Magnetic Resonance Spectroscopy for Cervical Cancer: Review and Potential Prognostic Applications
Abstract
:Simple Summary
Abstract
1. Introduction to MRS
1.1. Localization Methods
1.2. Applications in Medicine
2. Background on Metabolic Pathways of Interest in Cervical Cancer MRS
2.1. Metabolic Pathways of Choline in Cancer
2.2. Metabolic Pathways of Lipids in Cancer
3. In Vivo MRS Studies in Cervical Cancer
4. Future of MRS in Cervical Cancer
4.1. Technological Improvement in Acquisition
4.2. Spectroscopic Imaging
4.3. Artificial Intelligence
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
ADC | Apparent diffusion coefficient |
AI | Artificial Intelligence |
AUC | Area under the curve |
Cho | Free choline |
CIN | Cervical intraepithelial neoplasia |
DWI | Diffusion weighted imaging |
FOV | Field of view |
GBM | Glioblastoma multiforme |
GPC | Glycerophosphocholine |
GR | Good responder |
HPV | Human papillomavirus |
MAS | Magical angle spinning |
MRI | Magnetic resonance imaging |
MRS | Magnetic resonance spectroscopy |
MRSI | Magnetic resonance spectroscopic imaging |
NED | No evidence of disease |
NR | Non-responder |
PCh | Phosphocholine |
PD | Progression of disease |
PR | Partial responder |
PRESS | Point-resolved spectroscopy |
RF | Radiofrequency |
SIB | Simultaneous integrated boost |
SNR | Signal-to-noise ratio |
STEAM | Stimulated echo acquisition mode |
tCho | Total choline |
TE | Echo time |
TM | Mixing time |
TR | Repetition time |
References
- Bottomley, P.A. Spatial Localization in NMR Spectroscopy in Vivo. Ann. N. Y. Acad. Sci. 1987, 508, 333–348. [Google Scholar] [CrossRef] [PubMed]
- Shah, N.; Sattar, A.; Benanti, M.; Hollander, S.; Cheuck, L. Magnetic Resonance Spectroscopy as an Imaging Tool for Cancer: A Review of the Literature. J. Osteopath. Med. 2006, 106, 23–27. [Google Scholar]
- Barker, P.B. Clinical MR Spectroscopy: Techniques and Applications; Cambridge University Press: Cambridge, UK, 2010; ISBN 0-521-86898-X. [Google Scholar]
- Zhu, H.; Barker, P.B. MR Spectroscopy and Spectroscopic Imaging of the Brain. In Magnetic Resonance Neuroimaging: Methods and Protocols; Springer: Berlin/Heidelberg, Germany, 2011; pp. 203–226. [Google Scholar]
- Keeler, J. Understanding NMR Spectroscopy; John Wiley & Sons: Hoboken, NJ, USA, 2010; ISBN 0-470-74608-4. [Google Scholar]
- Henriksen, O. In Vivo Quantitation of Metabolite Concentrations in the Brain by Means of Proton MRS. NMR Biomed. 1995, 8, 139–148. [Google Scholar] [CrossRef]
- Barker, P.B.; Hearshen, D.O.; Boska, M.D. Single-voxel Proton MRS of the Human Brain at 1.5 T and 3.0 T. In Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine; Wiley: Hoboken, NJ, USA, 2001; Volume 45, pp. 765–769. [Google Scholar]
