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Musetta L, Helsper S, Roosen L, Maes D, Croitor Sava A, Vanherp L, Gsell W, Vande Velde G, Lagrou K, Meyer W, Himmelreich U. Quantitative MRI of a Cerebral Cryptococcoma Mouse Model for In Vivo Distinction between Different Cryptococcal Molecular Types. J Fungi (Basel) 2024; 10:593. [PMID: 39194918 DOI: 10.3390/jof10080593] [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: 06/25/2024] [Revised: 08/13/2024] [Accepted: 08/15/2024] [Indexed: 08/29/2024] Open
Abstract
The controversially discussed taxonomy of the Cryptococcus neoformans/Cryptococcus gattii species complex encompasses at least eight major molecular types. Cerebral cryptococcomas are a common manifestation of cryptococcal neurological disease. In this study, we compared neurotypical symptoms and differential neurovirulence induced by one representative isolate for each of the eight molecular types studied. We compared single focal lesions caused by the different isolates and evaluated the potential relationships between the fungal burden and properties obtained with quantitative magnetic resonance imaging (qMRI) techniques such as diffusion MRI, T2 relaxometry and magnetic resonance spectroscopy (MRS). We observed an inverse correlation between parametric data and lesion density, and we were able to monitor longitudinally biophysical properties of cryptococcomas induced by different molecular types. Because the MRI/MRS techniques are also clinically available, the same approach could be used to assess image-based biophysical properties that correlate with fungal cell density in lesions in patients to determine personalized treatments.
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Affiliation(s)
- Luigi Musetta
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
| | - Shannon Helsper
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
| | - Lara Roosen
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
| | - Dries Maes
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
| | - Anca Croitor Sava
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
| | - Liesbeth Vanherp
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, 2000 Antwerp, Belgium
| | - Willy Gsell
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
| | - Greetje Vande Velde
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
| | - Katrien Lagrou
- Laboratory of Clinical Microbiology, Department of Microbiology, Immunology and Transplantation, KU Leuven, 3000 Leuven, Belgium
- Department of Laboratory Medicine, National Reference Center for Mycosis, UZ Leuven, 3000 Leuven, Belgium
| | - Wieland Meyer
- Westerdjjk Fungal Biodiversity Institute-KNAW, 3584 CT Utrecht, The Netherlands
| | - Uwe Himmelreich
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
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2
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Kader A, Snellings J, Adams LC, Gottheil P, Mangarova DB, Heyl JL, Kaufmann JO, Moeckel J, Brangsch J, Auer TA, Collettini F, Sauer F, Hamm B, Käs J, Sack I, Makowski MR, Braun J. Sensitivity of magnetic resonance elastography to extracellular matrix and cell motility in human prostate cancer cell line-derived xenograft models. BIOMATERIALS ADVANCES 2024; 161:213884. [PMID: 38723432 DOI: 10.1016/j.bioadv.2024.213884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 04/05/2024] [Accepted: 04/26/2024] [Indexed: 06/04/2024]
Abstract
Prostate cancer (PCa) is a significant health problem in the male population of the Western world. Magnetic resonance elastography (MRE), an emerging medical imaging technique sensitive to mechanical properties of biological tissues, detects PCa based on abnormally high stiffness and viscosity values. Yet, the origin of these changes in tissue properties and how they correlate with histopathological markers and tumor aggressiveness are largely unknown, hindering the use of tumor biomechanical properties for establishing a noninvasive PCa staging system. To infer the contributions of extracellular matrix (ECM) components and cell motility, we investigated fresh tissue specimens from two PCa xenograft mouse models, PC3 and LNCaP, using magnetic resonance elastography (MRE), diffusion-weighted imaging (DWI), quantitative histology, and nuclear shape analysis. Increased tumor stiffness and impaired water diffusion were observed to be associated with collagen and elastin accumulation and decreased cell motility. Overall, LNCaP, while more representative of clinical PCa than PC3, accumulated fewer ECM components, induced less restriction of water diffusion, and exhibited increased cell motility, resulting in overall softer and less viscous properties. Taken together, our results suggest that prostate tumor stiffness increases with ECM accumulation and cell adhesion - characteristics that influence critical biological processes of cancer development. MRE paired with DWI provides a powerful set of imaging markers that can potentially predict prostate tumor development from benign masses to aggressive malignancies in patients. STATEMENT OF SIGNIFICANCE: Xenograft models of human prostate tumor cell lines, allowing correlation of microstructure-sensitive biophysical imaging parameters with quantitative histological methods, can be investigated to identify hallmarks of cancer.
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Affiliation(s)
- Avan Kader
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany; Department of Biology, Chemistry and Pharmacy, Institute of Biology, Freie Universität Berlin, Königin-Luise-Str. 1-3, 14195 Berlin, Germany; Technical University of Munich, Department of Diagnostic and Interventional Radiology, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Joachim Snellings
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany.
| | - Lisa C Adams
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany; Technical University of Munich, Department of Diagnostic and Interventional Radiology, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Pablo Gottheil
- Peter Debye Institute for Soft Matter Physics, Leipzig University, Linnéstraße 5, 04103 Leipzig, Germany
| | - Dilyana B Mangarova
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany.
| | - Jennifer L Heyl
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany.
| | - Jan O Kaufmann
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany; Bundesanstalt für Materialforschung und -prüfung (BAM), Division 1.5 Protein Analysis, Richard-Willstätter-Str. 11, 12489 Berlin, Germany.
| | - Jana Moeckel
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany.
| | - Julia Brangsch
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany; Technical University of Munich, Department of Diagnostic and Interventional Radiology, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Timo A Auer
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany; Berlin Insitute of Health (BIH), Berlin, Germany.
| | - Federico Collettini
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany; Berlin Insitute of Health (BIH), Berlin, Germany.
| | - Frank Sauer
- Peter Debye Institute for Soft Matter Physics, Leipzig University, Linnéstraße 5, 04103 Leipzig, Germany.
| | - Bernd Hamm
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany.
| | - Josef Käs
- Peter Debye Institute for Soft Matter Physics, Leipzig University, Linnéstraße 5, 04103 Leipzig, Germany.
| | - Ingolf Sack
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany.
| | - Marcus R Makowski
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany; Technical University of Munich, Department of Diagnostic and Interventional Radiology, Ismaninger Str. 22, 81675 Munich, Germany; King's College London, School of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, Westminster Bridge Road, London SE1 7EH, United Kingdom.
| | - Jürgen Braun
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany.
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Pouliquen DL. The biophysics of water in cell biology: perspectives on a keystone for both marine sciences and cancer research. Front Cell Dev Biol 2024; 12:1403037. [PMID: 38803391 PMCID: PMC11128620 DOI: 10.3389/fcell.2024.1403037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
The biophysics of water, has been debated over more than a century. Although its importance is still underestimated, significant breakthroughs occurred in recent years. The influence of protein condensation on water availability control was documented, new findings on water-transport proteins emerged, and the way water molecules rearrange to minimize free energy at interfaces was deciphered, influencing membrane thermodynamics. The state of knowledge continued to progress in the field of deep-sea marine biology, highlighting unknown effects of high hydrostatic pressure and/or temperature on interactions between proteins and ligands in extreme environments, and membrane structure adaptations. The role of osmolytes in protein stability control under stress is also discussed here in relation to fish egg hydration/buoyancy. The complexity of water movements within the cell is updated, all these findings leading to a better view of their impact on many cellular processes. The way water flow and osmotic gradients generated by ion transport work together to produce the driving force behind cell migration is also relevant to both marine biology and cancer research. Additional common points concern water dynamic changes during the neoplastic transformation of cells and tissues, or embryo development. This could improve imaging techniques, early cancer diagnosis, and understanding of the molecular and physiological basis of buoyancy for many marine species.
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Affiliation(s)
- Daniel L. Pouliquen
- Inserm, CNRS, CRCINA, Nantes Université, University of Angers, Angers, France
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Wu Q, Yi Y, Lai B, Li J, Lian Y, Chen J, Wu Y, Wang X, Cao W. Texture analysis of apparent diffusion coefficient maps: can it identify nonresponse to neoadjuvant chemotherapy for additional radiation therapy in rectal cancer patients? Gastroenterol Rep (Oxf) 2024; 12:goae035. [PMID: 38651169 PMCID: PMC11035003 DOI: 10.1093/gastro/goae035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/15/2024] [Accepted: 03/26/2024] [Indexed: 04/25/2024] Open
Abstract
Background Neoadjuvant chemotherapy (NCT) alone can achieve comparable treatment outcomes to chemoradiotherapy in locally advanced rectal cancer (LARC) patients. This study aimed to investigate the value of texture analysis (TA) in apparent diffusion coefficient (ADC) maps for identifying non-responders to NCT. Methods This retrospective study included patients with LARC after NCT, and they were categorized into nonresponse group (pTRG 3) and response group (pTRG 0-2) based on pathological tumor regression grade (pTRG). Predictive texture features were extracted from pre- and post-treatment ADC maps to construct a TA model using RandomForest. The ADC model was developed by manually measuring pre- and post-treatment ADC values and calculating their changes. Simultaneously, subjective evaluations based on magnetic resonance imaging assessment of TRG were performed by two experienced radiologists. Model performance was compared using the area under the curve (AUC) and DeLong test. Results A total of 299 patients from two centers were divided into three cohorts: the primary cohort (center A; n = 194, with 36 non-responders and 158 responders), the internal validation cohort (center A; n = 49, with 9 non-responders) and external validation cohort (center B; n = 56, with 33 non-responders). The TA model was constructed by post_mean, mean_change, post_skewness, post_entropy, and entropy_change, which outperformed both the ADC model and subjective evaluations with an impressive AUC of 0.997 (95% confidence interval [CI], 0.975-1.000) in the primary cohort. Robust performances were observed in internal and external validation cohorts, with AUCs of 0.919 (95% CI, 0.805-0.978) and 0.938 (95% CI, 0.840-0.985), respectively. Conclusions The TA model has the potential to serve as an imaging biomarker for identifying nonresponse to NCT in LARC patients, providing a valuable reference for these patients considering additional radiation therapy.
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Affiliation(s)
- Qianyu Wu
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Yongju Yi
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Department of Information Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Bingjia Lai
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Jiao Li
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Yanbang Lian
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Junhong Chen
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, P. R. China
| | - Yue Wu
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Xinhua Wang
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Wuteng Cao
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
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Naghavi AO, Bryant JM, Kim Y, Weygand J, Redler G, Sim AJ, Miller J, Coucoules K, Michael LT, Gloria WE, Yang G, Rosenberg SA, Ahmed K, Bui MM, Henderson-Jackson EB, Lee A, Lee CD, Gonzalez RJ, Feygelman V, Eschrich SA, Scott JG, Torres-Roca J, Latifi K, Parikh N, Costello J. Habitat escalated adaptive therapy (HEAT): a phase 2 trial utilizing radiomic habitat-directed and genomic-adjusted radiation dose (GARD) optimization for high-grade soft tissue sarcoma. BMC Cancer 2024; 24:437. [PMID: 38594603 PMCID: PMC11003059 DOI: 10.1186/s12885-024-12151-7] [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: 11/25/2023] [Accepted: 03/20/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Soft tissue sarcomas (STS), have significant inter- and intra-tumoral heterogeneity, with poor response to standard neoadjuvant radiotherapy (RT). Achieving a favorable pathologic response (FPR ≥ 95%) from RT is associated with improved patient outcome. Genomic adjusted radiation dose (GARD), a radiation-specific metric that quantifies the expected RT treatment effect as a function of tumor dose and genomics, proposed that STS is significantly underdosed. STS have significant radiomic heterogeneity, where radiomic habitats can delineate regions of intra-tumoral hypoxia and radioresistance. We designed a novel clinical trial, Habitat Escalated Adaptive Therapy (HEAT), utilizing radiomic habitats to identify areas of radioresistance within the tumor and targeting them with GARD-optimized doses, to improve FPR in high-grade STS. METHODS Phase 2 non-randomized single-arm clinical trial includes non-metastatic, resectable high-grade STS patients. Pre-treatment multiparametric MRIs (mpMRI) delineate three distinct intra-tumoral habitats based on apparent diffusion coefficient (ADC) and dynamic contrast enhanced (DCE) sequences. GARD estimates that simultaneous integrated boost (SIB) doses of 70 and 60 Gy in 25 fractions to the highest and intermediate radioresistant habitats, while the remaining volume receives standard 50 Gy, would lead to a > 3 fold FPR increase to 24%. Pre-treatment CT guided biopsies of each habitat along with clip placement will be performed for pathologic evaluation, future genomic studies, and response assessment. An mpMRI taken between weeks two and three of treatment will be used for biological plan adaptation to account for tumor response, in addition to an mpMRI after the completion of radiotherapy in addition to pathologic response, toxicity, radiomic response, disease control, and survival will be evaluated as secondary endpoints. Furthermore, liquid biopsy will be performed with mpMRI for future ancillary studies. DISCUSSION This is the first clinical trial to test a novel genomic-based RT dose optimization (GARD) and to utilize radiomic habitats to identify and target radioresistance regions, as a strategy to improve the outcome of RT-treated STS patients. Its success could usher in a new phase in radiation oncology, integrating genomic and radiomic insights into clinical practice and trial designs, and may reveal new radiomic and genomic biomarkers, refining personalized treatment strategies for STS. TRIAL REGISTRATION NCT05301283. TRIAL STATUS The trial started recruitment on March 17, 2022.
