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Cancers
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Magnetic Resonance in Cancer Research
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Magnetic Resonance in Cancer Research

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: closed (15 April 2025) | Viewed by 5200

Special Issue Editors

Dr. Qiuhong He

E-Mail Website
Guest Editor
School of Health Sciences, Purdue University, West Lafayette, IN 47907, USA
Interests: magnetic resonance spectroscopic imaging technology for early detection of cancer treatment response; bacteria-based cancer therapy and imaging; cancer metastasis
Dr. Marie-France Vidaver

E-Mail Website
Guest Editor
Department of Radiology and Radiological Science, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
Interests: applying magnetic resonance imaging and magnetic resonance spectroscopy to study the tumor microenvironment; early detection of ovarian cancer; cancer cachexia; theranostic imaging

Special Issue Information

Dear Colleagues,

Since Rabi’s first experiment on nuclear magnetic resonance (NMR) in 1938, magnetic resonance has become an indispensable medical imaging modality in cancer diagnosis and treatment monitoring. NMR-based metabolomics is now employed for tumor metabolic profiling to evaluate patients’ response to cancer treatment. NMR is also a powerful tool in structural biology. The structural characterization of protein interactions with small molecules has led to novel drug designs.  

We are pleased to invite you to contribute to this Special Issue of Cancers entitled ”Magnetic Resonance in Cancer Research”, focusing on cancer imaging for cancer diagnosis and treatment monitoring, as well as cellular and molecular imaging for precision medicine in order to improve patient outcomes. This Special Issue aims to collect research articles addressing magnetic resonance in cancer research, including pre-clinical studies and clinical investigations on all cancer types. 

In this Special Issue, original research articles and reviews are welcome. Research areas may include, but are not limited to, the following: (a) clinical MRI of cancer and machine learning; (b) diffusion-weighted MRI (DWI) in cancer treatment assessment; (c) fast proton MRSI in cancer biomarker detection; (d) MRI evaluation of cardiotoxicity of cancer therapy; (e) PET/MRI of cancer; (f) MR characterization of tumor oxygenation; (g) molecular imaging of cancer; (h) hyperpolarization C-13 MRS in cancer metabolic imaging; (i) deuterium MRS in cancer biomarker imaging; and (j) F-19 MRI in cancer drug metabolism and cell tracking, etc.

We look forward to receiving your contributions.

Dr. Qiuhong He
Dr. Marie-France Vidaver
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • magnetic resonance
  • cancer
  • MRI
  • MRS
  • MRSI
  • NMR
  • DWI
  • MR
  • molecular imaging

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Published Papers (4 papers)