- Öz, G.; Deelchand, D.K.; Wijnen, J.P.; Mlynárik, V.; Xin, L.; Mekle, R.; Noeske, R.; Scheenen, T.W.; Tkáč, I.; Experts’ Working Group on Advanced Single Voxel 1H MRS; et al. 1H Advanced Single Voxel 1H Magnetic Resonance Spectroscopy Techniques in Humans: Experts’ Consensus Recommendations. NMR Biomed. 2021, 34, e4236. [Google Scholar]
- Barker, P.; Gillard, J.; Waldman, A. Fundamentals of MR Spectroscopy. Survival 2005, 12, 14. [Google Scholar]
- Chan, K.L.; Ruhm, L.; Henning, A. MR Spectroscopy and Spectroscopic Imaging. In Advances in Magnetic Resonance Technology and Applications; Elsevier: Amsterdam, The Netherlands, 2023; Volume 10, pp. 421–448. ISBN 2666-9099. [Google Scholar]
- Považan, M.; Mikkelsen, M.; Berrington, A.; Bhattacharyya, P.K.; Brix, M.K.; Buur, P.F.; Cecil, K.M.; Chan, K.L.; Chen, D.Y.; Craven, A.R. Comparison of Multivendor Single-Voxel MR Spectroscopy Data Acquired in Healthy Brain at 26 Sites. Radiology 2020, 295, 171–180. [Google Scholar] [CrossRef]
- Posse, S.; Otazo, R.; Dager, S.R.; Alger, J. MR Spectroscopic Imaging: Principles and Recent Advances. J. Magn. Reson. Imaging 2013, 37, 1301–1325. [Google Scholar] [CrossRef]
- Thota, S.M.; Chan, K.L.; Pradhan, S.S.; Nagabushana, B.; Priyanka, G.B.; Sunil, H.V.; Kanneganti, V.; Vasoya, P.; Vinnakote, K.M.; Viswamitra, S. Multimodal Imaging and Visual Evoked Potentials Reveal Key Structural and Functional Features That Distinguish Symptomatic from Presymptomatic Huntington’s Disease Brain. Neurol. India 2021, 69, 1247–1258. [Google Scholar] [PubMed]
- Chan, K.L.; Panatpur, A.; Messahel, S.; Dahshi, H.; Johnson, T.; Henning, A.; Ren, J.; Minassian, B.A. 1H and 31P Magnetic Resonance Spectroscopy Reveals Potential Pathogenic and Biomarker Metabolite Alterations in Lafora Disease. Brain Commun. 2024, 6, fcae104. [Google Scholar] [CrossRef]
- Moriguchi, S.; Takamiya, A.; Noda, Y.; Horita, N.; Wada, M.; Tsugawa, S.; Plitman, E.; Sano, Y.; Tarumi, R.; ElSalhy, M. Glutamatergic Neurometabolite Levels in Major Depressive Disorder: A Systematic Review and Meta-Analysis of Proton Magnetic Resonance Spectroscopy Studies. Mol. Psychiatry 2019, 24, 952–964. [Google Scholar] [CrossRef]
- Ino, H.; Honda, S.; Yamada, K.; Horita, N.; Tsugawa, S.; Yoshida, K.; Noda, Y.; Meyer, J.H.; Mimura, M.; Nakajima, S. Glutamatergic Neurometabolite Levels in Bipolar Disorder: A Systematic Review and Meta-Analysis of Proton Magnetic Resonance Spectroscopy Studies. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 2023, 8, 140–150. [Google Scholar] [CrossRef] [PubMed]
- Choi, C.; Ganji, S.K.; DeBerardinis, R.J.; Hatanpaa, K.J.; Rakheja, D.; Kovacs, Z.; Yang, X.-L.; Mashimo, T.; Raisanen, J.M.; Marin-Valencia, I. 2-Hydroxyglutarate Detection by Magnetic Resonance Spectroscopy in IDH-Mutated Patients with Gliomas. Nat. Med. 2012, 18, 624–629. [Google Scholar] [CrossRef]
- Thomas, M.A.; Ryner, L.N.; Mehta, M.P.; Turski, P.A.; Sorenson, J.A. Localized 2D J-resolved H MR Spectroscopy of Human Brain Tumors in Vivo. J. Magn. Reson. Imaging 1996, 6, 453–459. [Google Scholar] [CrossRef]