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Affiliation(s)
- Arash O Naghavi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
| | - J M Bryant
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Youngchul Kim
- Department of Bioinformatics and Biostatistics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Joseph Weygand
- Department of Radiation Oncology and Applied Sciences, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA
| | - Gage Redler
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Austin J Sim
- Department of Radiation Oncology, James Cancer Hospital, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Justin Miller
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Kaitlyn Coucoules
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Lauren Taylor Michael
- Clinical Trials Office, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Warren E Gloria
- Department of Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - George Yang
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Stephen A Rosenberg
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Kamran Ahmed
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Marilyn M Bui
- Department of Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | - Andrew Lee
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Caitlin D Lee
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Ricardo J Gonzalez
- Department of Sarcoma, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Vladimir Feygelman
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Steven A Eschrich
- Department of Bioinformatics and Biostatistics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jacob G Scott
- Translational Hematology and Oncology Research, Radiation Oncology Department, Cleveland Clinic, Cleveland, OH, USA
| | - Javier Torres-Roca
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Kujtim Latifi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Nainesh Parikh
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - James Costello
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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Rabe M, Dietrich O, Forbrig R, Niyazi M, Belka C, Corradini S, Landry G, Kurz C. Repeatability quantification of brain diffusion-weighted imaging for future clinical implementation at a low-field MR-linac. Radiat Oncol 2024; 19:31. [PMID: 38448888 PMCID: PMC10916154 DOI: 10.1186/s13014-024-02424-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 02/26/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Longitudinal assessments of apparent diffusion coefficients (ADCs) derived from diffusion-weighted imaging (DWI) during intracranial radiotherapy at magnetic resonance imaging-guided linear accelerators (MR-linacs) could enable early response assessment by tracking tumor diffusivity changes. However, DWI pulse sequences are currently unavailable in clinical practice at low-field MR-linacs. Quantifying the in vivo repeatability of ADC measurements is a crucial step towards clinical implementation of DWI sequences but has not yet been reported on for low-field MR-linacs. This study assessed ADC measurement repeatability in a phantom and in vivo at a 0.35 T MR-linac. METHODS Eleven volunteers and a diffusion phantom were imaged on a 0.35 T MR-linac. Two echo-planar imaging DWI sequence variants, emphasizing high spatial resolution ("highRes") and signal-to-noise ratio ("highSNR"), were investigated. A test-retest study with an intermediate outside-scanner-break was performed to assess repeatability in the phantom and volunteers' brains. Mean ADCs within phantom vials, cerebrospinal fluid (CSF), and four brain tissue regions were compared to literature values. Absolute relative differences of mean ADCs in pre- and post-break scans were calculated for the diffusion phantom, and repeatability coefficients (RC) and relative RC (relRC) with 95% confidence intervals were determined for each region-of-interest (ROI) in volunteers. RESULTS Both DWI sequence variants demonstrated high repeatability, with absolute relative deviations below 1% for water, dimethyl sulfoxide, and polyethylene glycol in the diffusion phantom. RelRCs were 7% [5%, 12%] (CSF; highRes), 12% [9%, 22%] (CSF; highSNR), 9% [8%, 12%] (brain tissue ROIs; highRes), and 6% [5%, 7%] (brain tissue ROIs; highSNR), respectively. ADCs measured with the highSNR variant were consistent with literature values for volunteers, while smaller mean values were measured for the diffusion phantom. Conversely, the highRes variant underestimated ADCs compared to literature values, indicating systematic deviations. CONCLUSIONS High repeatability of ADC measurements in a diffusion phantom and volunteers' brains were measured at a low-field MR-linac. The highSNR variant outperformed the highRes variant in accuracy and repeatability, at the expense of an approximately doubled voxel volume. The observed high in vivo repeatability confirms the potential utility of DWI at low-field MR-linacs for early treatment response assessment.
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Affiliation(s)
- Moritz Rabe
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
| | - Olaf Dietrich
- Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Robert Forbrig
- Institute of Neuroradiology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Maximilian Niyazi
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, a Partnership Between DKFZ and LMU University Hospital Munich, Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
- Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
| | - Claus Belka
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, a Partnership Between DKFZ and LMU University Hospital Munich, Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
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7
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Davies-Jenkins CW, Döring A, Fasano F, Kleban E, Mueller L, Evans CJ, Afzali M, Jones DK, Ronen I, Branzoli F, Tax CMW. Practical considerations of diffusion-weighted MRS with ultra-strong diffusion gradients. Front Neurosci 2023; 17:1258408. [PMID: 38144210 PMCID: PMC10740196 DOI: 10.3389/fnins.2023.1258408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 11/03/2023] [Indexed: 12/26/2023] Open
Abstract
Introduction Diffusion-weighted magnetic resonance spectroscopy (DW-MRS) offers improved cellular specificity to microstructure-compared to water-based methods alone-but spatial resolution and SNR is severely reduced and slow-diffusing metabolites necessitate higher b-values to accurately characterize their diffusion properties. Ultra-strong gradients allow access to higher b-values per-unit time, higher SNR for a given b-value, and shorter diffusion times, but introduce additional challenges such as eddy-current artefacts, gradient non-uniformity, and mechanical vibrations. Methods In this work, we present initial DW-MRS data acquired on a 3T Siemens Connectom scanner equipped with ultra-strong (300 mT/m) gradients. We explore the practical issues associated with this manner of acquisition, the steps that may be taken to mitigate their impact on the data, and the potential benefits of ultra-strong gradients for DW-MRS. An in-house DW-PRESS sequence and data processing pipeline were developed to mitigate the impact of these confounds. The interaction of TE, b-value, and maximum gradient amplitude was investigated using simulations and pilot data, whereby maximum gradient amplitude was restricted. Furthermore, two DW-MRS voxels in grey and white matter were acquired using ultra-strong gradients and high b-values. Results Simulations suggest T2-based SNR gains that are experimentally confirmed. Ultra-strong gradient acquisitions exhibit similar artefact profiles to those of lower gradient amplitude, suggesting adequate performance of artefact mitigation strategies. Gradient field non-uniformity influenced ADC estimates by up to 4% when left uncorrected. ADC and Kurtosis estimates for tNAA, tCho, and tCr align with previously published literature. Discussion In conclusion, we successfully implemented acquisition and data processing strategies for ultra-strong gradient DW-MRS and results indicate that confounding effects of the strong gradient system can be ameliorated, while achieving shorter diffusion times and improved metabolite SNR.
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Affiliation(s)
- Christopher W. Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - André Döring
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- CIBM Center for Biomedical Imaging, EPFL CIBM-AIT, EPFL Lausanne, Lausanne, Switzerland
| | - Fabrizio Fasano
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Siemens Healthcare Ltd., Camberly, United Kingdom
| | - Elena Kleban
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Department of Radiology, Universität Bern, Bern, Switzerland
| | - Lars Mueller
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Leeds Institute of Cardiovascular & Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - C. John Evans
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Leeds Institute of Cardiovascular & Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Itamar Ronen
- Clinical Sciences Institue, Brighton and Sussex Medical School, Brighton, United Kingdom
| | - Francesca Branzoli
- Center for NeuroImaging Research (CENIR), Paris Brain Institute (ICM), Pitié-Salpêtrière Hospital, Paris, France
- Inserm U1127, CNRS U7225, Sorbonne Universités, Paris, France
| | - Chantal M. W. Tax
- Brain Research Imaging Centre, School Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
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8
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Guo Y, Ma Z, Pei D, Duan W, Guo Y, Liu Z, Guan F, Wang Z, Xing A, Guo Z, Luo L, Wang W, Yu B, Zhou J, Ji Y, Yan D, Cheng J, Liu X, Yan J, Zhang Z. Improving Noninvasive Classification of Molecular Subtypes of Adult Gliomas With Diffusion-Weighted MR Imaging: An Externally Validated Machine Learning Algorithm. J Magn Reson Imaging 2023; 58:1234-1242. [PMID: 36727433 DOI: 10.1002/jmri.28630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Genetic testing for molecular markers of gliomas sometimes is unavailable because of time-consuming and expensive, even limited tumor specimens or nonsurgery cases. PURPOSE To train a three-class radiomic model classifying three molecular subtypes including isocitrate dehydrogenase (IDH) mutations and 1p/19q-noncodeleted (IDHmut-noncodel), IDH wild-type (IDHwt), IDH-mutant and 1p/19q-codeleted (IDHmut-codel) of adult gliomas and investigate whether radiomic features from diffusion-weighted imaging (DWI) could bring additive value. STUDY TYPE Retrospective. POPULATION A total of 755 patients including 111 IDHmut-noncodel, 571 IDHwt, and 73 IDHmut-codel cases were divided into training (n = 480) and internal validation set (n = 275); 139 patients including 21 IDHmut-noncodel, 104 IDHwt, and 14 IDHmut-codel cases were utilized as external validation set. FIELD STRENGTH/SEQUENCE A 1.5 T or 3.0 T/multiparametric MRI, including T1-weighted (T1), T1-weighted gadolinium contrast-enhanced (T1c), T2-weighted (T2), fluid attenuated inversion recovery (FLAIR), and DWI. ASSESSMENT The performance of multiparametric radiomic model (random-forest model) using 22 selected features from T1, T2, FLAIR, T1c images and apparent diffusion coefficient (ADC) maps, and conventional radiomic model using 20 selected features from T1, T2, FLAIR, and T1c images was assessed in internal and external validation sets by comparing probability values and actual incidence. STATISTICAL TESTS Mann-Whitney U test, Chi-Squared test, Wilcoxon test, receiver operating curve (ROC), and area under the curve (AUC); DeLong analysis. P < 0.05 was statistically significant. RESULTS The multiparametric radiomic model achieved AUC values for IDHmut-noncodel, IDHwt, and IDHmut-codel of 0.8181, 0.8524, and 0.8502 in internal validation set and 0.7571, 0.7779, and 0.7491 in external validation set, respectively. Multiparametric radiomic model showed significantly better diagnostic performance after DeLong analysis, especially in classifying IDHwt and IDHmut-noncodel subtypes. DATA CONCLUSION Radiomic features from DWI could bring additive value and improve the performance of conventional MRI-based radiomic model for classifying the molecular subtypes especially IDHmut-noncodel and IDHwt of adult gliomas. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Yang Guo
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Department of Neurosurgery, The Henan Provincial People's Hospital, Zhengzhou, Henan, China
| | - Zeyu Ma
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Dongling Pei
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Wenchao Duan
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yu Guo
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zhongyi Liu
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Fangzhan Guan
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zilong Wang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Aoqi Xing
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zhixuan Guo
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Lin Luo
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Weiwei Wang
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Bin Yu
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jinqiao Zhou
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yuchen Ji
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Dongming Yan
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xianzhi Liu
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jing Yan
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zhenyu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
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Vanherp L, Poelmans J, Govaerts K, Hillen A, Lagrou K, Vande Velde G, Himmelreich U. In vivo assessment of differences in fungal cell density in cerebral cryptococcomas of mice infected with Cryptococcus neoformans or Cryptococcus gattii. Microbes Infect 2023; 25:105127. [PMID: 36940783 DOI: 10.1016/j.micinf.2023.105127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 03/07/2023] [Accepted: 03/15/2023] [Indexed: 03/22/2023]
Abstract
In cerebral cryptococcomas caused by Cryptococcus neoformans or Cryptococcus gattii, the density of fungal cells within lesions can contribute to the overall brain fungal burden. In cultures, cell density is inversely related to the size of the cryptococcal capsule, a dynamic polysaccharide layer surrounding the cell. Methods to investigate cell density or related capsule size within fungal lesions of a living host are currently unavailable, precluding in vivo studies on longitudinal changes. Here, we assessed whether intravital microscopy and quantitative magnetic resonance imaging techniques (diffusion MRI and MR relaxometry) would enable non-invasive investigation of fungal cell density in cerebral cryptococcomas in mice. We compared lesions caused by type strains C. neoformans H99 and C. gattii R265 and evaluated potential relations between observed imaging properties, fungal cell density, total cell and capsule size. The observed inverse correlation between apparent diffusion coefficient and cell density permitted longitudinal investigation of cell density changes. Using these imaging methods, we were able to study the multicellular organization and cell density within brain cryptococcomas in the intact host environment of living mice. Since the MRI techniques are also clinically available, the same approach could be used to assess fungal cell density in brain lesions of patients.
