Functional imaging beyond anatomy: The emerging role of magnetic resonance spectroscopy and metabolic biomarkers in monitoring targeted cancer therapy

Authors

  • Hao Sun ZJU-Xinchang Joint Innovation Centre (TianMu Laboratory), Gaochang Hi-Tech Park, Xinchang 312500, China; College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; Wolfson Institute for Biomedical Research, Division of Medicine, University College London, Gower St, WC1E 6BT, London, United Kingdom https://orcid.org/0009-0009-9013-5681
  • Xiaojun Dong Hunan Provincial Key Laboratory of Novel Antibody Drugs and Their Intelligent Transportation Systems, Hunan Provincial Key Laboratory of Synthetic Biology Research of Traditional Chinese Medicines, School of Medical Information and Engineering, Hunan University of Medicine, Huaihua, China https://orcid.org/0009-0009-7497-4944
  • Jinze Yu Computer Science and Communication Engineering, Global Center for Science and Engineering, Faculty of Science, Waseda University, Tokyo, Japan https://orcid.org/0009-0000-2509-0047
  • Yuanyin Teng Institute of Hematology, Zhejiang University, Hangzhou 310003, China https://orcid.org/0009-0003-5331-7220
  • Gaoyang Guo Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia (UKM), 43600 UKM Bangi, Selangor, Malaysia https://orcid.org/0000-0001-9873-8676
  • Liangyu Li Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia (UKM), 43600 UKM Bangi, Selangor, Malaysia https://orcid.org/0000-0002-7535-7448
  • Wei Wang Center for Precision Health, School of Medical and Health Science, Edith Cowan University, Perth, WA, 6027, Australia; The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, 515041, China https://orcid.org/0000-0003-4821-0130
  • Bing Liu College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; ZJU-Xinchang Joint Innovation Centre (TianMu Laboratory), Gaochuang Hi-Tech Park, Xinchang 312500, China https://orcid.org/0000-0003-0000-3810
  • Yuquan Li State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guizhou, China https://orcid.org/0009-0006-9568-3335
  • Keyang Xu Faculty of Chinese Medicine, State Key Laboratory of Mechanism and Quality of Chinese Medicine, Macau University of Science and Technology, Macau, China https://orcid.org/0000-0002-7049-2528

DOI:

https://doi.org/10.14295/bjs.v6i3.963

Keywords:

magnetic resonance spectroscopy, metabolic biomarkers, targeted cancer therapy, hyperpolarized 13C MRI, deuterium metabolic imaging

Abstract

Anatomical imaging — measurements of tumour size on computed tomography or conventional magnetic resonance imaging (MRI) — remains the backbone of oncologic response assessment, yet it is structurally blind to the earliest events of treatment action. Targeted anticancer agents, by definition, act on specific molecular pathways whose blockade alters tumour metabolism within hours to days, long before any measurable change in tumour dimensions. Magnetic resonance spectroscopy (MRS) and spectroscopic imaging (MRSI) are uniquely positioned to capture these pharmacodynamic events non-invasively, repeatedly and without ionising radiation, by quantifying endogenous metabolites that report on membrane phospholipid turnover (total choline, phosphomonoesters), aerobic glycolysis (lactate), neuronal and tissue integrity (N-acetylaspartate, citrate), oncometabolite production (2-hydroxyglutarate in IDH-mutant glioma) and, with hyperpolarised 13C and deuterium (2H) tracers, real-time metabolic fluxes. This review synthesises the principles, biomarker repertoire and clinical evidence for MRS-based monitoring of targeted cancer therapy across glioma, breast, prostate and abdominal malignancies; examines how metabolic readouts document on-target pathway modulation for kinase, PI3K/AKT/mTOR, IDH, antiangiogenic and endocrine agents; surveys the maturing technologies of hyperpolarised 13C MRSI and deuterium metabolic imaging; and discusses the integration of MRS with multiparametric MRI, artificial intelligence and nanotheranostic drug development. We argue that the field is transitioning from proof-of-principle pharmacodynamic studies toward standardised, biomarker-guided adaptive therapy, and that the principal barriers — technical harmonisation, quantification standards and prospective validation — are now well defined and addressable. Functional imaging beyond anatomy, anchored in MRS-detectable metabolic biomarkers, offers a practical route to earlier response assessment, reduced exposure to ineffective therapy and more informative oncology drug development.