- Richter, J.K.; Vallesi, V.; Zölch, N.; Chan, K.L.; Hunkeler, N.; Abramovic, M.; Hashagen, C.; Christiaanse, E.; Shetty, G.; Verma, R.K. Metabolic Profile of Complete Spinal Cord Injury in Pons and Cerebellum: A 3T 1H MRS Study. Sci. Rep. 2023, 13, 7245. [Google Scholar] [CrossRef] [PubMed]
- Bottomley, P.A.; Weiss, R.G. Non-Invasive Magnetic-Resonance Detection of Creatine Depletion in Non-Viable Infarcted Myocardium. Lancet 1998, 351, 714–718. [Google Scholar] [CrossRef]
- Kreis, R.; Bruegger, K.; Skjelsvik, C.; Zwicky, S.; Ith, M.; Jung, B.; Baumgartner, I.; Boesch, C. Quantitative 1H Magnetic Resonance Spectroscopy of Myoglobin De-and Reoxygenation in Skeletal Muscle: Reproducibility and Effects of Location and Disease. In Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine; Wiley: Hoboken, NJ, USA, 2001; Volume 46, pp. 240–248. [Google Scholar]
- Stamatelatou, A.; Scheenen, T.W.; Heerschap, A. Developments in Proton MR Spectroscopic Imaging of Prostate Cancer. In Magnetic Resonance Materials in Physics, Biology and Medicine; Springer: Berlin/Heidelberg, Germany, 2022; Volume 35, pp. 645–665. [Google Scholar]
- Thomas, M.A.; Nagarajan, R.; Huda, A.; Margolis, D.; Sarma, M.K.; Sheng, K.; Reiter, R.E.; Raman, S.S. Multidimensional MR Spectroscopic Imaging of Prostate Cancer in Vivo. NMR Biomed. 2014, 27, 53–66. [Google Scholar] [CrossRef]
- Dolciami, M.; Canese, R.; Testa, C.; Pernazza, A.; Santangelo, G.; Palaia, I.; Rocca, C.D.; Catalano, C.; Manganaro, L. The Contribution of the 1H-MRS Lipid Signal to Cervical Cancer Prognosis: A Preliminary Study. Eur. Radiol. Exp. 2022, 6, 47. [Google Scholar] [CrossRef]
- Arteaga de Castro, C.S.; Hoogendam, J.P.; van Kalleveen, I.M.L.; Raaijmakers, A.J.E.; Zweemer, R.P.; Verheijen, R.H.M.; Luijten, P.R.; Veldhuis, W.B.; Klomp, D.W.J. Proton MRS of Cervical Cancer at 7 T. NMR Biomed. 2019, 32, e4015. [Google Scholar] [CrossRef] [PubMed]
- Hwang, J.-H.; Choi, C.S. Use of in Vivo Magnetic Resonance Spectroscopy for Studying Metabolic Diseases. Exp. Mol. Med. 2015, 47, e139. [Google Scholar] [CrossRef] [PubMed]
- Pasanta, D.; Htun, K.T.; Pan, J.; Tungjai, M.; Kaewjaeng, S.; Kim, H.; Kaewkhao, J.; Kothan, S. Magnetic Resonance Spectroscopy of Hepatic Fat from Fundamental to Clinical Applications. Diagnostics 2021, 11, 842. [Google Scholar] [CrossRef]
- Zapotoczna, A.; Sasso, G.; Simpson, J.; Roach III, M. Current Role and Future Perspectives of Magnetic Resonance Spectroscopy in Radiation Oncology for Prostate Cancer. Neoplasia 2007, 9, 455–463. [Google Scholar] [CrossRef]
- Pickett, B.; Vigneault, E.; Kurhanewicz, J.; Verhey, L.; Roach, M. Static Field Intensity Modulation to Treat a Dominant Intra-Prostatic Lesion to 90 Gy Compared to Seven Field 3-Dimensional Radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 1999, 44, 921–929. [Google Scholar] [CrossRef]