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Research

22 pages, 9669 KiB  
Article
Radiomic Profiling of Orthotopic Mouse Models of Glioblastoma Reveals Histopathological Correlations Associated with Tumour Response to Ionising Radiation
by Nicoleta Baxan, Richard Perryman, Maria V. Chatziathanasiadou and Nelofer Syed
Cancers 2025, 17(8), 1258; https://doi.org/10.3390/cancers17081258 - 8 Apr 2025
Viewed by 405
Abstract
Background: Glioblastoma (GB) is a particularly malignant brain tumour which carries a poor prognosis and presents limited treatment options. MRI is standard practice for differential diagnosis at initial presentation of GB and can assist in both treatment planning and response assessment. MRI radiomics [...] Read more.
Background: Glioblastoma (GB) is a particularly malignant brain tumour which carries a poor prognosis and presents limited treatment options. MRI is standard practice for differential diagnosis at initial presentation of GB and can assist in both treatment planning and response assessment. MRI radiomics allows for discerning GB features of clinical importance that are not evident by visual analysis, augmenting the morphological and functional tumour characterisation beyond traditional imaging techniques. Given that radiotherapy is part of the standard of care for GB patients, establishing a platform for phenotyping radiation treatment responses using non-invasive methods is of high relevance. Methods: In this study, we modelled the responses to ionising radiation across four orthotopic mouse models of GB using diffusion and perfusion radiomics. We have identified the optimal set of radiomic features that reflect tumour cellularity, microvascularity, and blood flow changes brought about by radiation treatment in these murine orthotopic models of GB, and directly compared them with endpoint histopathological analysis. Results: We showed that the selected radiomic features can quantify textural information and pixel interrelationships of tumour response to radiation therapy, revealing subtle image patterns that may reflect intra-tumoural spatial heterogeneity. When compared to GB patients, similarities in selected radiomic features were noted between orthotopic murine tumours and non-enhancing central tumour areas in patients, along with several discrepancies in tumour cellularity and vascularization, denoted by distinct grey level intensities and nonuniformity metrics. Conclusion: As the field evolves, radiomic profiling of GB may enhance the evaluation of targeted therapeutic strategies, accelerate the development of new therapies, and act as a potential virtual biopsy tool. Full article
(This article belongs to the Special Issue Magnetic Resonance in Cancer Research)
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17 pages, 7005 KiB  
Article
K-Means Clustering of Hyperpolarised 13C-MRI Identifies Intratumoral Perfusion/Metabolism Mismatch in Renal Cell Carcinoma as the Best Predictor of the Highest Grade
by Ines Horvat-Menih, Alixander S. Khan, Mary A. McLean, Joao Duarte, Eva Serrao, Stephan Ursprung, Joshua D. Kaggie, Andrew B. Gill, Andrew N. Priest, Mireia Crispin-Ortuzar, Anne Y. Warren, Sarah J. Welsh, Thomas J. Mitchell, Grant D. Stewart and Ferdia A. Gallagher
Cancers 2025, 17(4), 569; https://doi.org/10.3390/cancers17040569 - 7 Feb 2025
Viewed by 1017
Abstract
Background: Early and accurate grading of renal cell carcinoma (RCC) improves patient risk stratification and has implications for clinical management and mortality. However, current diagnostic approaches using imaging and renal mass biopsy have limited specificity and may lead to undergrading. Methods: [...] Read more.
Background: Early and accurate grading of renal cell carcinoma (RCC) improves patient risk stratification and has implications for clinical management and mortality. However, current diagnostic approaches using imaging and renal mass biopsy have limited specificity and may lead to undergrading. Methods: This study explored the use of hyperpolarised [1-13C]pyruvate MRI (HP 13C-MRI) to identify the most aggressive areas within the tumour of patients with clear cell renal cell carcinoma (ccRCC) as a method to guide biopsy targeting and to reduce undergrading. Six patients with ccRCC underwent presurgical HP 13C-MRI and conventional contrast-enhanced MRI. From the imaging data, three k-means clusters were computed by combining the kPL as a marker of metabolic activity, and the 13C-pyruvate signal-to-noise ratio (SNRPyr) as a perfusion surrogate. The combined clusters were compared to those derived from individual parameters and to those derived from the percentage of enhancement on the nephrographic phase (%NG). The diagnostic performance of each cluster was assessed based on its ability to predict the highest histological tumour grade in postsurgical tissue samples. The postsurgical tissue samples underwent immunohistochemical staining for the pyruvate transporter (monocarboxylate transporter 1, MCT1), as well as RNA and whole-exome sequencing. Results: The clustering approach combining SNRPyr and kPL demonstrated the best performance for predicting the highest tumour grade: specificity 85%; sensitivity 64%; positive predictive value 82%; and negative predictive value 68%. Epithelial MCT1 was identified as the major determinant of the HP 13C-MRI signal. The perfusion/metabolism mismatch cluster showed an increased expression of metabolic genes and markers of aggressiveness. Conclusions: This study demonstrates the potential of using HP 13C-MRI-derived metabolic clusters to identify intratumoral variations in tumour grade with high specificity. This work supports the use of metabolic imaging to guide biopsies to the most aggressive tumour regions and could potentially reduce sampling error. Full article