References

Amir, J., Muhammad, S., Kashif, M., Khan, A. A., Gul, M., Sun, H., Shah, M., Azizi, S., & Maaza, M. (2025). Synthesis, characterization and dielectric properties evaluation of NiO-Co3O4 nanocomposite. Journal of the Iranian Chemical Society, 22, 63-72. https://doi.org/10.1007/s13738-024-03129-0

Andronesi, O. C., Arrillaga-Romany, I. C., Ly, K. I., Bogner, W., Ratai, E. M., Reitz, K., Chi, A. S., Cahill, D. P., & Batchelor, T. T. (2018). Pharmacodynamics of mutant-IDH1 inhibitors in glioma patients probed by in vivo 3D MRS imaging of 2-hydroxyglutarate. Nature Communications, 9, 1474. https://doi.org/10.1038/s41467-018-03905-6

Baek, H. M., Chen, J. H., Nalcioglu, O., Su, M. Y. (2008). Proton MR spectroscopy for monitoring early treatment response of breast cancer to neo-adjuvant chemotherapy. Annals of Oncology, 19(5), 1022-1024.

Branzoli, F., & Marjańska, M. (2020). Magnetic resonance spectroscopy of IDH-mutated gliomas: current knowledge on the neurochemical profile. Current Opinion in Neurology, 33(4), 413-421. https://doi.org/10.1097/WCO.0000000000000833

Bushra, Kashif, M., Khairullah, Khan, A. A., Sun, H., Yousaf, J., & Ahmad, T. (2024). Synthesis and characterization of Fe-doped CuO nanoparticles: Catalytic efficiency in crystal violet dye degradation and exploration of electrical properties. Brazilian Journal of Science, 3(8), 1-18. https://doi.org/10.14295/bjs.v3i8.601

Bushra, Kashif, M., Khairullah, Khan, A. A., Sun, H., Yousaf, J., Shah, M. I. A., Hussain, S., Amir, J., Jamal, Y., & Ahmad, T. (2024). Synthesis and characterization of Fe-doped CuO nanoparticles: Catalytic efficiency in crystal violet dye degradation and exploration of electrical properties. Brazilian Journal of Science, 3(8), 1-18. https://doi.org/10.14295/bjs.v3i8.601

Cao, M. D., Sitter, B., Bathen, T. F., Bofin, A., Lundgren, S., & Gribbestad, I. S. (2012). Predicting long-term survival and treatment response in breast cancer patients receiving neoadjuvant chemotherapy by MR metabolic profiling. NMR in Biomedicine, 25(2), 369-378. https://doi.org/10.1002/nbm.1762

Chetry, M., Feng, R., Babar, S., Sun, H., Zafar, I., Mohany, M., Afridi, H. I., Khan, N. U., Ali, I., Shafiq, M., & Khan, S. (2025). Early detection and analysis of accurate breast cancer for improved diagnosis using deep supervised learning for enhanced patient outcomes. PeerJ Computer Science, 11, e2784. https://doi.org/10.7717/peerj-cs.2784

Chen, H. Y., Aggarwal, R., Bok, R. A., Ohliger, M. A., Zhu, Z., Lee, P., Gordon, J. W., Carvajal, L., Laeson, P. E. S., Kurhanewicz, J., & Vigneron, D. B. (2020). Hyperpolarized 13C-pyruvate MRI detects real-time metabolic flux in prostate cancer metastases to bone and liver: a clinical feasibility study. Prostate Cancer and Prostatic Diseases, 23, 269-276. https://doi.org/10.1038/s41391-019-0180-z

Cheng, L. L., MA Burns, M. A., Taylor, J. L., He, W., & . (2005). Metabolic characterization of human prostate cancer with tissue magnetic resonance spectroscopy. Cancer Research, 65(8), 3030-3034. https://doi.org/10.1158/0008-5472.CAN-04-4106

Chetta, P., & Zadra, G. (2021). Metabolic reprogramming as an emerging mechanism of resistance to endocrine therapies in prostate cancer. Cancer Drug Resistance, 4(1), 143-162. https://doi.org/10.20517/cdr.2020.54