- Manzo-Merino, J.; Contreras-Paredes, A.; Vázquez-Ulloa, E.; Rocha-Zavaleta, L.; Fuentes-Gonzalez, A.M.; Lizano, M. The Role of Signaling Pathways in Cervical Cancer and Molecular Therapeutic Targets. Arch. Med. Res. 2014, 45, 525–539. [Google Scholar] [CrossRef]
- Chang, Q.-Q.; Chen, C.-Y.; Chen, Z.; Chang, S. LncRNA PVT1 Promotes Proliferation and Invasion through Enhancing Smad3 Expression by Sponging miR-140-5p in Cervical Cancer. Radiol. Oncol. 2019, 53, 443–452. [Google Scholar] [CrossRef] [PubMed]
- Chen, S.; Chen, Y.; Yu, L.; Hu, X. Overexpression of SOCS4 Inhibits Proliferation and Migration of Cervical Cancer Cells by Regulating JAK1/STAT3 Signaling Pathway. Eur. J. Gynaecol. Oncol. 2021, 42, 554–560. [Google Scholar]
- Yi, Y.; Fang, Y.; Wu, K.; Liu, Y.; Zhang, W. Comprehensive Gene and Pathway Analysis of Cervical Cancer Progression. Oncol. Lett. 2020, 19, 3316–3332. [Google Scholar] [CrossRef] [PubMed]
- Warburg, O. On the Origin of Cancer Cells. Science 1956, 123, 309–314. [Google Scholar] [CrossRef]
- Glunde, K.; Jacobs, M.A.; Bhujwalla, Z.M. Choline Metabolism in Cancer: Implications for Diagnosis and Therapy. Expert Rev. Mol. Diagn. 2006, 6, 821–829. [Google Scholar] [CrossRef]
- Glunde, K.; Bhujwalla, Z.M.; Ronen, S.M. Choline Metabolism in Malignant Transformation. Nat. Rev. Cancer 2011, 11, 835–848. [Google Scholar] [CrossRef]
- Glunde, K.; Penet, M.-F.; Jiang, L.; Jacobs, M.A.; Bhujwalla, Z.M. Choline Metabolism-Based Molecular Diagnosis of Cancer: An Update. Expert Rev. Mol. Diagn. 2015, 15, 735–747. [Google Scholar] [CrossRef]
- Bagnoli, M.; Granata, A.; Nicoletti, R.; Krishnamachary, B.; Bhujwalla, Z.M.; Canese, R.; Podo, F.; Canevari, S.; Iorio, E.; Mezzanzanica, D. Choline Metabolism Alteration: A Focus on Ovarian Cancer. Front. Oncol. 2016, 6, 153. [Google Scholar] [CrossRef]
- Lyndon, D.; Lansley, J.A.; Evanson, J.; Krishnan, A.S. Dural Masses: Meningiomas and Their Mimics. Insights Imaging 2019, 10, 11. [Google Scholar] [CrossRef]
- Ramesh, K.; Mellon, E.A.; Gurbani, S.S.; Weinberg, B.D.; Schreibmann, E.; Sheriff, S.A.; Goryawala, M.; De Le Fuente, M.; Eaton, B.R.; Zhong, J. A Multi-Institutional Pilot Clinical Trial of Spectroscopic MRI-Guided Radiation Dose Escalation for Newly Diagnosed Glioblastoma. Neuro-Oncol. Adv. 2022, 4, vdac006. [Google Scholar] [CrossRef]
- Aboagye, E.O.; Bhujwalla, Z.M. Malignant Transformation Alters Membrane Choline Phospholipid Metabolism of Human Mammary Epithelial Cells. Cancer Res. 1999, 59, 80–84. [Google Scholar]
- Currie, E.; Schulze, A.; Zechner, R.; Walther, T.C.; Farese, R.V. Cellular Fatty Acid Metabolism and Cancer. Cell Metab. 2013, 18, 153–161. [Google Scholar] [CrossRef]
- Nascimento, J.; Mariot, C.; Vianna, D.R.; Kliemann, L.M.; Chaves, P.S.; Loda, M.; Buffon, A.; Beck, R.C.; Pilger, D.A. Fatty Acid Synthase as a Potential New Therapeutic Target for Cervical Cancer. An. Acad. Bras. Ciências 2022, 94, e20210670. [Google Scholar] [CrossRef]