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Affiliation(s)
- Liesbeth Vanherp
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Jennifer Poelmans
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Kristof Govaerts
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Amy Hillen
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Katrien Lagrou
- Laboratory of Clinical Bacteriology and Mycology, Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium; National Reference Centre for Mycosis, Department of Laboratory Medicine, University Hospitals Leuven, Leuven, Belgium
| | - Greetje Vande Velde
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Uwe Himmelreich
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium.
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10
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Shi J, Li J, Li Z, Li Y, Xu L, Zhang Y. Prediction of pathological response grading for esophageal squamous carcinoma after neoadjuvant chemoradiotherapy based on MRI imaging using PDX. Front Oncol 2023; 13:1160815. [PMID: 37377911 PMCID: PMC10292012 DOI: 10.3389/fonc.2023.1160815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
Introduction To confirm the efficacy of magnetic resonance-diffusion weighted imaging (MR-DWI) in esophageal squamous cell carcinoma (ESCC) early pathological response prediction and assessment to neoadjuvant chemoradiotherapy (nCRT) using patient-derived xenografts (PDXs). Methods PDX-bearing mice were randomly divided into two groups: the experimental group receiving cisplatin combined with radiotherapy, whereas the control group receiving normal saline. MRI scans were performed in treatment groups in the before, middle, and end of treatment. The correlations between tumor volumes, ADC values and tumor pathological response at different time nodes were explored. Then, expression of proliferation marker and apoptotic marker were detected using immunohistochemistry, and apoptosis rate was detected by TUNEL assay to further verify the results observed in the PDX models. Results The ADC values of the experimental group were significantly higher than the control group in the both middle and end stage of treatment (all P< 0.001), however, significant difference was only observed in tumor volume at the end stage of treatment (P< 0.001). Furthermore, the △ADCmid-pre in our study may able to identify tumors with or without pCR to nCRT at an early stage, due to these changes were prior to the changes of tumor volume after treatment. Finally, TUNEL results also showed that the apoptosis rate of the experiment groups increased the most in the middle stage of treatment, especially the groups with pCR, but the highest apoptosis rate occurred in the end of the treatment. Further, the two PDX models with pCR exhibited the highest levels of apoptotic marker (Bax), and lowest levels of proliferation marker (PCNA and Ki-67) in the both middle and end stage of the treatment. Conclusions ADC values could be used to determine the tumor's response to nCRT, especially in the middle stages of treatment and before the tumor tissue morphology changes, and further, the ADC values were consistent with the potential biomarkers reflecting histopathological changes. Therefore, we suggest that radiation oncologists could refer to the ADC values in the middle stages of treatment when predicting the tumor histopathological response to n CRT in patients with ESCC.
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Affiliation(s)
- Jingzhen Shi
- Department of Oncology, The Second Hospital of Tianjin Medical University, Tianjin, China
- School of Medicine, Shandong University, Jinan, China
| | - Jianbin Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Zhenxiang Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yankang Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Liang Xu
- Department of Medical Imaging, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yingjie Zhang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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Brabec J, Englund E, Bengzon J, Szczepankiewicz F, van Westen D, Sundgren PC, Nilsson M. Coregistered histology sections with diffusion tensor imaging data at 200 µm resolution in meningioma tumors. Data Brief 2023; 48:109261. [PMID: 37383742 PMCID: PMC10294079 DOI: 10.1016/j.dib.2023.109261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/08/2023] [Accepted: 05/17/2023] [Indexed: 06/30/2023] Open
Abstract
A significant problem in diffusion MRI (dMRI) is the lack of understanding regarding which microstructural features account for the variability in the diffusion tensor imaging (DTI) parameters observed in meningioma tumors. A common assumption is that mean diffusivity (MD) and fractional anisotropy (FA) from DTI are inversely proportional to cell density and proportional to tissue anisotropy, respectively. Although these associations have been established across a wide range of tumors, they have been challenged for interpreting within-tumor variations where several additional microstructural features have been suggested as contributing to MD and FA. To facilitate the investigation of the biological underpinnings of DTI parameters, we performed ex-vivo DTI at 200 µm isotropic resolution on sixteen excised meningioma tumor samples. The samples exhibit a variety of microstructural features because the dataset includes meningiomas of six different meningioma types and two different grades. Diffusion-weighted signal (DWI) maps, DWI maps averaged over all directions for given b-value, signal intensities without diffusion encoding (S0) as well as DTI parameters: MD, FA, in-plane FA (FAIP), axial diffusivity (AD) and radial diffusivity (RD), were coregistered to Hematoxylin & Eosin- (H&E) and Elastica van Gieson-stained (EVG) histological sections by a non-linear landmark-based approach. Here, we provide DWI signal and DTI maps coregistered to histology sections and describe the pipeline for processing the raw DTI data and the coregistration. The raw, processed, and coregistered data are hosted by Analytic Imaging Diagnostics Arena (AIDA) data hub registry, and software tools for processing are provided via GitHub. We hope that data can be used in research and education concerning the link between the meningioma microstructure and parameters obtained by DTI.
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Affiliation(s)
- Jan Brabec
- Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | | | - Johan Bengzon
- Division of Neurosurgery, Skane University Hospital, Lund, Sweden
- Stem Cell Center, Department of Clinical Sciences, Lund, Sweden
| | | | | | - Pia C. Sundgren
- Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden
- Lund University Bioimaging Centre, Lund University, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Markus Nilsson
- Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden
- Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden
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12
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Lopez-Rueda A, Puig J, Thió-Henestrosa S, Moreno-Negrete JL, Zwanzger C, Pujol T, Aldecoa I, Pineda E, Valduvieco I, González JJ, Oleaga L. Texture Analysis of the Apparent Diffusion Coefficient Focused on Contrast-Enhancing Lesions in Predicting Survival for Bevacizumab-Treated Patients with Recurrent Glioblastoma. Cancers (Basel) 2023; 15:cancers15113026. [PMID: 37296988 DOI: 10.3390/cancers15113026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/19/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
PURPOSE Glioblastoma often recurs after treatment. Bevacizumab increases progression-free survival in some patients with recurrent glioblastoma. Identifying pretreatment predictors of survival can help clinical decision making. Magnetic resonance texture analysis (MRTA) quantifies macroscopic tissue heterogeneity indirectly linked to microscopic tissue properties. We investigated the usefulness of MRTA in predicting survival in patients with recurrent glioblastoma treated with bevacizumab. METHODS We evaluated retrospective longitudinal data from 33 patients (20 men; mean age 56 ± 13 years) who received bevacizumab on the first recurrence of glioblastoma. Volumes of contrast-enhancing lesions segmented on postcontrast T1-weighted sequences were co-registered on apparent diffusion coefficient maps to extract 107 radiomic features. To assess the performance of textural parameters in predicting progression-free survival and overall survival, we used receiver operating characteristic curves, univariate and multivariate regression analysis, and Kaplan-Meier plots. RESULTS Longer progression-free survival (>6 months) and overall survival (>1 year) were associated with lower values of major axis length (MAL), a lower maximum 2D diameter row (m2Ddr), and higher skewness values. Longer progression-free survival was also associated with higher kurtosis, and longer overall survival with higher elongation values. The model combining MAL, m2Ddr, and skewness best predicted progression-free survival at 6 months (AUC 0.886, 100% sensitivity, 77.8% specificity, 50% PPV, 100% NPV), and the model combining m2Ddr, elongation, and skewness best predicted overall survival (AUC 0.895, 83.3% sensitivity, 85.2% specificity, 55.6% PPV, 95.8% NPV). CONCLUSIONS Our preliminary analyses suggest that in patients with recurrent glioblastoma pretreatment, MRTA helps to predict survival after bevacizumab treatment.
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Affiliation(s)
- Antonio Lopez-Rueda
- Department of Radiology (CDI), Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Josep Puig
- Department of Radiology (IDI) and IDIBGI Hospital Universitari de Girona Doctor Josep Trueta, 17190 Girona, Spain
| | - Santiago Thió-Henestrosa
- Department of Computer Science Applied Mathmatics and Statistics, University of Girona, 17003 Girona, Spain
| | | | | | - Teresa Pujol
- Department of Radiology (CDI), Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Iban Aldecoa
- Department of Anatomical Pathology, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Estela Pineda
- Translational Genomics and Targeted Therapeutics in Solid Tumors Group, Medical Oncology Department, Hospital Clínic de Barcelona, IDIBAPS, University of Barcelona, 08036 Barcelona, Spain
| | - Izaskun Valduvieco
- Radiotherapy Oncology Service, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - José Juan González
- Department of Neurosurgery, Laboratory of Experimental Oncological Neurosurgery, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Laura Oleaga
- Department of Radiology (CDI), Hospital Clínic de Barcelona, 08036 Barcelona, Spain
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Fritz V, Martirosian P, Machann J, Thorwarth D, Schick F. Soy lecithin: A beneficial substance for adjusting the ADC in aqueous solutions to the values of biological tissues. Magn Reson Med 2023; 89:1674-1683. [PMID: 36458695 DOI: 10.1002/mrm.29543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/31/2022] [Accepted: 11/11/2022] [Indexed: 12/04/2022]
Abstract
PURPOSE To test soy lecithin as a substance added to water for the construction of MRI phantoms with tissue-like diffusion coefficients. The performance of soy lecithin was assessed for the useable range of adjustable ADC values, the degree of non-Gaussian diffusion, simultaneous effects on relaxation times, and spectral signal properties. METHODS Aqueous soy lecithin solutions of different concentrations (0%, 0.5%, 1%, 2%, 3% …, 10%) and soy lecithin-agar gels were prepared and examined on a 3 Tesla clinical scanner at 18.5° ± 0.5°C. Echoplanar sequences (b values: 0-1000/3000 s/mm2 ) were applied for ADC measurements. Quantitative relaxometry and MRS were performed for assessment of T1 , T2 , and detectable spectral components. RESULTS The presence of soy lecithin significantly restricts the diffusion of water molecules and mimics the nearly Gaussian nature of diffusion observed in tissue (for b values <1000 s/mm2 ). ADC values ranged from 2.02 × 10-3 mm2 /s to 0.48 × 10-3 mm2 /s and cover the entire physiological range reported on biological tissue. Measured T1 /T2 values of pure lecithin solutions varied from 2685/2013 to 668/133 ms with increasing concentration. No characteristic signals of soy lecithin were observed in the MR spectrum. The addition of agar to the soy lecithin solutions allowed T2 values to be well adjusted to typical values found in parenchymal tissue without affecting the soy lecithin-controlled ADC value. CONCLUSION Soy lecithin is a promising substance for the construction of diffusion phantoms with tissue-like ADC values. It provides several advantages over previously proposed substances, in particular a wide range of adjustable ADC values, the lack of additional 1 H-signals, and the possibility to adjust ADC and T2 values (by adding agar) almost independently of each other.
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Affiliation(s)
- Victor Fritz
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany
| | - Petros Martirosian
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany
| | - Jürgen Machann
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany.,Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen, Tübingen, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
| | - Fritz Schick
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany.,Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
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14
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Meng T, Liu H, Liu J, Wang F, Xie C, Ke L, He H. The investigation of reduced field-of-view diffusion-weighted imaging (DWI) in patients with nasopharyngeal carcinoma: comparison with conventional DWI. Acta Radiol 2023; 64:2118-2125. [PMID: 36912041 DOI: 10.1177/02841851231159389] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
Abstract
BACKGROUND Field-of-view optimized and constrained undistorted single-shot imaging (FOCUS) is a new sequence that shows enhanced anatomical details, improving the diffusion-weighted (DW) images. PURPOSE To investigate the value of FOCUS diffusion-weighted imaging (DWI) in the evaluation of nasopharyngeal carcinoma (NPC) and compare it with the single-shot echo planner imaging (SS-EPI) DWI approach. MATERIAL AND METHODS A total of 87 patients with NPC underwent magnetic resonance imaging, including FOCUS and SS-EPI DWI sequences. The signal-to-noise ratio (SNR), signal-intensity ratio (SIR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values of the nasopharyngeal lesions were measured and compared. According to the clinical stages of patients, T and N were divided into early and advanced stage groups, respectively. The mean ADC values of the two techniques were computed, and the area under the curve (AUC) was estimated to calculate the diagnostic efficiency. RESULTS Subjective and objective image qualitative values of FOCUS were significantly higher than those of SS-EPI. The ADC values for FOCUS of early T and N stages were significantly lower than those of the advanced stages. CONCLUSION FOCUS provides significantly better image quality in NPC compared to SS-EPI, with lower ADC values for early-stage disease than late-stage disease.