Chetry, M., Feng, R., Babar, S., Sun, H., Zafar, I., Mohany, M., Afridi, H. I., Khan, N. U., Ali, I., Shafiq, M., & Khan, S. (2025). Early detection and analysis of accurate breast cancer for improved diagnosis using deep supervised learning for enhanced patient outcomes. PeerJ Computer Science, 11, e2784. https://doi.org/10.7717/peerj-cs.2784

Cho, N., Im, S. A., Kang, K. W., Park, I. A., Song, I-A., Lee, K-H., Kim, T-Y., Lee, H., Chun, I. K., Yoon, H-J., & Moon, W. K. (2016). Early prediction of response to neoadjuvant chemotherapy in breast cancer patients: comparison of single-voxel 1H-magnetic resonance spectroscopy and 18F-fluorodeoxyglucose positron emission tomography. European Radiology, 26, 2279-2290. https://doi.org/10.1007/s00330-015-4014-7

Day, S. E., Kettunen, M. I., Gallagher, F. A., Hu, E. E., Lerche, M., Wolber, J., Golman, K., Ardenkjaer-Larsen, J. H., & Brindle, K. M. (2007). Detecting tumor response to treatment using hyperpolarized 13C magnetic resonance imaging and spectroscopy. Nature Medicine, 13, 1382-1387. https://doi.org/10.1038/nm1650

De Feyter, H. M., Behar, K. L., Corbin, Z. A., Fulbright, R. K., Brown, P. B., McIntyre, S., Nixon, T. W., Rothman, D. L., & De Graaf, R. A. (2018). Deuterium metabolic imaging (DMI) for MRI-based 3D mapping of metabolism in vivo. Science Advances, 4(8). https://doi.org/10.1126/sciadv.aat7314

Glunde, K., & Bhujwalla, Z. M. (2011). Metabolic tumor imaging using magnetic resonance spectroscopy. Seminars in Oncology, 38(1), 26-41. https://doi.org/10.1053/j.seminoncol.2010.11.001

Gul, M., Kashif, M., Muhammad, S., Azizi, S., & Sun, H. (2025). Various methods of synthesis and applications of gold-based nanomaterials: A detailed review. Crystal Growth & Design, 25(7), 2227-2266. https://doi.org/10.1021/acs.cgd.4c01687

Gul, M., Kashif, M., Muhammad, S., Azizi, S., & Sun, H. (2025). Various methods of synthesis and applications of gold-based nanomaterials: A detailed review. Crystal Growth & Design, 25(7), 2227-2266. https://doi.org/10.1021/acs.cgd.4c01687

Hesketh, R. L., & Brindle, K. M. (2018). Magnetic resonance imaging of cancer metabolism with hyperpolarized 13C-labeled cell metabolites. Current Opinion in Chemical Biology, 45, 187-194. https://doi.org/10.1016/j.cbpa.2018.03.004

Jagannathan, N. R., Kumar, M., Seenu, V., Coshic, O., Dwivedi, S. N., Julka, P. K., Srivastava, A., & Rath, G. K. (2001). Evaluation of total choline from in-vivo volume localized proton MR spectroscopy and its response to neoadjuvant chemotherapy in locally advanced breast cancer. British Journal of Cancer, 84, 1016-1022. https://doi.org/10.1054/bjoc.2000.1711

Jamal, M., Nabi, G. A. K., Sun, H., Ullah, K., Khattak, O. A., Kashif, M., Khan, S., Alam, M., Hussain, S., Ullah, M., Aleena, S., Haq, H., Umar, S., Atif, M., Hussain, I., & Masood, A. (2024). Preparation of manganese-doped bismuth oxide for the photocatalytic degradation of methylene blue. Archives of Advanced Engineering Science, 1-7. 10.47852/bonviewAAES42023402.

Jia, B., Zhao, L., Tang, M., Xie, N., Huang, Y., Liang, X., Yang, G., Wu, Q., Sun, Z., Kwan, H. L., Sun, H., & Xu, K. (2026). Neuron–tumor crosstalk in cancer: Molecular mechanisms and translational advances. Molecular Cancer. https://doi.org/10.1186/s12943-026-02715-5

Kashif, M., Jawad, M., Khan, A., Sun, H., Ullah, K., Fakayode, O., & Azizi, S. (2024). Fe/Ti-codoped strontium oxide nanoparticles for enhanced photocatalytic degradation of methyl orange. Journal of Applied Research in Water and Wastewater, 11(1), 8-14. https://doi.org/10.22126/arww.2024.10753.1336