- Ping, P.; Li, J.; Lei, H.; Xu, X. Fatty Acid Metabolism: A New Therapeutic Target for Cervical Cancer. Front. Oncol. 2023, 13, 1111778. [Google Scholar] [CrossRef]
- Thakur, S.B.; Horvat, J.V.; Hancu, I.; Sutton, O.M.; Bernard-Davila, B.; Weber, M.; Oh, J.H.; Marino, M.A.; Avendano, D.; Leithner, D. Quantitative in Vivo Proton MR Spectroscopic Assessment of Lipid Metabolism: Value for Breast Cancer Diagnosis and Prognosis. J. Magn. Reson. Imaging 2019, 50, 239–249. [Google Scholar] [CrossRef]
- Thomas, M.A.; Lipnick, S.; Velan, S.S.; Liu, X.; Banakar, S.; Binesh, N.; Ramadan, S.; Ambrosio, A.; Raylman, R.R.; Sayre, J. Investigation of Breast Cancer Using Two-dimensional MRS. NMR Biomed. Int. J. Devoted Dev. Appl. Magn. Reson. Vivo 2009, 22, 77–91. [Google Scholar] [CrossRef]
- Sijens, P.E.; Levendag, P.C.; Vecht, C.J.; Dijk, P.; van Oudkerk, M. 1H MR Spectroscopy Detection of Lipids and Lactate in Metastatic Brain Tumors. NMR Biomed. Int. J. Devoted Dev. Appl. Magn. Reson. Vivo 1996, 9, 65–71. [Google Scholar] [CrossRef]
- Agarwal, K.; Sharma, U.; Mathur, S.; Seenu, V.; Parshad, R.; Jagannathan, N.R. Study of Lipid Metabolism by Estimating the Fat Fraction in Different Breast Tissues and in Various Breast Tumor Sub-Types by In Vivo 1H MR Spectroscopy. Magn. Reson. Imaging 2018, 49, 116–122. [Google Scholar] [CrossRef]
- Lee, J.H.; Cho, K.S.; Kim, Y.M.; Kim, S.T.; Mun, C.W.; Na, J.H.; Mok, J.E.; Lim, T.H. Localized in Vivo 1H Nuclear MR Spectroscopy for Evaluation of Human Uterine Cervical Carcinoma. AJR Am. J. Roentgenol. 1998, 170, 1279–1282. [Google Scholar] [CrossRef]
- Booth, S.J.; Pickles, M.D.; Turnbull, L.W. In Vivo Magnetic Resonance Spectroscopy of Gynaecological Tumours at 3.0 Tesla. BJOG Int. J. Obstet. Gynaecol. 2009, 116, 300–303. [Google Scholar] [CrossRef]
- De Silva, S.S.; Payne, G.S.; Morgan, V.A.; Ind, T.E.; Shepherd, J.H.; Barton, D.P.; desouza, N.M. Epithelial and Stromal Metabolite Changes in the Transition from Cervical Intraepithelial Neoplasia to Cervical Cancer: An in Vivo 1 H Magnetic Resonance Spectroscopic Imaging Study with Ex Vivo Correlation. Eur. Radiol. 2009, 19, 2041–2048. [Google Scholar] [CrossRef]
- Mahon, M.M.; Williams, A.D.; Soutter, W.P.; Cox, I.J.; McIndoe, G.A.; Coutts, G.A.; Dina, R.; desouza, N.M. 1H Magnetic Resonance Spectroscopy of Invasive Cervical Cancer: An in Vivo Study with Ex Vivo Corroboration. NMR Biomed. Int. J. Devoted Dev. Appl. Magn. Reson. In Vivo 2004, 17, 1–9. [Google Scholar] [CrossRef]
- Mahon, M.M.; Cox, I.J.; Dina, R.; Soutter Frcog, W.P.; Mcindoe Mrcog, G.A.; Williams, A.D.; desouza, N.M. 1H Magnetic Resonance Spectroscopy of Preinvasive and Invasive Cervical Cancer: In Vivo–Ex Vivo Profiles and Effect of Tumor Load. J. Magn. Reson. Imaging Off. J. Int. Soc. Magn. Reson. Med. 2004, 19, 356–364. [Google Scholar] [CrossRef]