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Affiliation(s)
- Tiebao Meng
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, 71067Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Huiming Liu
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, 71067Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Jinbo Liu
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, 71067Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Feixiang Wang
- Department of Thoracic Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, PR China
| | - Chuanmiao Xie
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, 71067Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Liangru Ke
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, 71067Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Haoqiang He
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, 71067Sun Yat-sen University Cancer Center, Guangzhou, PR China
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15
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Horvat N, El Homsi M, Miranda J, Mazaheri Y, Gollub MJ, Paroder V. Rectal MRI Interpretation After Neoadjuvant Therapy. J Magn Reson Imaging 2023; 57:353-369. [PMID: 36073323 PMCID: PMC9851947 DOI: 10.1002/jmri.28426] [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: 05/20/2022] [Revised: 08/23/2022] [Accepted: 08/25/2022] [Indexed: 02/01/2023] Open
Abstract
In recent years, several key advances in the management of locally advanced rectal cancer have been made, including the implementation of total mesorectal excision as the standard surgical approach; use of neoadjuvant chemoradiotherapy in selected patients with a high risk of local recurrence, and finally, adoption of organ preservation strategies, through either local excision or nonoperative management in selected patients with clinical complete response following neoadjuvant chemoradiotherapy. This review aims to shed light on the role of rectal MRI in the assessment of treatment response after neoadjuvant therapy, which is especially important given the growing feasibility of nonoperative management. First, an overview of current neoadjuvant therapies and response assessment based on digital rectal examination, endoscopy, and MRI will be provided. Second, the use of a high-quality restaging rectal MRI protocol will be presented. Third, a step-by-step approach to assessing treatment response on restaging rectal MRI following neoadjuvant treatment will be outlined, acknowledging challenges faced by radiologists during MRI interpretation. Finally, research related to response assessment will be discussed. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Maria El Homsi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo, Brazil
| | - Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marc J. Gollub
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Viktoriya Paroder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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16
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Effectiveness of simultaneous multislice accelerated readout-segmented echo planar imaging for the assessment of rectal cancer. Eur J Radiol 2023; 159:110649. [PMID: 36563564 DOI: 10.1016/j.ejrad.2022.110649] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 11/17/2022] [Accepted: 12/10/2022] [Indexed: 12/15/2022]
Abstract
PURPOSE To investigate the effectiveness of simultaneous multislice (SMS) accelerated readout-segmented echo planar imaging (RESOLVE) DWI for assessing rectal cancer in the clinic. METHOD Sixty consecutive histologically proven rectal cancer patients were enrolled. They all received MRI examinations, including both SMS-RESOLVE and RESOLVE sequences. Two readers visually assessed the overall image quality, distinction of anatomical structures, lesion conspicuity, and artifacts of two sequences by using a qualitative 4-point Likert scale. The quantitative ADC value, lesion contrast, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and temporal SNR (tSNR) were independently calculated in rectal cancer on the largest slice of the tumor. RESULTS The scan time was shortened from 3 min and 50 s to 1 min and 47 s. The interobserver agreement of visual and quantitative assessments between the two readers was good overall. There were no differences in overall image quality, lesion conspicuity or artifact scores between the two sequences in both readers (all p > 0.05). The lesion contrast (p = 0.013) was significantly higher in SMS-RESOLVE, and the CNR was similar in the two DWIs. The scores of distinctions of anatomical structures in SMS-RESOLVE were lower (all p < 0.05) in both readers. The SNR of SMS-RESOLVE was lower than that of RESOLVE (p = 0.004), and the tSNR of SMS-RESOLVE was significantly higher (p < 0.001). The ADC value of the tumor was lower in SMS-RESOLVE (p = 0.001), but the ADC values of the normal rectal wall showed no difference between the two DWIs. CONCLUSION SMS-RESOLVE allowed a substantial reduction in acquisition time while maintaining overall image quality and lesion conspicuity in rectal cancer. It also had a higher contrast of the lesion and a higher temporal SNR.
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17
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Caruso M, Stanzione A, Prinster A, Pizzuti LM, Brunetti A, Maurea S, Mainenti PP. Role of advanced imaging techniques in the evaluation of oncological therapies in patients with colorectal liver metastases. World J Gastroenterol 2023; 29:521-535. [PMID: 36688023 PMCID: PMC9850941 DOI: 10.3748/wjg.v29.i3.521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/25/2022] [Accepted: 01/04/2023] [Indexed: 01/12/2023] Open
Abstract
In patients with colorectal liver metastasis (CRLMs) unsuitable for surgery, oncological treatments, such as chemotherapy and targeted agents, can be performed. Cross-sectional imaging [computed tomography (CT), magnetic resonance imaging (MRI), 18-fluorodexoyglucose positron emission tomography with CT/MRI] evaluates the response of CRLMs to therapy, using post-treatment lesion shrinkage as a qualitative imaging parameter. This point is critical because the risk of toxicity induced by oncological treatments is not always balanced by an effective response to them. Consequently, there is a pressing need to define biomarkers that can predict treatment responses and estimate the likelihood of drug resistance in individual patients. Advanced quantitative imaging (diffusion-weighted imaging, perfusion imaging, molecular imaging) allows the in vivo evaluation of specific biological tissue features described as quantitative parameters. Furthermore, radiomics can represent large amounts of numerical and statistical information buried inside cross-sectional images as quantitative parameters. As a result, parametric analysis (PA) translates the numerical data contained in the voxels of each image into quantitative parameters representative of peculiar neoplastic features such as perfusion, structural heterogeneity, cellularity, oxygenation, and glucose consumption. PA could be a potentially useful imaging marker for predicting CRLMs treatment response. This review describes the role of PA applied to cross-sectional imaging in predicting the response to oncological therapies in patients with CRLMs.
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Affiliation(s)
- Martina Caruso
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
| | - Anna Prinster
- Institute of Biostructures and Bioimaging, National Research Council, Napoli 80131, Italy
| | - Laura Micol Pizzuti
- Institute of Biostructures and Bioimaging, National Research Council, Napoli 80131, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging, National Research Council, Napoli 80131, Italy
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18
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Rahbek S, Mahmood F, Tomaszewski MR, Hanson LG, Madsen KH. Decomposition-based framework for tumor classification and prediction of treatment response from longitudinal MRI. Phys Med Biol 2023; 68. [PMID: 36595245 DOI: 10.1088/1361-6560/acaa85] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022]
Abstract
Objective.In the field of radiation oncology, the benefit of MRI goes beyond that of providing high soft-tissue contrast images for staging and treatment planning. With the recent clinical introduction of hybrid MRI linear accelerators it has become feasible to map physiological parameters describing diffusion, perfusion, and relaxation during the entire course of radiotherapy, for example. However, advanced data analysis tools are required for extracting qualified prognostic and predictive imaging biomarkers from longitudinal MRI data. In this study, we propose a new prediction framework tailored to exploit temporal dynamics of tissue features from repeated measurements. We demonstrate the framework using a newly developed decomposition method for tumor characterization.Approach.Two previously published MRI datasets with multiple measurements during and after radiotherapy, were used for development and testing:T2-weighted multi-echo images obtained for two mouse models of pancreatic cancer, and diffusion-weighted images for patients with brain metastases. Initially, the data was decomposed using the novel monotonous slope non-negative matrix factorization (msNMF) tailored for MR data. The following processing consisted of a tumor heterogeneity assessment using descriptive statistical measures, robust linear modelling to capture temporal changes of these, and finally logistic regression analysis for stratification of tumors and volumetric outcome.Main Results.The framework was able to classify the two pancreatic tumor types with an area under curve (AUC) of 0.999,P< 0.001 and predict the tumor volume change with a correlation coefficient of 0.513,P= 0.034. A classification of the human brain metastases into responders and non-responders resulted in an AUC of 0.74,P= 0.065.Significance.A general data processing framework for analyses of longitudinal MRI data has been developed and applications were demonstrated by classification of tumor type and prediction of radiotherapy response. Further, as part of the assessment, the merits of msNMF for tumor tissue decomposition were demonstrated.
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Affiliation(s)
- Sofie Rahbek
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, DK-2800, Denmark
| | - Faisal Mahmood
- Department of Clinical Research, University of Southern Denmark, Odense, DK-5000, Denmark.,Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense C, DK-5000, Denmark
| | - Michal R Tomaszewski
- Translation Imaging Department, Merck & Co, West Point, PA, United States of America.,Cancer Physiology Department, H. Lee Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Dr, Tampa, FL 33612, United States of America
| | - Lars G Hanson
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, DK-2800, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, DK-2650, Denmark
| | - Kristoffer H Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, DK-2650, Denmark.,Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, DK-2800, Denmark
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19
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Brabec J, Friedjungová M, Vašata D, Englund E, Bengzon J, Knutsson L, Szczepankiewicz F, van Westen D, Sundgren PC, Nilsson M. Meningioma microstructure assessed by diffusion MRI: An investigation of the source of mean diffusivity and fractional anisotropy by quantitative histology. Neuroimage Clin 2023; 37:103365. [PMID: 36898293 PMCID: PMC10020119 DOI: 10.1016/j.nicl.2023.103365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/08/2023] [Accepted: 02/25/2023] [Indexed: 03/06/2023]
Abstract
BACKGROUND Mean diffusivity (MD) and fractional anisotropy (FA) from diffusion MRI (dMRI) have been associated with cell density and tissue anisotropy across tumors, but it is unknown whether these associations persist at the microscopic level. PURPOSE To quantify the degree to which cell density and anisotropy, as determined from histology, account for the intra-tumor variability of MD and FA in meningioma tumors. Furthermore, to clarify whether other histological features account for additional intra-tumor variability of dMRI parameters. MATERIALS AND METHODS We performed ex-vivo dMRI at 200 μm isotropic resolution and histological imaging of 16 excised meningioma tumor samples. Diffusion tensor imaging (DTI) was used to map MD and FA, as well as the in-plane FA (FAIP). Histology images were analyzed in terms of cell nuclei density (CD) and structure anisotropy (SA; obtained from structure tensor analysis) and were used separately in a regression analysis to predict MD and FAIP, respectively. A convolutional neural network (CNN) was also trained to predict the dMRI parameters from histology patches. The association between MRI and histology was analyzed in terms of out-of-sample (R2OS) on the intra-tumor level and within-sample R2 across tumors. Regions where the dMRI parameters were poorly predicted from histology were analyzed to identify features apart from CD and SA that could influence MD and FAIP, respectively. RESULTS Cell density assessed by histology poorly explained intra-tumor variability of MD at the mesoscopic level (200 μm), as median R2OS = 0.04 (interquartile range 0.01-0.26). Structure anisotropy explained more of the variation in FAIP (median R2OS = 0.31, 0.20-0.42). Samples with low R2OS for FAIP exhibited low variations throughout the samples and thus low explainable variability, however, this was not the case for MD. Across tumors, CD and SA were clearly associated with MD (R2 = 0.60) and FAIP (R2 = 0.81), respectively. In 37% of the samples (6 out of 16), cell density did not explain intra-tumor variability of MD when compared to the degree explained by the CNN. Tumor vascularization, psammoma bodies, microcysts, and tissue cohesivity were associated with bias in MD prediction based solely on CD. Our results support that FAIP is high in the presence of elongated and aligned cell structures, but low otherwise. CONCLUSION Cell density and structure anisotropy account for variability in MD and FAIP across tumors but cell density does not explain MD variations within the tumor, which means that low or high values of MD locally may not always reflect high or low tumor cell density. Features beyond cell density need to be considered when interpreting MD.
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Affiliation(s)
- Jan Brabec
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Magda Friedjungová
- Faculty of Information Technology, Czech Technical University in Prague, Prague, Czech Republic
| | - Daniel Vašata
- Faculty of Information Technology, Czech Technical University in Prague, Prague, Czech Republic
| | | | - Johan Bengzon
- Neurosurgery, Clinical Sciences, Lund University, Lund, Sweden
| | - Linda Knutsson
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | | | - Pia C Sundgren
- Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden; Lund University Bioimaging Centre, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Markus Nilsson
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden; Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden
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20
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Shahbazi-Gahrouei D, Aminolroayaei F, Nematollahi H, Ghaderian M, Gahrouei SS. Advanced Magnetic Resonance Imaging Modalities for Breast Cancer Diagnosis: An Overview of Recent Findings and Perspectives. Diagnostics (Basel) 2022; 12:2741. [PMID: 36359584 PMCID: PMC9689118 DOI: 10.3390/diagnostics12112741] [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: 10/08/2022] [Revised: 10/26/2022] [Accepted: 11/07/2022] [Indexed: 08/28/2023] Open
Abstract
Breast cancer is the most prevalent cancer among women and the leading cause of death. Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are advanced magnetic resonance imaging (MRI) procedures that are widely used in the diagnostic and treatment evaluation of breast cancer. This review article describes the characteristics of new MRI methods and reviews recent findings on breast cancer diagnosis. This review study was performed on the literature sourced from scientific citation websites such as Google Scholar, PubMed, and Web of Science until July 2021. All relevant works published on the mentioned scientific citation websites were investigated. Because of the propensity of malignancies to limit diffusion, DWI can improve MRI diagnostic specificity. Diffusion tensor imaging gives additional information about diffusion directionality and anisotropy over traditional DWI. Recent findings showed that DWI and DTI and their characteristics may facilitate earlier and more accurate diagnosis, followed by better treatment. Overall, with the development of instruments and novel MRI modalities, it may be possible to diagnose breast cancer more effectively in the early stages.
