imaging Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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Greenwood HI, Freimanis RI, Carpentier BM, Joe BN. Clinical Breast Magnetic Resonance Imaging: Technique, Indications, and Future Applications. Semin Ultrasound CT MR 2017; 39:45-59. [PMID: 29317039 DOI: 10.1053/j.sult.2017.07.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for the detection of breast cancer, and it is indicated for breast cancer screening in patients at high-risk of developing breast cancer. It is limited to this group given the high cost. In addition, breast MRI is also indicated for evaluating the extent of disease in patients with new breast cancer diagnoses, monitoring the response to neoadjuvant treatment, and evaluating implant integrity. New promising innovations in breast MRI include fast abbreviated MRI, and functional techniques including diffusion-weighted imaging and magnetic resonance spectroscopy are promising particularly as regards to treatment response.
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Affiliation(s)
- Heather I Greenwood
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA.
| | - Rita I Freimanis
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA
| | - Bianca M Carpentier
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA
| | - Bonnie N Joe
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA
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Breast Tissue Metabolism by Magnetic Resonance Spectroscopy. Metabolites 2017; 7:metabo7020025. [PMID: 28590405 PMCID: PMC5487996 DOI: 10.3390/metabo7020025] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Revised: 05/31/2017] [Accepted: 05/31/2017] [Indexed: 02/06/2023] Open
Abstract
Metabolic alterations are known to occur with oncogenesis and tumor progression. During malignant transformation, the metabolism of cells and tissues is altered. Cancer metabolism can be studied using advanced technologies that detect both metabolites and metabolic activities. Identification, characterization, and quantification of metabolites (metabolomics) are important for metabolic analysis and are usually done by nuclear magnetic resonance (NMR) or by mass spectrometry. In contrast to the magnetic resonance imaging that is used to monitor the tumor morphology during progression of the disease and during therapy, in vivo NMR spectroscopy is used to study and monitor tumor metabolism of cells/tissues by detection of various biochemicals or metabolites involved in various metabolic pathways. Several in vivo, in vitro and ex vivo NMR studies using 1H and 31P magnetic resonance spectroscopy (MRS) nuclei have documented increased levels of total choline containing compounds, phosphomonoesters and phosphodiesters in human breast cancer tissues, which is indicative of altered choline and phospholipid metabolism. These levels get reversed with successful treatment. Another method that increases the sensitivity of substrate detection by using nuclear spin hyperpolarization of 13C-lableled substrates by dynamic nuclear polarization has revived a great interest in the study of cancer metabolism. This review discusses breast tissue metabolism studied by various NMR/MRS methods.
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