- Lin, G.; Lai, C.-H.; Tsai, S.-Y.; Lin, Y.-C.; Huang, Y.-T.; Wu, R.-C.; Yang, L.-Y.; Lu, H.-Y.; Chao, A.; Wang, C.-C. 1H MR Spectroscopy in Cervical Carcinoma Using External Phase Array Body Coil at 3.0 Tesla: Prediction of Poor Prognostic Human Papillomavirus Genotypes. J. Magn. Reson. Imaging 2017, 45, 899–907. [Google Scholar] [CrossRef]
- Provencher, S.W. Automatic Quantitation of Localized in Vivo 1H Spectra with LCModel. NMR Biomed. Int. J. Devoted Dev. Appl. Magn. Reson. In Vivo 2001, 14, 260–264. [Google Scholar]
- Allen, J.R.; Prost, R.W.; Griffith, O.W.; Erickson, S.J.; Erickson, B.A. In Vivo Proton (H1) Magnetic Resonance Spectroscopy for Cervical Carcinoma. Am. J. Clin. Oncol. 2001, 24, 522–529. [Google Scholar] [CrossRef]
- Rizzo, S.; Buscarino, V.; Origgi, D.; Summers, P.; Raimondi, S.; Lazzari, R.; Landoni, F.; Bellomi, M. Evaluation of Diffusion-Weighted Imaging (DWI) and MR Spectroscopy (MRS) as Early Response Biomarkers in Cervical Cancer Patients. Radiol. Med. 2016, 121, 838–846. [Google Scholar] [CrossRef]
- Payne, G.S.; Schmidt, M.; Morgan, V.A.; Giles, S.; Bridges, J.; Ind, T.; DeSouza, N.M. Evaluation of Magnetic Resonance Diffusion and Spectroscopy Measurements as Predictive Biomarkers in Stage 1 Cervical Cancer. Gynecol. Oncol. 2010, 116, 246–252. [Google Scholar] [CrossRef]
- Fuchs, A.; Luttje, M.; Boesiger, P.; Henning, A. SPECIAL semi-LASER with Lipid Artifact Compensation for 1H MRS at 7 T. Magn. Reson. Med. 2013, 69, 603–612. [Google Scholar] [CrossRef]
- Deelchand, D.K.; Berrington, A.; Noeske, R.; Joers, J.M.; Arani, A.; Gillen, J.; Schär, M.; Nielsen, J.-F.; Peltier, S.; Seraji-Bozorgzad, N. Across-vendor Standardization of semi-LASER for Single-voxel MRS at 3T. NMR Biomed. 2021, 34, e4218. [Google Scholar] [CrossRef]
- Juchem, C.; Cudalbu, C.; de Graaf, R.A.; Gruetter, R.; Henning, A.; Hetherington, H.P.; Boer, V.O. B0 Shimming for in Vivo Magnetic Resonance Spectroscopy: Experts’ Consensus Recommendations. NMR Biomed. 2021, 34, e4350. [Google Scholar] [CrossRef]
- Chan, K.L.; Hock, A.; Edden, R.A.; MacMillan, E.L.; Henning, A. Improved Prospective Frequency Correction for Macromolecule-suppressed GABA Editing with Metabolite Cycling at 3T. Magn. Reson. Med. 2021, 86, 2945–2956. [Google Scholar] [CrossRef]
- Puts, N.A.; Edden, R.A. In Vivo Magnetic Resonance Spectroscopy of GABA: A Methodological Review. Prog. Nucl. Magn. Reson. Spectrosc. 2012, 60, 29–41. [Google Scholar] [CrossRef]
- Lindeboom, L.; de Graaf, R.A. Measurement of Lipid Composition in Human Skeletal Muscle and Adipose Tissue with 1H-MRS Homonuclear Spectral Editing. Magn. Reson. Med. 2018, 79, 619–627. [Google Scholar] [CrossRef]
- Lin, D.; Zhou, J.; Cao, Y.; Wang, Z.; Hsu, Y.-C.; Zheng, F.; Li, H.; Sun, S.; Ren, H.; Deng, L. Echo Time Optimization for In-vivo Measurement of Unsaturated Lipid Resonances Using J-difference-edited MRS. Magn. Reson. Med. 2023, 90, 2217–2232. [Google Scholar] [CrossRef]