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Affiliation(s)
- Daryoush Shahbazi-Gahrouei
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Fahimeh Aminolroayaei
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Hamide Nematollahi
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Mohammad Ghaderian
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Sogand Shahbazi Gahrouei
- Department of Management, School of Humanities, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran
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21
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Zhang J, Xing X, Wang Q, Chen Y, Yuan H, Lang N. Preliminary study of monoexponential, biexponential, and stretched-exponential models of diffusion-weighted MRI and diffusion kurtosis imaging on differential diagnosis of spinal metastases and chordoma. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2022; 31:3130-3138. [PMID: 35648206 DOI: 10.1007/s00586-022-07269-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/03/2022] [Accepted: 05/15/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Quantitative comparison of diffusion parameters from various models of diffusion-weighted (DWI) and diffusion kurtosis (DKI) imaging for distinguishing spinal metastases and chordomas. METHODS DWI and DKI examinations were performed in 31 and 13 cases of spinal metastases and chordomas, respectively. DWI derived apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), water molecular distributed diffusion coefficient (DDC), and intravoxel water diffusion heterogeneity (α). DKI derived mean diffusivity (MD) and mean kurtosis (MK). Independent sample t-testing compared statistical differences among parameters. Sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were determined. Pearson correlation analysis evaluated the parameters' correlations. RESULTS ADC, D, f, DDC, α, and MD were significantly lower in spinal metastases than chordomas (all P < 0.05). MK was significantly higher in spinal metastases than chordomas (P < 0.05). D had the highest area under the ROC curve (AUC) of 0.886, greater than MD (AUC = 0.706) or DDC (AUC = 0.742) in differentiating the two tumors (both P < 0.05). Combining D with f and α statistically significantly increased the AUC for diagnosis (to 0.995) relative to D alone (P < 0.05). There was a certain correlation among DDC, ADC, and D (all P < 0.05). CONCLUSIONS Monoexponential, biexponential, and stretched-exponential models of DWI and DKI can potentially differentiate spinal metastases and chordomas. D combined with f and α performed best.
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Affiliation(s)
- Jiahui Zhang
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Xiaoying Xing
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Qizheng Wang
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Yongye Chen
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Ning Lang
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
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22
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Wang Y, Galante JR, Haroon A, Wan S, Afaq A, Payne H, Bomanji J, Adeleke S, Kasivisvanathan V. The future of PSMA PET and WB MRI as next-generation imaging tools in prostate cancer. Nat Rev Urol 2022; 19:475-493. [PMID: 35789204 DOI: 10.1038/s41585-022-00618-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/01/2022] [Indexed: 11/09/2022]
Abstract
Radiolabelled prostate-specific membrane antigen (PSMA)-based PET-CT has been shown in numerous studies to be superior to conventional imaging in the detection of nodal or distant metastatic lesions. 68Ga-PSMA PET-CT is now recommended by many guidelines for the detection of biochemically relapsed disease after radical local therapy. PSMA radioligands can also function as radiotheranostics, and Lu-PSMA has been shown to be a potential new line of treatment for metastatic castration-resistant prostate cancer. Whole-body (WB) MRI has been shown to have a high diagnostic performance in the detection and monitoring of metastatic bone disease. Prospective, randomized, multicentre studies comparing 68Ga-PSMA PET-CT and WB MRI for pelvic nodal and metastatic disease detection are yet to be performed. Challenges for interpretation of PSMA include tracer trapping in non-target tissues and also urinary excretion of tracers, which confounds image interpretation at the vesicoureteral junction. Additionally, studies have shown how long-term androgen deprivation therapy (ADT) affects PSMA expression and could, therefore, reduce tracer uptake and visibility of PSMA+ lesions. Furthermore, ADT of short duration might increase PSMA expression, leading to the PSMA flare phenomenon, which makes the accurate monitoring of treatment response to ADT with PSMA PET challenging. Scan duration, detection of incidentalomas and presence of metallic implants are some of the major challenges with WB MRI. Emerging data support the wider adoption of PSMA PET and WB MRI for diagnosis, staging, disease burden evaluation and response monitoring, although their relative roles in the standard-of-care management of patients are yet to be fully defined.
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Affiliation(s)
- Yishen Wang
- School of Clinical Medicine, University of Cambridge, Cambridge, UK. .,Barking, Havering and Redbridge University Hospitals NHS Trust, Romford, UK.
| | - Joao R Galante
- Department of Oncology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Athar Haroon
- Department of Nuclear Medicine, Barts Health NHS Trust, London, UK
| | - Simon Wan
- Institute of Nuclear Medicine, University College London, London, UK
| | - Asim Afaq
- Institute of Nuclear Medicine, University College London, London, UK.,Department of Radiology, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Heather Payne
- Department of Oncology, University College London Hospitals, London, UK
| | - Jamshed Bomanji
- Institute of Nuclear Medicine, University College London, London, UK
| | - Sola Adeleke
- Department of Oncology, Guy's and St Thomas' NHS Foundation Trust, London, UK.,School of Cancer & Pharmaceutical Sciences, King's College London, London, UK
| | - Veeru Kasivisvanathan
- Division of Surgery & Interventional Science, University College London, London, UK.,Department of Urology, University College London Hospitals NHS Foundation Trust, London, UK
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23
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Xiao Y, Li J, Zhong J, Chen D, Shi J, Jin H. Diagnostic Performance of Diffusion-Weighted Imaging for Colorectal Cancer Detection: An Updated Systematic Review and Meta-Analysis. Front Oncol 2022; 12:656095. [PMID: 35814462 PMCID: PMC9260027 DOI: 10.3389/fonc.2022.656095] [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: 01/20/2021] [Accepted: 05/23/2022] [Indexed: 11/24/2022] Open
Abstract
Background Magnetic resonance imaging (MRI), which uses strong magnetic fields and radio waves (radiofrequency energy) to make images, is one of the best imaging methods for soft tissues and can clearly display unique anatomical structures. Diffusion-weighted imaging (DWI) has been developed for identifying various malignant tumors. Aim To investigate the diagnostic value of DWI-MRI quantitative analysis in colorectal cancer detection. Methods The PubMed, Cochrane Library, and Embase databases were searched from inception to May 29, 2020. Studies published in English that used DWI-MRI for diagnosing colorectal cancer were included. Case reports, letters, reviews, and studies conducted in non-humans or in-vitro experiments were excluded. The pooled diagnostic odds ratio (DOR) and hierarchical summary receiver operating characteristic (HSROC) curves were computed for DWI, and the area under the curve (AUC) and associated standard error (SE) and 95% confidence intervals (CIs) were also used. Results In total, 15 studies with 1,655 participants were finally included in this meta-analysis. There were four prospective studies and 11 retrospective studies. Eight studies focused on rectal cancer, six on colorectal cancer, and one on colonic cancer. The performance of DWI-MRI for diagnosing colorectal cancer was accurate, with pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of 0.88 (95% CI = 0.85–0.91), 0.92 (95% CI = 0.91–0.94), 30.36 (95% CI = 11.05–83.43), and 0.44 (95% CI = 0.30–0.64), respectively. The DOR and HSROC curves were 121 (95% CI = 56–261) and 0.92 (λ: 4.79), respectively. Conclusion DWI showed high diagnostic accuracy for colorectal cancer detection. Further studies with large sample sizes and prospective design are needed to confirm these results.
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Affiliation(s)
- Yunfei Xiao
- Department of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Juan Li
- Department of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jiamei Zhong
- Department of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dequan Chen
- Department of Radiology, People’s Hospital of Chongqing Hechuan, Chongqing, China
| | - Jianbo Shi
- Department of Radiology, The Seventh People’s Hospital of Chongqing, Chongqing, China
| | - Hongrui Jin
- Department of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Hongrui Jin,
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24
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Du N, Zhou X, Mao R, Shu W, Xiao L, Ye Y, Xu X, Shen Y, Lin G, Fang X, Li S. Preoperative and Noninvasive Prediction of Gliomas Histopathological Grades and IDH Molecular Types Using Multiple MRI Characteristics. Front Oncol 2022; 12:873839. [PMID: 35712483 PMCID: PMC9196247 DOI: 10.3389/fonc.2022.873839] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/05/2022] [Indexed: 01/30/2023] Open
Abstract
Background and Purpose Gliomas are one of the most common tumors in the central nervous system. This study aimed to explore the correlation between MRI morphological characteristics, apparent diffusion coefficient (ADC) parameters and pathological grades, as well as IDH gene phenotypes of gliomas. Methods Preoperative MRI data from 166 glioma patients with pathological confirmation were retrospectively analyzed to compare the differences of MRI characteristics and ADC parameters between the low-grade and high-grade gliomas (LGGs vs. HGGs), IDH mutant and wild-type gliomas (IDHmut vs. IDHwt). Multivariate models were constructed to predict the pathological grades and IDH gene phenotypes of gliomas and the performance was assessed by the receiver operating characteristic (ROC) analysis. Results Two multivariable logistic regression models were developed by incorporating age, ADC parameters, and MRI morphological characteristics to predict pathological grades, and IDH gene phenotypes of gliomas, respectively. The Noninvasive Grading Model classified tumor grades with areas under the ROC curve (AUROC) of 0.934 (95% CI=0.895-0.973), sensitivity of 91.2%, and specificity of 78.6%. The Noninvasive IDH Genotyping Model differentiated IDH types with an AUROC of 0.857 (95% CI=0.787-0.926), sensitivity of 88.2%, and specificity of 63.8%. Conclusion MRI features were correlated with glioma grades and IDH mutation status. Multivariable logistic regression models combined with MRI morphological characteristics and ADC parameters may provide a noninvasive and preoperative approach to predict glioma grades and IDH mutation status.
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Affiliation(s)
- Ningfang Du
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China
| | - Xiaotao Zhou
- Department of Emergency, Changhai Hospital, Naval Medical University, Second Military Medical University, Shanghai, China
| | - Renling Mao
- Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Weiquan Shu
- Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Li Xiao
- Department of Pathology, Huadong Hospital, Fudan University, Shanghai, China
| | - Yao Ye
- Department of Pathology, Huadong Hospital, Fudan University, Shanghai, China
| | - Xinxin Xu
- Clinical Research Center for Gerontology, Huadong Hospital, Fudan University, Shanghai, China
| | - Yilang Shen
- Institute of Business Analytics, Adelphi University, Garden City, NY, United States
| | - Guangwu Lin
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China
| | - Xuhao Fang
- Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Shihong Li
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China
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Martins ML, Bordallo HN, Mamontov E. Water Dynamics in Cancer Cells: Lessons from Quasielastic Neutron Scattering. Medicina (B Aires) 2022; 58:medicina58050654. [PMID: 35630072 PMCID: PMC9145030 DOI: 10.3390/medicina58050654] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/04/2022] [Accepted: 05/10/2022] [Indexed: 12/14/2022] Open
Abstract
The severity of the cancer statistics around the globe and the complexity involving the behavior of cancer cells inevitably calls for contributions from multidisciplinary areas of research. As such, materials science became a powerful asset to support biological research in comprehending the macro and microscopic behavior of cancer cells and untangling factors that may contribute to their progression or remission. The contributions of cellular water dynamics in this process have always been debated and, in recent years, experimental works performed with Quasielastic neutron scattering (QENS) brought new perspectives to these discussions. In this review, we address these works and highlight the value of QENS in comprehending the role played by water molecules in tumor cells and their response to external agents, particularly chemotherapy drugs. In addition, this paper provides an overview of QENS intended for scientists with different backgrounds and comments on the possibilities to be explored with the next-generation spectrometers under construction.
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Affiliation(s)
- Murillo L. Martins
- Oak Ridge National Laboratory, Neutron Scattering Division, Oak Ridge, TN 37831, USA
- Correspondence: (M.L.M.); (E.M.)
| | - Heloisa N. Bordallo
- Niels Bohr Institute, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen, Denmark;
| | - Eugene Mamontov
- Oak Ridge National Laboratory, Neutron Scattering Division, Oak Ridge, TN 37831, USA
- Correspondence: (M.L.M.); (E.M.)
