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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 10439

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Special Issue Editors

Dr. Qiuhong He

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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

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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.

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Keywords

magnetic resonance
cancer
MRI
MRS
MRSI
NMR
DWI
MR
molecular imaging

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Published Papers (7 papers)

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Research

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20 pages, 12298 KiB

Open AccessArticle

Impact of Metastatic Microenvironment on Physiology and Metabolism of Small Cell Neuroendocrine Prostate Cancer Patient-Derived Xenografts

by Shubhangi Agarwal, Deepti Upadhyay, Jinny Sun, Emilie Decavel-Bueff, Robert A. Bok, Romelyn Delos Santos, Said Al Muzhahimi, Rosalie Nolley, Jason Crane, John Kurhanewicz, Donna M. Peehl and Renuka Sriram

Cancers 2025, 17(14), 2385; https://doi.org/10.3390/cancers17142385 - 18 Jul 2025

Viewed by 393

Abstract

Background: Potent androgen receptor pathway inhibitors induce small cell neuroendocrine prostate cancer (SCNC), a highly aggressive subtype of metastatic androgen deprivation-resistant prostate cancer (ARPC) with limited treatment options and poor survival rates. Patients with metastases in the liver have a poor prognosis relative to those with bone metastases alone. The mechanisms that underlie the different behavior of ARPC in bone vs. liver may involve factors intrinsic to the tumor cell, tumor microenvironment, and/or systemic factors, and identifying these factors is critical to improved diagnosis and treatment of SCNC. Metabolic reprogramming is a fundamental strategy of tumor cells to colonize and proliferate in microenvironments distinct from the primary site. Understanding the metabolic plasticity of cancer cells may reveal novel approaches to imaging and treating metastases more effectively. Methods: Using magnetic resonance (MR) imaging and spectroscopy, we interrogated the physiological and metabolic characteristics of SCNC patient-derived xenografts (PDXs) propagated in the bone and liver, and used correlative biochemical, immunohistochemical, and transcriptomic measures to understand the biological underpinnings of the observed imaging metrics. Results: We found that the influence of the microenvironment on physiologic measures using MRI was variable among PDXs. However, the MR measure of glycolytic capacity in the liver using hyperpolarized ¹³C pyruvic acid recapitulated the enzyme activity (lactate dehydrogenase), cofactor (nicotinamide adenine dinucleotide), and stable isotope measures of fractional enrichment of lactate. While in the bone, the congruence of the glycolytic components was lost and potentially weighted by the interaction of cancer cells with osteoclasts/osteoblasts. Conclusion: While there was little impact of microenvironmental factors on metabolism, the physiological measures (cellularity and perfusion) are highly variable and necessitate the use of combined hyperpolarized ¹³C MRI and multiparametric (anatomic, diffusion-, and perfusion- weighted) ¹H MRI to better characterize pre-treatment tumor characteristics, which will be crucial to evaluate treatment response. Full article

(This article belongs to the Special Issue Magnetic Resonance in Cancer Research)

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15 pages, 2259 KiB

Open AccessArticle

Correlation Between Neurocognitive Outcomes and Neuroaxonal Connectome Alterations After Whole Brain Radiotherapy: A Proof-of-Concept Study

by Sreenija Yarlagadda, Starlie Belnap, John Candela, Tugce Kutuk, Thailin Companioni Reyes, Miguel Ramirez Menendez, Matthew Hall, Robert Press, Yazmin Odia, Minesh Mehta, Michael McDermott and Rupesh Kotecha

Cancers 2025, 17(11), 1752; https://doi.org/10.3390/cancers17111752 - 23 May 2025

Viewed by 867

Abstract

Background/Objectives: Connectomics is an evolving branch of neuroscience that determines structural and functional connectivity in the brain. The objective of this prospective imaging study is to evaluate the effect of whole brain radiotherapy (WBRT) on the connectome. Methods: A combination of diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) was used to study the structural and functional connectivity of the brain, and a machine learning algorithm trained to analyze subject-specific data was applied to create individualized brain maps with 15 neuronal networks for each patient. These brain maps were compared to normal brains from the human connectome project, producing an anomaly matrix. Connectome analysis and multi-dimensional neurocognitive testing on a web-based platform were performed at baseline and 3 months post-WBRT. The change in anomaly frequency was co-related with neurocognitive outcomes. Results: At baseline, connectome analysis revealed that the multiple demand network had the most anomalies (46%). Pre- and post-WBRT comparison revealed increases in proportional anomaly frequency across multiple networks. Pearson correlation showed correlation between neurocognitive domain decline and anomaly changes: learning and memory domain with subcortical network [Verbal recall (Pearson coefficient −0.94; p < 0.01), verbal revision (Pearson coefficient −0.89; p = 0.01), and verbal recognition (Pearson coefficient −0.94; p < 0.01)]. Conclusions: This proof-of-concept study integrated data from DTI and fMRI in the form of connectome and revealed significant changes in brain connectivity, with WBRT that also correlated with neurocognitive outcomes. Further studies in a larger cohort are underway, and correlations with white matter changes and tumor locations/numbers will be performed. Full article

(This article belongs to the Special Issue Magnetic Resonance in Cancer Research)

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22 pages, 9669 KiB

Open AccessArticle

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 2606

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1296

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: 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 k_PL as a marker of metabolic activity, and the ¹³C-pyruvate signal-to-noise ratio (SNR_Pyr) 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 SNR_Pyr and k_PL 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

Open AccessArticle

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 1020

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 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

Open AccessArticle

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 5 | Viewed by 2440

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 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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Review

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23 pages, 6719 KiB

Open AccessReview

Potential of Metabolic MRI to Address Unmet Clinical Needs in Localised Kidney Cancer

by Ines Horvat-Menih, Grant D. Stewart and Ferdia A. Gallagher

Cancers 2025, 17(11), 1773; https://doi.org/10.3390/cancers17111773 - 26 May 2025

Viewed by 711

Abstract

Renal cell carcinoma (RCC) is a major global health issue with an increasing incidence and mortality rate. Current diagnostic methods are either invasive or limited in their ability to accurately differentiate between benign and malignant tumours and to predict early treatment response. This can lead to incorrect diagnosis, delayed treatment, patient anxiety, and suboptimal outcomes. RCC subtypes are known to exhibit distinct metabolic alterations, for example in glucose metabolism. These metabolic phenotypes offer potential targets for non-invasive imaging techniques to improve diagnosis and treatment, but current clinically available metabolic imaging tools such as ¹⁸F-FDG-PET and ^99mTc-sestamibi SPECT have limitations. Therefore, new approaches are required to assess this metabolism, and novel metabolic MRI techniques including hyperpolarised [1-¹³C]pyruvate MRI and deuterium metabolic imaging offer promising alternatives. These techniques are non-radioactive, demonstrate spatial metabolic heterogeneity, and can probe metabolic flux beyond tracer uptake. This review aims to explore the potential of metabolic MRI in the clinical management of RCC by (1) summarising current clinical guidelines; (2) reviewing metabolic heterogeneity across RCC subtypes; (3) discussing the potential of metabolic MRI to advance the understanding of in vivo metabolism; (4) and finally suggesting future directions for research in this field. Full article

(This article belongs to the Special Issue Magnetic Resonance in Cancer Research)

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