- Bogner, W.; Otazo, R.; Henning, A. Accelerated MR Spectroscopic Imaging—A Review of Current and Emerging Techniques. NMR Biomed. 2021, 34, e4314. [Google Scholar] [CrossRef]
- Chan, K.L.; Ziegs, T.; Henning, A. Improved Signal-to-noise Performance of MultiNet GRAPPA 1H FID MRSI Reconstruction with Semi-synthetic Calibration Data. Magn. Reson. Med. 2022, 88, 1500–1515. [Google Scholar] [CrossRef]
- Strasser, B.; Považan, M.; Hangel, G.; Hingerl, L.; Chmelik, M.; Gruber, S.; Trattnig, S.; Bogner, W. (2 + 1) D-CAIPIRINHA Accelerated MR Spectroscopic Imaging of the Brain at 7T. Magn. Reson. Med. 2017, 78, 429–440. [Google Scholar] [CrossRef] [PubMed]
- Wilson, N.E.; Iqbal, Z.; Burns, B.L.; Keller, M.; Thomas, M.A. Accelerated Five-dimensional Echo Planar J-resolved Spectroscopic Imaging: Implementation and Pilot Validation in Human Brain. Magn. Reson. Med. 2016, 75, 42–51. [Google Scholar] [CrossRef] [PubMed]
- Posse, S.; DeCarli, C.; Le Bihan, D. Three-Dimensional Echo-Planar MR Spectroscopic Imaging at Short Echo Times in the Human Brain. Radiology 1994, 192, 733–738. [Google Scholar] [CrossRef] [PubMed]
- Hatami, N.; Sdika, M.; Ratiney, H. Magnetic Resonance Spectroscopy Quantification Using Deep Learning. In Proceedings of the Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, 16–20 September 2018; Proceedings, Part I. Springer: Berlin/Heidelberg, Germany, 2018; pp. 467–475. [Google Scholar]
- Iqbal, Z.; Nguyen, D.; Thomas, M.A.; Jiang, S. Acceleration and Quantitation of Localized Correlated Spectroscopy Using Deep Learning: A Pilot Simulation Study. arXiv 2018, arXiv:1806.11068. [Google Scholar]
- Iqbal, Z.; Nguyen, D.; Hangel, G.; Motyka, S.; Bogner, W.; Jiang, S. Super-Resolution 1H Magnetic Resonance Spectroscopic Imaging Utilizing Deep Learning. Front. Oncol. 2019, 9, 1010. [Google Scholar] [CrossRef] [PubMed]
- Lundervold, A.S.; Lundervold, A. An Overview of Deep Learning in Medical Imaging Focusing on MRI. Z. Med. Phys. 2019, 29, 102–127. [Google Scholar] [CrossRef] [PubMed]
- Kumawat, G.; Vishwakarma, S.K.; Chakrabarti, P.; Chittora, P.; Chakrabarti, T.; Lin, J.C.-W. Prognosis of Cervical Cancer Disease by Applying Machine Learning Techniques. J. Circuits Syst. Comput. 2023, 32, 2350019. [Google Scholar] [CrossRef]
- Habtemariam, L.W.; Zewde, E.T.; Simegn, G.L. Cervix Type and Cervical Cancer Classification System Using Deep Learning Techniques. Med. Devices Evid. Res. 2022, 163–176. [Google Scholar] [CrossRef] [PubMed]
- Youneszade, N.; Marjani, M.; Pei, C.P. Deep Learning in Cervical Cancer Diagnosis: Architecture, Opportunities, and Open Research Challenges. IEEE Access 2023, 11, 6133–6149. [Google Scholar] [CrossRef]