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Kornaropoulos EN, Zacharaki EI, Zerbib P, Lin C, Rahmouni A, Paragios N. Joint Deformable Image Registration and ADC Map Regularization: Application to DWI-based Lymphoma Classification. IEEE J Biomed Health Inform 2022; 26:3151-3162. [PMID: 35239496 DOI: 10.1109/jbhi.2022.3156009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The Apparent Diffusion Coefficient (ADC) is considered an important imaging biomarker contributing to the assessment of tissue microstructure and pathophysiology. It is calculated from Diffusion-Weighted Magnetic Resonance Imaging (DWI) by means of a diffusion model, usually without considering any motion during image acquisition. We propose a method to improve the computation of the ADC by coping jointly with both motion artifacts in whole-body DWI (through group-wise registration) and possible instrumental noise in the diffusion model. The proposed deformable registration method yielded on average the lowest ADC reconstruction error on data with simulated motion and diffusion. Moreover, our approach was applied on whole-body diffusion weighted images obtained with five different b-values from a cohort of 38 patients with histologically confirmed lymphomas of three different types (Hodgkin, diffuse large B-cell lymphoma and follicular lymphoma). Evaluation on the real data showed that ADC-based features, extracted using our joint optimization approach classified lymphomas with an accuracy of approximately 78.6\% (yielding a 11\% increase in respect to the standard features extracted from unregistered diffusion-weighted images). Furthermore, the correlation between diffusion characteristics and histopathological findings was higher than any other previous approach of ADC computation.
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Diffusion and perfusion imaging biomarkers of H3 K27M mutation status in diffuse midline gliomas. Neuroradiology 2022; 64:1519-1528. [PMID: 35083503 DOI: 10.1007/s00234-021-02857-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 11/08/2021] [Indexed: 10/19/2022]
Abstract
PURPOSE H3K27M-mutant diffuse midline gliomas (M-DMGs) exhibit a clinically aggressive course. We studied diffusion-weighted imaging (DWI) and perfusion (PWI) MRI features of DMG with the hypothesis that DWI-PWI metrics can serve as biomarkers for the prediction of the H3K27M mutation status in DMGs. METHODS A retrospective review of the institutional database (imaging and histopathology) of patients with DMG (July 2016 to July 2020) was performed. Tumoral apparent diffusion coefficient (ADC) and peritumoral ADC (PT ADC) values and their normalized values (nADC and nPT ADC) were computed. Perfusion data were analyzed with manual arterial input function (AIF) and leakage correction (LC) Boxerman-Weiskoff models. Normalized maximum relative CBV (rCBV) was evaluated. Intergroup analysis of the imaging variables was done between M-DMGs and wild-type (WT-DMGs) groups. RESULTS Ninety-four cases (M-DMGs-n = 48 (51%) and WT-DMGs-n = 46(49%)) were included. Significantly lower PT ADC (mutant-1.1 ± 0.33, WT-1.23 ± 0.34; P = 0.033) and nPT ADC (mutant-1.64 ± 0.48, WT-1.83 ± 0.54; P = 0.040) were noted in the M-DMGs. The rCBV (mutant-25.17 ± 27.76, WT-13.73 ± 14.83; P = 0.018) and nrCBV (mutant-3.44 ± 2.16, WT-2.39 ± 1.25; P = 0.049) were significantly higher in the M-DMGs group. Among thalamic DMGs, the min ADC, PT ADC, and nADC and nPT ADC were lower in M-DMGs while nrCBV (corrected and uncorrected) was significantly higher. Receiver operator characteristic curve analysis demonstrated that PT ADC (cut-off-1.245), nPT ADC (cut-off-1.853), and nrCBV (cut-off-1.83) were significant independent predictors of H3K27M mutational status in DMGs. CONCLUSION DWI and PWI features hold value in preoperative prediction of H3K27M-mutation status in DMGs.
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Su X, Liu Y, Wang H, Chen N, Sun H, Yang X, Wang W, Zhang S, Wan X, Tan Q, Yue Q, Gong Q. Multimodal MR imaging signatures to identify brain diffuse midline gliomas with H3 K27M mutation. Cancer Med 2021; 11:1048-1058. [PMID: 34953060 PMCID: PMC8855915 DOI: 10.1002/cam4.4500] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 11/05/2021] [Accepted: 11/28/2021] [Indexed: 02/05/2023] Open
Affiliation(s)
- Xiaorui Su
- Huaxi MR Research Center (HMRRC) Department of Radiology West China Hospital of Sichuan University Chengdu China
- Huaxi Glioma Center West China Hospital of Sichuan University Chengdu China
- Research Unit of Psychoradiology Chinese Academy of Medical Sciences Chengdu China
| | - Yanhui Liu
- Huaxi Glioma Center West China Hospital of Sichuan University Chengdu China
- Department of Neurosurgery West China Hospital of Sichuan University Chengdu China
| | - Haoyu Wang
- Huaxi Glioma Center West China Hospital of Sichuan University Chengdu China
- Department of Neurosurgery West China Hospital of Sichuan University Chengdu China
| | - Ni Chen
- Huaxi Glioma Center West China Hospital of Sichuan University Chengdu China
- Department of Pathology West China Hospital of Sichuan University Chengdu China
| | - Huaiqiang Sun
- Huaxi MR Research Center (HMRRC) Department of Radiology West China Hospital of Sichuan University Chengdu China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province Chengdu China
| | - Xibiao Yang
- Department of Radiology West China Hospital of Sichuan University Chengdu China
| | - Weina Wang
- Huaxi MR Research Center (HMRRC) Department of Radiology West China Hospital of Sichuan University Chengdu China
- Department of Radiology The First Affiliated Hospital College of Medicine Zhejiang University Hangzhou Zhejiang China
| | - Simin Zhang
- Huaxi MR Research Center (HMRRC) Department of Radiology West China Hospital of Sichuan University Chengdu China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province Chengdu China
| | - Xinyue Wan
- Huaxi MR Research Center (HMRRC) Department of Radiology West China Hospital of Sichuan University Chengdu China
- Research Unit of Psychoradiology Chinese Academy of Medical Sciences Chengdu China
| | - Qiaoyue Tan
- Huaxi MR Research Center (HMRRC) Department of Radiology West China Hospital of Sichuan University Chengdu China
- Department of Radiotherapy West China Hospital of Sichuan University Chengdu China
| | - Qiang Yue
- Huaxi Glioma Center West China Hospital of Sichuan University Chengdu China
- Research Unit of Psychoradiology Chinese Academy of Medical Sciences Chengdu China
- Department of Radiology West China Hospital of Sichuan University Chengdu China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC) Department of Radiology West China Hospital of Sichuan University Chengdu China
- Research Unit of Psychoradiology Chinese Academy of Medical Sciences Chengdu China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province Chengdu China
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Hoff BA, Lemasson B, Chenevert TL, Luker GD, Tsien CI, Amouzandeh G, Johnson TD, Ross BD. Parametric Response Mapping of FLAIR MRI Provides an Early Indication of Progression Risk in Glioblastoma. Acad Radiol 2021; 28:1711-1720. [PMID: 32928633 DOI: 10.1016/j.acra.2020.08.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 08/11/2020] [Accepted: 08/13/2020] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES Glioblastoma image evaluation utilizes Magnetic Resonance Imaging contrast-enhanced, T1-weighted, and noncontrast T2-weighted fluid-attenuated inversion recovery (FLAIR) acquisitions. Disease progression assessment relies on changes in tumor diameter, which correlate poorly with survival. To improve treatment monitoring in glioblastoma, we investigated serial voxel-wise comparison of anatomically-aligned FLAIR signal as an early predictor of GBM progression. MATERIALS AND METHODS We analyzed longitudinal normalized FLAIR images (rFLAIR) from 52 subjects using voxel-wise Parametric Response Mapping (PRM) to monitor volume fractions of increased (PRMrFLAIR+), decreased (PRMrFLAIR-), or unchanged (PRMrFLAIR0) rFLAIR intensity. We determined response by rFLAIR between pretreatment and 10 weeks posttreatment. Risk of disease progression in a subset of subjects (N = 26) with stable disease or partial response as defined by Response Assessment in Neuro-Oncology (RANO) criteria was assessed by PRMrFLAIR between weeks 10 and 20 and continuously until the PRMrFLAIR+ exceeded a defined threshold. RANO defined criteria were compared with PRM-derived outcomes for tumor progression detection. RESULTS Patient stratification for progression-free survival (PFS) and overall survival (OS) was achieved at week 10 using RANO criteria (PFS: p <0.0001; OS: p <0.0001), relative change in FLAIR-hyperintense volume (PFS: p = 0.0011; OS: p <0.0001), and PRMrFLAIR+ (PFS: p <0.01; OS: p <0.001). PRMrFLAIR+ also stratified responding patients' progression between weeks 10 and 20 (PFS: p <0.05; OS: p = 0.01) while changes in FLAIR-volume measurements were not predictive. As a continuous evaluation, PRMrFLAIR+ exceeding 10% stratified patients for PFA after 5.6 months (p<0.0001), while RANO criteria did not stratify patients until 15.4 months (p <0.0001). CONCLUSION PRMrFLAIR may provide an early biomarker of disease progression in glioblastoma.
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Virostko J, Sorace AG, Slavkova KP, Kazerouni AS, Jarrett AM, DiCarlo JC, Woodard S, Avery S, Goodgame B, Patt D, Yankeelov TE. Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting. Breast Cancer Res 2021; 23:110. [PMID: 34838096 PMCID: PMC8627106 DOI: 10.1186/s13058-021-01489-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 11/17/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced breast cancer. METHODS Patients with stage II/III breast cancer (N = 28) were enrolled in a multicenter study performed in community radiology settings. Dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI data were acquired at four time points during the course of NAT. Estimates of the vascular perfusion and permeability, as assessed by the volume transfer rate (Ktrans) using the Patlak model, were generated from the DCE-MRI data while estimates of cell density, as assessed by the apparent diffusion coefficient (ADC), were calculated from DW-MRI data. Tumor volume was calculated using semi-automatic segmentation and combined with Ktrans and ADC to yield bulk tumor blood flow and cellularity, respectively. The percent change in quantitative parameters at each MRI scan was calculated and compared to pathological response at the time of surgery. The predictive accuracy of each MRI parameter at different time points was quantified using receiver operating characteristic curves. RESULTS Tumor size and quantitative MRI parameters were similar at baseline between groups that achieved pCR (n = 8) and those that did not (n = 20). Patients achieving a pCR had a larger decline in volume and cellularity than those who did not achieve pCR after one cycle of NAT (p < 0.05). At the third and fourth MRI, changes in tumor volume, Ktrans, ADC, cellularity, and bulk tumor flow from baseline (pre-treatment) were all significantly greater (p < 0.05) in the cohort who achieved pCR compared to those patients with non-pCR. CONCLUSIONS Quantitative analysis of DCE-MRI and DW-MRI can be implemented in the community care setting to accurately predict the response of breast cancer to NAT. Dissemination of quantitative MRI into the community setting allows for the incorporation of these parameters into the standard of care and increases the number of clinical community sites able to participate in novel drug trials that require quantitative MRI.
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Affiliation(s)
- John Virostko
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX, 78712, USA
- Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX, USA
- Department of Oncology, University of Texas at Austin, Austin, TX, USA
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
| | - Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Kalina P Slavkova
- Department of Physics, University of Texas at Austin, Austin, TX, USA
| | - Anum S Kazerouni
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
| | - Julie C DiCarlo
- Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX, USA
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
| | - Stefanie Woodard
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sarah Avery
- Austin Radiological Association, Austin, TX, USA
| | - Boone Goodgame
- Dell Seton Medical Center at the University of Texas, Austin, USA
| | | | - Thomas E Yankeelov
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX, 78712, USA.
- Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX, USA.
- Department of Oncology, University of Texas at Austin, Austin, TX, USA.
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA.
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA.
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA.
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31
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Morelli L, Buizza G, Palombo M, Riva G, Fontana G, Imparato S, Iannalfi A, Orlandi E, Paganelli C, Baroni G. Analysis of tumour microstructure estimation from conventional diffusion MRI and application to skull-base chordoma . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3761-3764. [PMID: 34892054 DOI: 10.1109/embc46164.2021.9630129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Skull-base chordoma (SBC) is a rare tumour whose molecular and radiological characteristics are still being investigated. In neuro-oncology microstructural imaging techniques, like diffusion-weighted MRI (DW-MRI), have been widely investigated, with the apparent diffusion coefficient (ADC) being one of the most used DW-MRI parameters due to its ease of acquisition and computation. ADC is a potential biomarker without a clear link to microstructure. The aim of this work was to derive microstructural information from conventional ADC, showing its potential for the characterisation of skull-base chordomas. Sixteen patients affected by SBC, who underwent conventional DW-MRI were retrospectively selected. From mono-exponential fits of DW-MRI, ADC maps were estimated using different sets of b-values. DW-MRI signals were simulated from synthetic substrates , which mimic the cellular packing of a tumour tissue with well-defined microstructural features. Starting from a published method, an error-driven procedure was evaluated to improve the estimates of microstructural parameters obtained through the simulated signals. A quantitative description of the tumour microstructure was then obtained from the DW-MRI images. This allowed successfully differentiating patients according to histologically-verified cell proliferation information.Clinical Relevance - The impact on cancer management derives from the expected improvement of radiation treatment quality tailored to a patient-specific non-invasive description of tumour microstructure.