Kashif, M., Jawad, M., Khan, A., Sun, H., Ullah, K., Fakayode, O., Azizi, S. (2024). Fe/Ti-codoped strontium oxide nanoparticles for enhanced photocatalytic degradation of methyl orange. Journal of Applied Research in Water and Wastewater, 11(1), 8-14. https://doi.org/10.22126/arww.2024.10753.1336

Kashif, M., Gul, M., Shahzad, N., Sun, H., Shezan, S. A., Ahmad, N., Hamlaoui, O., & Tozan, H. (2026). Metal oxide nanoparticles: A comprehensive review of recent advances in synthesis strategies, characterization, and multifunctional applications. Catalysts, 16(8), 678. https://doi.org/10.3390/catal16080678

Kauppinen, R. A., & Peet, A. C. (2011). Using magnetic resonance imaging and spectroscopy in cancer diagnostics and monitoring: preclinical and clinical approaches. Cancer Biology & Therapy, 12(8), 665-679. https://doi.org/10.4161/cbt.12.8.18137

Kreis, F., Wright, A. J., Hesse, F., Fala, M., Hu, D., & Brindle, K. M. (2020). Measuring tumor glycolytic flux in vivo by using fast deuterium MRI. Radiology, 294(2). https://doi.org/10.1148/radiol.2019191242

Liu, S., Zhang, X., Wang, W., Li, X., Sun, X., Zhao, Y., Wang, Q. (2024). Metabolic reprogramming and therapeutic resistance in primary and metastatic breast cancer. Molecular Cancer, 23, 261. https://doi.org/10.1186/s12943-024-02165-x

Liu, S., Sun, H., Song, T., Liang, C., Deng, L., Zhu, H., Zhao, F., & Li, S. (2025). Comprehensive characterization of T cell subtypes in lung adenocarcinoma: Prognostic, predictive, and therapeutic implications. Translational Oncology, 55, 102332. https://doi.org/10.1016/j.tranon.2025.102332

Liu, Y., Zhou, Q., Song, S., & Tang, S. (2021). Integrating metabolic reprogramming and metabolic imaging to predict breast cancer therapeutic responses. Trends in Endocrinology & Metabolism, 32(10), 762-775.

Lodi, A., & Ronen, S. M. (2011). Magnetic resonance spectroscopy detectable metabolomic fingerprint of response to antineoplastic treatment. PLOS ONE, 6(10), e26155. https://doi.org/10.1371/journal.pone.0026155

Manton, D. J., Chaturvedi, A., Hubbard, A., Lind, M. J., Lowry, M., Maeaveyas, A., Pickles, M. D., & Turnbull, L. W. (2006). Neoadjuvant chemotherapy in breast cancer: early response prediction with quantitative MR imaging and spectroscopy. British Journal of Cancer, 94, 427-435. https://doi.org/10.1038/sj.bjc.6602948

Mignion, L., Dutta, P., Martinez, G. V., Foroutan, P., Gillies, R. J., & Jordan, B. F. (2014). Monitoring chemotherapeutic response by hyperpolarized 13C-fumarate MRS and diffusion MRI. Cancer Research, 74(3), 686-694. https://doi.org/10.1158/0008-5472.CAN-13-1914

Muhammad, S., Sun, H., Zahoor, S., Khan, S., Ullah, R., Fu, B., Ullah, S., Isimjan, T. T., Azizi, S., & Yang, X. (2026). Advancing MOFs for electrocatalytic ORR, UOR, and CO2RR: From fundamentals to device integration and deployment challenges. Materials Science and Engineering: R: Reports, 171, 101248. https://doi.org/10.1016/j.mser.2026.101248

Najac, C., & Ronen, S. M. (2016). MR molecular imaging of brain cancer metabolism using hyperpolarized 13C magnetic resonance spectroscopy. Topics in Magnetic Resonance Imaging, 25(5), 187-196 https://doi.org/10.1097/RMR.0000000000000104

Pan, F., Liu, X., Wan, J., Guo, Y., Sun, P., Zhang, X., Wang, J., Bao, Q., & Yang, L. (2024). Advances and prospects in deuterium metabolic imaging (DMI): a systematic review of in vivo studies. European Radiology Experimental, 8. https://doi.org/10.1186/s41747-024-00464-y