- Tran, K.A.; Kondrashova, O.; Bradley, A.; Williams, E.D.; Pearson, J.V.; Waddell, N. Deep Learning in Cancer Diagnosis, Prognosis and Treatment Selection. Genome Med. 2021, 13, 152. [Google Scholar] [CrossRef]
- Trebeschi, S.; Bodalal, Z.; Boellaard, T.N.; Tareco Bucho, T.M.; Drago, S.G.; Kurilova, I.; Calin-Vainak, A.M.; Delli Pizzi, A.; Muller, M.; Hummelink, K. Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy. Front. Oncol. 2021, 11, 609054. [Google Scholar] [CrossRef] [PubMed]
Number of Patients | Field Strength (T) | MRS Sequence | TR/TE (ms) | Clinical Finding | Reference |
---|---|---|---|---|---|
51 | 1.5 | PRESS | 3000/20 and 3000/135 | Lipid peaks can be used to discriminate between squamous cell carcinoma and adenocarcinoma | [49] |
14 | 3 | PRESS | 1500/72 | tCho peaks prominent in different GYN cancers, however, cannot discriminate level of malignancy | [50] |
47 | 1.5 | PRESS CSI | 888/135 | tCho are elevated in cancer compared to cervical intraepithelial neoplasia (p = 0.033) | [51] |
39 | 1.5 | PRESS | 1600/135 | tCho and lipids are elevated in cervical cancer patients | [52] |
27 | 1.5 | PRESS | 1600/135 | Lipid peaks are significantly elevated in cervical cancer, however there was no correlation with tumor load | [53] |
52 | 3 | PRESS | 2000/35 | Lipid peaks can be used to distinguish poor prognostic HPV genotypes | [54] |
10 | 7 | STEAM and CSI | 1400/36–75 1400/10 | Lipid ratios were higher as a function of tumor grade, however the result was not significant | [25] |
16–18 | 1.5 | PRESS | 1600/140 | tCho levels remain high for patients with recurrence after radiation treatment | [56] |
17 | 3 | PRESS | 1500/28 and 1500/144 | Lipids are significantly elevated in partial or no response group, and Choline is elevated in good responder group | [24] |
16 | 1.5 | PRESS | 2000/135 | No MRS changes. ADC values of the tumor increased at the mid-treatment time point | [57] |
62 | 1.5 | PRESS CSI | 888/135 | No MRS changes. ADC values of the tumor were significantly lower compared to normal tissue | [58] |
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Iqbal, Z.; Albuquerque, K.; Chan, K.L. Magnetic Resonance Spectroscopy for Cervical Cancer: Review and Potential Prognostic Applications. Cancers 2024, 16, 2141. https://doi.org/10.3390/cancers16112141
Iqbal Z, Albuquerque K, Chan KL. Magnetic Resonance Spectroscopy for Cervical Cancer: Review and Potential Prognostic Applications. Cancers. 2024; 16(11):2141. https://doi.org/10.3390/cancers16112141
Chicago/Turabian StyleIqbal, Zohaib, Kevin Albuquerque, and Kimberly L. Chan. 2024. "Magnetic Resonance Spectroscopy for Cervical Cancer: Review and Potential Prognostic Applications" Cancers 16, no. 11: 2141. https://doi.org/10.3390/cancers16112141
APA StyleIqbal, Z., Albuquerque, K., & Chan, K. L. (2024). Magnetic Resonance Spectroscopy for Cervical Cancer: Review and Potential Prognostic Applications. Cancers, 16(11), 2141. https://doi.org/10.3390/cancers16112141