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Hainc N, Alsafwani N, Gao A, O'Halloran PJ, Kongkham P, Zadeh G, Gutierrez E, Shultz D, Krings T, Alcaide-Leon P. The centrally restricted diffusion sign on MRI for assessment of radiation necrosis in metastases treated with stereotactic radiosurgery. J Neurooncol 2021; 155:325-333. [PMID: 34689307 PMCID: PMC8651583 DOI: 10.1007/s11060-021-03879-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 10/16/2021] [Indexed: 11/29/2022]
Abstract
Purpose Differentiation of radiation necrosis from tumor progression in brain metastases treated with stereotactic radiosurgery (SRS) is challenging. For this, we assessed the performance of the centrally restricted diffusion sign. Methods Patients with brain metastases treated with SRS who underwent a subsequent intervention (biopsy/resection) for a ring-enhancing lesion on preoperative MRI between 2000 and 2020 were included. Excluded were lesions containing increased susceptibility limiting assessment of DWI. Two neuroradiologists classified the location of the diffusion restriction with respect to the post-contrast T1 images as centrally within the ring-enhancement (the centrally restricted diffusion sign), peripherally correlating to the rim of contrast enhancement, both locations, or none. Measures of diagnostic accuracy and 95% CI were calculated for the centrally restricted diffusion sign. Cohen's kappa was calculated to identify the interobserver agreement. Results Fifty-nine patients (36 female; mean age 59, range 40 to 80) were included, 36 with tumor progression and 23 with radiation necrosis based on histopathology. Primary tumors included 34 lung, 12 breast, 5 melanoma, 3 colorectal, 2 esophagus, 1 head and neck, 1 endometrium, and 1 thyroid. The centrally restricted diffusion sign was seen in 19/23 radiation necrosis cases (sensitivity 83% (95% CI 63 to 93%), specificity 64% (95% CI 48 to 78%), PPV 59% (95% CI 42 to 74%), NPV 85% (95% CI 68 to 94%)) and 13/36 tumor progression cases (difference p < 0.001). Interobserver agreement was substantial, at 0.61 (95% CI 0.45 to 70.8). Conclusion We found a low probability of radiation necrosis in the absence of the centrally restricted diffusion sign.
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Affiliation(s)
- Nicolin Hainc
- Department of Medical Imaging, University of Toronto, Toronto, Canada. .,Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
| | - Noor Alsafwani
- Laboratory Medicine Program, University Health Network, Toronto, Canada.,Department of Pathology, College of Medicine, Imam Abdulrahman Bin Faisal University (IAU), Dammam, Saudi Arabia
| | - Andrew Gao
- Laboratory Medicine Program, University Health Network, Toronto, Canada
| | | | - Paul Kongkham
- Neurosurgery, University Health Network, Toronto, Canada
| | - Gelareh Zadeh
- Neurosurgery, University Health Network, Toronto, Canada
| | - Enrique Gutierrez
- Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | - David Shultz
- Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | - Timo Krings
- Department of Medical Imaging, University of Toronto, Toronto, Canada.,Joint Department of Medical Imaging, University Health Network, Toronto, Canada
| | - Paula Alcaide-Leon
- Department of Medical Imaging, University of Toronto, Toronto, Canada.,Joint Department of Medical Imaging, University Health Network, Toronto, Canada
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Lu N, Dong J, Fang X, Wang L, Jia W, Zhou Q, Wang L, Wei J, Pan Y, Han X. Predicting pathologic response to neoadjuvant chemotherapy in patients with locally advanced breast cancer using multiparametric MRI. BMC Med Imaging 2021; 21:155. [PMID: 34688263 PMCID: PMC8542288 DOI: 10.1186/s12880-021-00688-z] [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: 05/12/2021] [Accepted: 10/11/2021] [Indexed: 11/12/2022] Open
Abstract
Background This study aims to observe and analyze the effect of diffusion weighted magnetic resonance imaging (MRI) on the patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. Methods Fifty patients (mean age, 48.7 years) with stage II–III breast cancer who underwent neoadjuvant chemotherapy and preoperative MRI between 2016 and 2020 were retrospectively evaluated. The associations between preoperative breast MRI findings/clinicopathological features and outcomes of neoadjuvant chemotherapy were assessed. Results Clinical stage at baseline (OR: 0.104, 95% confidence interval (CI) 0.021–0.516, P = 0.006) and standard apparent diffusion coefficient (ADC) change (OR: 9.865, 95% CI 1.024–95.021, P = 0.048) were significant predictive factors of the effects of neoadjuvant chemotherapy. The percentage increase of standard ADC value in pathologic complete response (pCR) group was larger than that in non-pCR group at first time point (P < 0.05). A correlation was observed between the change in standard ADC values and tumor diameter at first follow-up (r: 0.438, P < 0.05). Conclusions Our findings support that change in standard ADC values and clinical stage at baseline can predict the effects of neoadjuvant chemotherapy for patients with breast cancer in early stage. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-021-00688-z.
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Affiliation(s)
- Nannan Lu
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China
| | - Jie Dong
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China.,Department of Medical Oncology, Anhui Provincial Hospital Affiliated To Anhui Medical University, Hefei, 230032, Anhui, China
| | - Xin Fang
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230031, Anhui, China
| | - Lufang Wang
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China
| | - Wei Jia
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China
| | - Qiong Zhou
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China.,Department of Medical Oncology, Anhui Provincial Hospital Affiliated To Anhui Medical University, Hefei, 230032, Anhui, China
| | - Lingyu Wang
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China
| | - Jie Wei
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China
| | - Yueyin Pan
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China
| | - Xinghua Han
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China.
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Lawrence LSP, Chan RW, Chen H, Keller B, Stewart J, Ruschin M, Chugh B, Campbell M, Theriault A, Stanisz GJ, MacKenzie S, Myrehaug S, Detsky J, Maralani PJ, Tseng CL, Czarnota GJ, Sahgal A, Lau AZ. Accuracy and precision of apparent diffusion coefficient measurements on a 1.5 T MR-Linac in central nervous system tumour patients. Radiother Oncol 2021; 164:155-162. [PMID: 34592363 DOI: 10.1016/j.radonc.2021.09.020] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/20/2021] [Accepted: 09/22/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND PURPOSE MRI linear accelerators (MR-Linacs) may allow treatment adaptation to be guided by quantitative MRI including diffusion-weighted imaging (DWI). The aim of this study was to evaluate the accuracy and precision of apparent diffusion coefficient (ADC) measurements from DWI on a 1.5 T MR-Linac in patients with central nervous system (CNS) tumours through comparison with a diagnostic scanner. MATERIALS AND METHODS CNS patients were treated using a 1.5 T Elekta Unity MR-Linac. DWI was acquired during MR-Linac treatment and on a Philips Ingenia 1.5 T. The agreement between the two scanners on median ADC over the gross tumour/clinical target volumes (GTV/CTV) and in brain regions (white/grey matter, cerebrospinal fluid (CSF)) was computed. Repeated scans were used to estimate ADC repeatability. Daily changes in ADC over the GTV of high-grade gliomas were characterized from MR-Linac scans. RESULTS DWI from 59 patients was analyzed. MR-Linac ADC measurements showed a small bias relative to Ingenia measurements in white matter, grey matter, GTV, and CTV (bias: -0.05 ± 0.03, -0.08 ± 0.05, -0.1 ± 0.1, -0.08 ± 0.07 μm2/ms). ADC differed substantially in CSF (bias: -0.5 ± 0.3 μm2/ms). The repeatability of MR-Linac ADC over white/grey matter was similar to previous reports (coefficients of variation for median ADC: 1.4%/1.8%). MR-Linac ADC changes in the GTV were detectable. CONCLUSIONS It is possible to obtain ADC measurements in the brain on a 1.5 T MR-Linac that are comparable to those of diagnostic-quality scanners. This technical validation study adds to the foundation for future studies that will correlate brain tumour ADC with clinical outcomes.
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Affiliation(s)
- Liam S P Lawrence
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Rachel W Chan
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada
| | - Hanbo Chen
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Brian Keller
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - James Stewart
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Mark Ruschin
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Brige Chugh
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada; Department of Physics, Ryerson University, Toronto, Canada
| | - Mikki Campbell
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Aimee Theriault
- Department of Medical Biophysics, University of Toronto, Toronto, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Neurosurgery and Paediatric Neurosurgery, Medical University, Lublin, Poland
| | - Greg J Stanisz
- Department of Medical Biophysics, University of Toronto, Toronto, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Neurosurgery and Paediatric Neurosurgery, Medical University, Lublin, Poland
| | - Scott MacKenzie
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Pejman J Maralani
- Medical Imaging, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Greg J Czarnota
- Department of Medical Biophysics, University of Toronto, Toronto, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Angus Z Lau
- Department of Medical Biophysics, University of Toronto, Toronto, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada.
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Ferjaoui R, Cherni MA, Boujnah S, Kraiem NEH, Kraiem T. Machine learning for evolutive lymphoma and residual masses recognition in whole body diffusion weighted magnetic resonance images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106320. [PMID: 34390938 DOI: 10.1016/j.cmpb.2021.106320] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 07/25/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND After the treatment of the patients with malignant lymphoma, there may persist lesions that must be labeled either as evolutive lymphoma requiring new treatments or as residual masses. We present in this work, a machine learning-based computer-aided diagnosis (CAD) applied to whole-body diffusion-weighted magnetic resonance images. METHODS The database consists of a total of 1005 MRI images with evolutive lymphoma and residual masses. More specifically, we propose a novel approach that leverages: (1)-The complementarity of the functional and anatomical criteria of MRI images through a fusion step based on the discrete wavelet transforms (DWT). (2)- The automatic segmentation of the lesions, their localization, and their enumeration using the Chan-Vese algorithm. (3)- The generation of the parametric image which contains the apparent diffusion coefficient value named ADC map. (4)- The features selection through the application of the sequential forward selection (SFS), Entropy, Symmetric uncertainty and Gain Ratio algorithm on 72 extracted features. (5)- The classification of the lesions by applying five well known supervised machine learning classification algorithms: the back-propagation artificial neural network (ANN), the support vector machine (SVM), the K-nearest neighbours (K-NN), Relevance Vectors Machine (RVM), and the random forest (RF) compared to deep learning based on convolutional neural network (CNN). Moreover, this study is achieved with an evaluation of the classification using 335 DW-MR images where 80% of them are used for the training and the remaining 20% for the test. RESULTS The obtained accuracy for the five classifiers recorded a slight superiority to the proposed method based on the back-propagation 3-9-1 ANN model which reaches 96,5%. In addition, we compared the proposed method to five other works from the literature. The proposed method gives much better results in terms of SE, SP, accuracy, F1-measure, and geometric-mean which reaches respectively 96.4%, 90.9%, 95.5%, 0.97, and 91.61%. CONCLUSIONS Our initial results suggest that Combining functional, anatomical, and morphological features of ROI's have very good accuracy (97.01%) for evolutive lymphoma and residual masses recognition when we based on the new proposed approach using the back-propagation 3-9-1 ANN model. Proposed method based on machine learning gives less than Deep learning CNN, which is 98.5%.
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Affiliation(s)
- Radhia Ferjaoui
- University of Tunis El Manar, Research Laboratory of biophysics and Medical technologies (LRBTM), ISTMT, Tunis, 1006, Tunisia.
| | - Mohamed Ali Cherni
- University of Tunis, LR13 ES03 SIME Laboratory, ENSIT, Montfleury 1008 Tunisia
| | - Sana Boujnah
- University of Tunis El Manar, National Engineering School of Tunis, Tunisia
| | | | - Tarek Kraiem
- University of Tunis El Manar, Faculty of Medicine of Tunis, Tunis, 1007, Tunisia; University of Tunis El Manar, Research Laboratory of biophysics and Medical technologies (LRBTM), ISTMT, Tunis, 1006, Tunisia
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36
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Ko CC, Yeh LR, Kuo YT, Chen JH. Imaging biomarkers for evaluating tumor response: RECIST and beyond. Biomark Res 2021; 9:52. [PMID: 34215324 PMCID: PMC8252278 DOI: 10.1186/s40364-021-00306-8] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 06/10/2021] [Indexed: 12/12/2022] Open
Abstract
Response Evaluation Criteria in Solid Tumors (RECIST) is the gold standard for assessment of treatment response in solid tumors. Morphologic change of tumor size evaluated by RECIST is often correlated with survival length and has been considered as a surrogate endpoint of therapeutic efficacy. However, the detection of morphologic change alone may not be sufficient for assessing response to new anti-cancer medication in all solid tumors. During the past fifteen years, several molecular-targeted therapies and immunotherapies have emerged in cancer treatment which work by disrupting signaling pathways and inhibited cell growth. Tumor necrosis or lack of tumor progression is associated with a good therapeutic response even in the absence of tumor shrinkage. Therefore, the use of unmodified RECIST criteria to estimate morphological changes of tumor alone may not be sufficient to estimate tumor response for these new anti-cancer drugs. Several studies have reported the low reliability of RECIST in evaluating treatment response in different tumors such as hepatocellular carcinoma, lung cancer, prostate cancer, brain glioma, bone metastasis, and lymphoma. There is an increased need for new medical imaging biomarkers, considering the changes in tumor viability, metabolic activity, and attenuation, which are related to early tumor response. Promising imaging techniques, beyond RECIST, include dynamic contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI), diffusion-weight imaging (DWI), magnetic resonance spectroscopy (MRS), and 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET). This review outlines the current RECIST with their limitations and the new emerging concepts of imaging biomarkers in oncology.