Peng, Z., Lv, X., Sun, H., Zhao, L., & Huang, S. (2025). 3D tumor cultures for drug resistance and screening development in clinical applications. Molecular Cancer, 24, 93. https://doi.org/10.1186/s12943-025-02281-2

Peng, Z., Lv, X., Sun, H., Zhao, L., & Huang, S. (2025). 3D tumor cultures for drug resistance and screening development in clinical applications. Molecular Cancer, 24, 93. https://doi.org/10.1186/s12943-025-02281-2

Pope, W. B., Prins, R. M., Albert Thomas, M., Nagarajan, R., Yen, K. E., Bittinger, M. A., Lai, A., Kornblum, H. I., Wu, H., Fantin, V. R., & Liau, L. M. (2012). Non-invasive detection of 2-hydroxyglutarate and other metabolites in IDH1 mutant glioma patients using magnetic resonance spectroscopy. Journal of Neuro-Oncology, 107, 197-205. https://doi.org/10.1007/s11060-011-0737-8

Saito, K., Matsumoto, S., Takakusagi, Y., Matsuo, M., Morris, H. D., Lizak, M. J., Munasinghe, J. P., Devasahayan, N., Subramanian, S., Mitchell, J. B., & Krishna, M. C. (2015). 13C-MR spectroscopic imaging with hyperpolarized [1-13C]pyruvate detects early response to radiotherapy in SCC tumors and HT-29 tumors. Clinical Cancer Research, 21(22), 5073-5081. https://doi.org/10.1158/1078-0432.CCR-14-1717

Serkova, N. J., & Brown, M. S. (2012). Quantitative analysis in magnetic resonance spectroscopy: From metabolic profiling to in vivo biomarkers. Bioanalysis, 4(3), 321-341. https://doi.org/10.4155/bio.11.320

Serrao, E. M., & Brindle, K. M. (2016). Potential clinical roles for metabolic imaging with hyperpolarized [1-13C] pyruvate. Frontiers in Oncology, 6. https://doi.org/10.3389/fonc.2016.00059

Shah, A. (2025). Bismuth vanadate nanoparticles- Structure, synthesis, and emerging engineering applications. Biomedical Journal of Scientific & Technical Research, 62(3), 54728-54740.DOI: 10.26717/BJSTR.2025.62.009758

Shah, J., Sun, H., Qiao, Z., Sharif, T., & Gul, M. (2024). Enhanced degradation of methylene blue dye using hydrothermally synthesized nickel-doped strontium oxide catalysts. Brazilian Journal of Science, 3, 13-27. https://doi.org/10.14295/bjs.v3i12.663

Sharma, U., & Jagannathan, N. R. (2022). Magnetic resonance imaging (MRI) and MR spectroscopic methods in understanding breast cancer biology and metabolism. Metabolites, 12(4), 295. https://doi.org/10.3390/metabo12040295

Sun, H., del Toro, F., Makki, M., Tenllado, F., & Canto, T. (2022). Adaptation of a potyvirus chimera increases its virulence in a compatible host through changes in HCPro. Plants, 11(17), 2262. https://doi.org/10.3390/plants11172262

Sun, H., Sohail, A., Shah, A., Liaqat, Z., Gul, M., & Liangyu. (2025). From precursors to performance: Diverse synthesis strategies for cerium oxide nanoparticles. Phytopharmacology Research Journal, 4(2), 28-42. https://ojs.prjn.org/index.php/prjn/article/view/82

Sun, H., Ciska, M., Makki, M., Tenllado, F., & Canto, T. (2024). Adaptive substitutions at two amino acids of HCPro modify its functional properties to separately increase the virulence of a potyviral chimera. Molecular Plant Pathology, 25(6), e13487. https://doi.org/10.1111/mpp.13487

Taglang, C., Batsios, G., Mukherjee, J., Tran, M., Gillespie, A. M., Hong, D., Romen, S. M., Pieper, R. O., & Viswanath, P. (2022). Deuterium magnetic resonance spectroscopy enables noninvasive metabolic imaging of tumor burden and response to therapy in low-grade gliomas. Neuro-Oncology, 24(7), 1101-1112. https://doi.org/10.1093/neuonc/noac022