(This article belongs to the Special Issue Magnetic Resonance in Cancer Research)
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13 pages, 3667 KiB  
Article
An Accelerated Spectroscopic MRI Metabolite Quantification Based on a Deep Learning Method for Radiation Therapy Planning in Brain Tumor Patients
by Alexander S. Giuffrida, Karthik Ramesh, Sulaiman Sheriff, Andrew A. Maudsley, Brent D. Weinberg, Lee A. D. Cooper and Hyunsuk Shim
Cancers 2025, 17(3), 423; https://doi.org/10.3390/cancers17030423 - 27 Jan 2025
Viewed by 738
Abstract
Background: Spectroscopic MRI (sMRI) is a quantitative imaging technique that maps infiltrated tumors in the brain without contrast injections. In a previous study (NCT03137888), sMRI-guided radiation treatment extended patient survival, showing promise for clinical translation. The spectral fitting of individual voxels in an [...] Read more.
Background: Spectroscopic MRI (sMRI) is a quantitative imaging technique that maps infiltrated tumors in the brain without contrast injections. In a previous study (NCT03137888), sMRI-guided radiation treatment extended patient survival, showing promise for clinical translation. The spectral fitting of individual voxels in an sMRI dataset generate metabolite concentration maps that guide treatment. The established spectral analysis methods use iterative least-squares fitting (FITT) that are computationally demanding. This study compares the performance of NNFit, a neural network-based, accelerated spectral fitting model, to the established FITT for metabolite quantification and radiation treatment planning. Methods: NNFit is a self-supervised deep learning model trained on 50 ms echo-time (TE) sMRI data to estimate metabolite levels of choline (Cho), creatine (Cr), and NAA. We trained the model on 30 GBM patients (56 scans) and tested it on 17 GBM patients (29 scans). NNFit’s performance was compared to the FITT using structural similarity indices (SSIM) and the Dice coefficient. Results: NNFit significantly improved processing speed while maintaining strong agreement with FITT. The radiation target volumes defined by Cho/NAA ≥ 2x were visually comparable, with fewer artifacts in NNFit. Structural similarity indices (SSIM) indicated minimal bias and high consistency across methods. Conclusions: This study highlights NNFit’s potential for rapid, accurate, and artifact-reduced metabolic imaging, enabling faster radiotherapy planning. Full article
(This article belongs to the Special Issue Magnetic Resonance in Cancer Research)
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16 pages, 2581 KiB  
Article
Lonidamine Induced Selective Acidification and De-Energization of Prostate Cancer Xenografts: Enhanced Tumor Response to Radiation Therapy
by Stepan Orlovskiy, Pradeep Kumar Gupta, Jeffrey Roman, Fernando Arias-Mendoza, David S. Nelson, Cameron J. Koch, Vivek Narayan, Mary E. Putt and Kavindra Nath
Cancers 2024, 16(7), 1384; https://doi.org/10.3390/cancers16071384 - 31 Mar 2024
Cited by 4 | Viewed by 2146
Abstract
Prostate cancer is a multi-focal disease that can be treated using surgery, radiation, androgen deprivation, and chemotherapy, depending on its presentation. Standard dose-escalated radiation therapy (RT) in the range of 70–80 Gray (GY) is a standard treatment option for prostate cancer. It could [...] Read more.
Prostate cancer is a multi-focal disease that can be treated using surgery, radiation, androgen deprivation, and chemotherapy, depending on its presentation. Standard dose-escalated radiation therapy (RT) in the range of 70–80 Gray (GY) is a standard treatment option for prostate cancer. It could be used at different phases of the disease (e.g., as the only primary treatment when the cancer is confined to the prostate gland, combined with other therapies, or as an adjuvant treatment after surgery). Unfortunately, RT for prostate cancer is associated with gastro-intestinal and genitourinary toxicity. We have previously reported that the metabolic modulator lonidamine (LND) produces cancer sensitization through tumor acidification and de-energization in diverse neoplasms. We hypothesized that LND could allow lower RT doses by producing the same effect in prostate cancer, thus reducing the detrimental side effects associated with RT. Using the Seahorse XFe96 and YSI 2300 Stat Plus analyzers, we corroborated the expected LND-induced intracellular acidification and de-energization of isolated human prostate cancer cells using the PC3 cell line. These results were substantiated by non-invasive 31P magnetic resonance spectroscopy (MRS), studying PC3 prostate cancer xenografts treated with LND (100 mg/kg, i.p.). In addition, we found that LND significantly increased tumor lactate levels in the xenografts using 1H MRS non-invasively. Subsequently, LND was combined with radiation therapy in a growth delay experiment, where we found that 150 µM LND followed by 4 GY RT produced a significant growth delay in PC3 prostate cancer xenografts, compared to either control, LND, or RT alone. We conclude that the metabolic modulator LND radio-sensitizes experimental prostate cancer models, allowing the use of lower radiation doses and diminishing the potential side effects of RT. These results suggest the possible clinical translation of LND as a radio-sensitizer in patients with prostate cancer. Full article
(This article belongs to the Special Issue Magnetic Resonance in Cancer Research)
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