Tiwari, V., Daoud, E. V., Hatanpaa, K. J., Gao, A., Zhang, S., An, Z., Ganji, S. K., Madden, C. J., Sherry, A. D., Malloy, C. R., & Choi, C. (2020). Glycine by MR spectroscopy is an imaging biomarker of glioma aggressiveness. Neuro-Oncology, 22(7), 1018-1029. https://doi.org/10.1093/neuonc/noaa034

Viswanath, P., Radoul, M., Izquierdo-Garcia, J. L., Luchman, H. A., Cairncross, J. G., Pieper, R. O., Phillips, J. J., & Ronen, S. M. (2018). Mutant IDH1 gliomas downregulate phosphocholine and phosphoethanolamine synthesis in a 2-hydroxyglutarate-dependent manner. Cancer & Metabolism, 6. https://doi.org/10.1186/s40170-018-0178-3

Wang, W., Gao, R., Zhang, L., Dong, Y., Du, Y., Dai, Q., Liu, B., Fang, C., Wang, C., Sun, H., Tong, F., & Huang, W. (2026). Precisely targeting cardiac remodeling nanomachine for restarting healthy heartbeat. Advanced Healthcare Materials, 15(3), e03478. https://doi.org/10.1002/adhm.202503478

Ward, C. S., Venkatesh, H. A., Chaumeil, M. M., Brandes, A. H., VanCriekinge, M., Dafni, H., Sukumar, S., Nelson, S. J., Vigneron, D. B., Hass-Kogan, D. A. & Ronen, S. M. (2010). Noninvasive detection of target modulation following phosphatidylinositol 3-Kinase inhibition using hyperpolarized 13C magnetic resonance spectroscopy. Cancer Research, 70(4), 1296-1305. https://doi.org/10.1158/0008-5472.CAN-09-2251

Woitek, R., & Gallagher, F. A. (2021). The use of hyperpolarised 13C-MRI in clinical body imaging to probe cancer metabolism. British Journal of Cancer, 124, 1187-1198. https://doi.org/10.1038/s41416-020-01224-6

Zeng, X., & Sun, H. (2025). Enhancing the understanding of complement protein changes in RVO: Insights and suggestions [Letter]. Journal of Inflammation Research, 18(0), 2781-2782. https://doi.org/10.2147/JIR.S522685

Zhang, B., Zhang, H., Zhu, L., Weng, X., & Sun, H. (2026). Microplastics in the human gastrointestinal system: A comprehensive study of metabolic risks and digestive toxicity. Msi Journal of Multidisciplinary Research, 3(4), 1-31. https://zenodo.org/records/19660158

Zhang, P., Liao, L., Dong, X., Peng, Z., Huang, S., Guo, D., & Sun, H. (2026). Mechanistic insights into Ginkgo biloba extract's anti-inflammatory effects in COPD: Regulation of Th1/Th2 balance via the p38 MAPK pathway. Current Medicinal Chemistry. https://doi.org/10.2174/0109298673432300260606201036

Zhu, H., Sun, H., Dai, J., Hao, J., & Zhou, B. (2024). Chitosan-based hydrogels in cancer therapy: Drug and gene delivery, stimuli-responsive carriers, phototherapy and immunotherapy. International Journal of Biological Macromolecules, 282(2), 137047. https://doi.org/10.1016/j.ijbiomac.2024.137047

Zhu, H., Sun, H., Dai, J., Hao, J., & Zhou, B. (2024). Chitosan-based hydrogels in cancer therapy: Drug and gene delivery, stimuli-responsive carriers, phototherapy and immunotherapy. International Journal of Biological Macromolecules, 282(2), 137047. https://doi.org/10.1016/j.ijbiomac.2024.137047

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2026-08-16

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Sun, H., Dong, X., Yu, J., Teng, Y., Guo, G., Li, L., … Xu, K. (2026). Functional imaging beyond anatomy: The emerging role of magnetic resonance spectroscopy and metabolic biomarkers in monitoring targeted cancer therapy. Brazilian Journal of Science, 6(3), 13–31. https://doi.org/10.14295/bjs.v6i3.963

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Vol. 6 No. 3 (2027): Brazilian Journal of Science

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Health Sciences

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Copyright (c) 2027 Hao Sun, Xiaojun Dong, Jinze Yu, Yuanyin Teng, Gaoyang Guo, Liangyu Li, Wei Wang, Bing Liu, Yuquan Li, Keyang Xu

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