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Affiliation(s)
- Ching-Chung Ko
- Department of Medical Imaging, Chi Mei Medical Center, Tainan, Taiwan.,Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
| | - Lee-Ren Yeh
- Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Yu-Ting Kuo
- Department of Medical Imaging, Chi Mei Medical Center, Tainan, Taiwan.,Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Jeon-Hor Chen
- Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan. .,Tu & Yuan Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, 164 Irvine Hall, Irvine, CA, 92697 - 5020, USA.
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Pham TT, Liney G, Wong K, Henderson C, Rai R, Graham PL, Borok N, Truong MX, Lee M, Shin JS, Hudson M, Barton MB. Multi-parametric magnetic resonance imaging assessment of whole tumour heterogeneity for chemoradiotherapy response prediction in rectal cancer. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2021; 18:26-33. [PMID: 34258404 PMCID: PMC8254202 DOI: 10.1016/j.phro.2021.03.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 02/03/2021] [Accepted: 03/18/2021] [Indexed: 12/30/2022]
Abstract
Background and purpose Prediction of chemoradiotherapy response (CRT) in locally advanced rectal cancer would enable stratification of management. The purpose was to prospectively evaluate multi-parametric magnetic resonance imaging (MRI) assessment of tumour heterogeneity combining diffusion weighted imaging (DWI) and dynamic contrast enhanced (DCE) MRI for the prediction of CRT response in locally advanced rectal cancer. Materials and methods Patients with Stage II or III rectal adenocarcinoma undergoing neoadjuvant CRT and surgery underwent MRI (DWI and DCE) before, during (week 3), and after CRT (1 week before surgery). Patients with histopathology tumour regression grade (TRG) 0-1 were classified as responders, and TRG 2-3 were classified as non-responders. A whole tumour voxel-wise technique was used to produce apparent diffusion coefficient (ADC) and Ktrans (Tofts model) histograms derived from DWI and DCE-MRI, respectively. Logistic regression was used to predict response status for ADC and Ktrans quantiles. Results Thirty-three patients were included in this analysis; 16 responders, and 17 non-responders. On heterogeneity analysis, odds of being a responder were significantly higher after CRT (before surgery) for higher ADC 75th (p = 0.049) and ADC 90th (p = 0.034) percentile values. The Ktrans quantiles were lower in non-responders than responders before and during CRT, and higher after CRT although no significant association with response status was observed (p ≥ 0.10). Conclusions DWI-MRI after CRT (before surgery) incorporating a histogram analysis of whole tumour heterogeneity was predictive of CRT response in patients with locally advanced rectal cancer. DCE-MRI did not add value in response prediction. Clinical trial registration Australian New Zealand Clinical Trials Registry (ANZCTR) number ACTRN12616001690448.
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Affiliation(s)
- Trang Thanh Pham
- Ingham Institute for Applied Medical Research, South West Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, PO Box 3151, Liverpool, NSW 2170, Australia.,Department of Radiation Oncology, Liverpool Cancer Therapy Centre, Liverpool Hospital, Sydney, Locked Bag 7103, Liverpool BC, NSW 1871, Australia
| | - Gary Liney
- Ingham Institute for Applied Medical Research, South West Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, PO Box 3151, Liverpool, NSW 2170, Australia
| | - Karen Wong
- Department of Radiation Oncology, Liverpool Cancer Therapy Centre, Liverpool Hospital, Sydney, Locked Bag 7103, Liverpool BC, NSW 1871, Australia
| | - Christopher Henderson
- Department of Anatomical Pathology, Liverpool Hospital, Sydney, Locked Bag 7103, Liverpool BC, NSW, 1871, Australia
| | - Robba Rai
- Department of Radiation Oncology, Liverpool Cancer Therapy Centre, Liverpool Hospital, Sydney, Locked Bag 7103, Liverpool BC, NSW 1871, Australia
| | - Petra L Graham
- Centre for Economic Impacts of Genomic Medicine, Macquarie Business School and Department of Mathematics and Statistics, Faculty of Science and Engineering, Macquarie University, Sydney, Macquarie University, NSW 2109, Australia
| | - Nira Borok
- Department of Radiology, Liverpool Hospital, Sydney, Locked Bag 7103, Liverpool BC, NSW 1871, Australia
| | - Minh Xuan Truong
- Department of Radiology, Liverpool Hospital, Sydney, Locked Bag 7103, Liverpool BC, NSW 1871, Australia
| | - Mark Lee
- Department of Radiation Oncology, Liverpool Cancer Therapy Centre, Liverpool Hospital, Sydney, Locked Bag 7103, Liverpool BC, NSW 1871, Australia
| | - Joo-Shik Shin
- Department of Anatomical Pathology, Liverpool Hospital, Sydney, Locked Bag 7103, Liverpool BC, NSW, 1871, Australia.,School of Medicine, Western Sydney University, Sydney, Locked Bag 1797, Penrith, NSW 2751, Australia
| | - Malcolm Hudson
- NHMRC Clinical Trials Centre, Sydney, Locked Bag 77, Camperdown, NSW 1450, Australia
| | - Michael B Barton
- Ingham Institute for Applied Medical Research, South West Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, PO Box 3151, Liverpool, NSW 2170, Australia
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Thust SC, Maynard JA, Benenati M, Wastling SJ, Mancini L, Jaunmuktane Z, Brandner S, Jäger HR. Regional and Volumetric Parameters for Diffusion-Weighted WHO Grade II and III Glioma Genotyping: A Method Comparison. AJNR Am J Neuroradiol 2021; 42:441-447. [PMID: 33414227 DOI: 10.3174/ajnr.a6965] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 10/19/2020] [Indexed: 12/21/2022]
Abstract
BACKGROUND AND PURPOSE Studies consistently report lower ADC values in isocitrate dehydrogenase (IDH) wild-type gliomas than in IDH mutant tumors, but their methods and thresholds vary. This research aimed to compare volumetric and regional ADC measurement techniques for glioma genotyping, with a focus on IDH status prediction. MATERIALS AND METHODS Treatment-naïve World Health Organization grade II and III gliomas were analyzed by 3 neuroradiologist readers blinded to tissue results. ADC minimum and mean ROIs were defined in tumor and in normal-appearing white matter to calculate normalized values. T2-weighted tumor VOIs were registered to ADC maps with histogram parameters (mean, 2nd and 5th percentiles) extracted. Nonparametric testing (eta2 and ANOVA) was performed to identify associations between ADC metrics and glioma genotypes. Logistic regression was used to probe the ability of VOI and ROI metrics to predict IDH status. RESULTS The study included 283 patients with 79 IDH wild-type and 204 IDH mutant gliomas. Across the study population, IDH status was most accurately predicted by ROI mean normalized ADC and VOI mean normalized ADC, with areas under the curve of 0.83 and 0.82, respectively. The results for ROI-based genotyping of nonenhancing and solid-patchy enhancing gliomas were comparable with volumetric parameters (area under the curve = 0.81-0.84). In rim-enhancing, centrally necrotic tumors (n = 23), only volumetric measurements were predictive (0.90). CONCLUSIONS Regional normalized mean ADC measurements are noninferior to volumetric segmentation for defining solid glioma IDH status. Partially necrotic, rim-enhancing tumors are unsuitable for ROI assessment and may benefit from volumetric ADC quantification.
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Affiliation(s)
- S C Thust
- From the Neuroradiological Academic Unit (S.C.T., J.A.M, S.J.W., L.M., H.R.J.), Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, UK
- Lysholm Department of Neuroradiology (S.C.T., J.A.M., M.B., S.J.W., L.M., H.R.J.), National Hospital for Neurology and Neurosurgery, London, UK
- Imaging Department (S.C.T., H.R.J.), University College London Foundation Hospital, London, UK
| | - J A Maynard
- From the Neuroradiological Academic Unit (S.C.T., J.A.M, S.J.W., L.M., H.R.J.), Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, UK
- Lysholm Department of Neuroradiology (S.C.T., J.A.M., M.B., S.J.W., L.M., H.R.J.), National Hospital for Neurology and Neurosurgery, London, UK
| | - M Benenati
- Lysholm Department of Neuroradiology (S.C.T., J.A.M., M.B., S.J.W., L.M., H.R.J.), National Hospital for Neurology and Neurosurgery, London, UK
- Dipartimento di Diagnostica per Immagini (M.B.), Radioterapia, Oncologia ed Ematologia, Fondazione Policlinico Universitario A. Gemelli Institute for Research, Hospitalization and Health Care, Rome, Italy
| | - S J Wastling
- From the Neuroradiological Academic Unit (S.C.T., J.A.M, S.J.W., L.M., H.R.J.), Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, UK
- Lysholm Department of Neuroradiology (S.C.T., J.A.M., M.B., S.J.W., L.M., H.R.J.), National Hospital for Neurology and Neurosurgery, London, UK
| | - L Mancini
- From the Neuroradiological Academic Unit (S.C.T., J.A.M, S.J.W., L.M., H.R.J.), Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, UK
- Lysholm Department of Neuroradiology (S.C.T., J.A.M., M.B., S.J.W., L.M., H.R.J.), National Hospital for Neurology and Neurosurgery, London, UK
| | - Z Jaunmuktane
- Department of Clinical and Movement Neurosciences (Z.J.)
| | - S Brandner
- Neurodegenerative Disease (S.B.), UCL Queen Square Institute of Neurology, and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - H R Jäger
- From the Neuroradiological Academic Unit (S.C.T., J.A.M, S.J.W., L.M., H.R.J.), Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, UK
- Lysholm Department of Neuroradiology (S.C.T., J.A.M., M.B., S.J.W., L.M., H.R.J.), National Hospital for Neurology and Neurosurgery, London, UK
- Imaging Department (S.C.T., H.R.J.), University College London Foundation Hospital, London, UK
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Xing S, Levesque IR. A simulation study of cell size and volume fraction mapping for tissue with two underlying cell populations using diffusion-weighted MRI. Magn Reson Med 2021; 86:1029-1044. [PMID: 33644889 DOI: 10.1002/mrm.28694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 12/23/2020] [Accepted: 01/04/2021] [Indexed: 11/08/2022]
Abstract
PURPOSE To propose a method for voxel-wise estimation of cell radii and volume fractions of two cell populations when they coexist in the same MR voxel using the combination of diffusion-weighted MRI and microstructural modeling. METHOD Microstructure models were investigated using diffusion data simulated with the matrix method for a range of microstructures mimicking tumor tissue with two cell populations, using acquisition parameters available on preclinical scanners. The effect of noise was investigated for a subset of these microstructures. The accuracy and precision of the estimated radii and volume fractions for large and small cells R l , R s , v i n , l , v i n , s were evaluated by comparing the estimates to their true values. The stability of model fitting was characterized by the percentage of accepted fits. RESULTS The estimation accuracy and precision, and thus the ability to robustly distinguish the two cell populations, depended on the microstructural properties and SNR. For a SNR of 50, a minimum difference of 3 μm between the radius of the large and small cell populations was required for differentiation. Proposed modifications to the two cell population microstructure model, including constrained fits, improved the stability of fits. CONCLUSIONS This proof-of-concept study proposed a diffusion MRI-based method for voxel-wise estimation of cell radii and volume fractions of two cell populations when they coexist in the same MR voxel. The ability to reliably characterize tissue with two cell populations opens exciting avenues of potential applications in both tumor diagnosis and treatment monitoring.
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Affiliation(s)
- Shu Xing
- Medical Physics Unit, McGill University, Montreal, Quebec, Canada.,Department of Physics, McGill University, Montreal, Quebec, Canada
| | - Ives R Levesque
- Medical Physics Unit, McGill University, Montreal, Quebec, Canada.,Department of Physics, McGill University, Montreal, Quebec, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada.,Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
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Zhang AD, Su XH, Wang YF, Shi GF, Han C, Zhang N. Predicting the effects of radiotherapy based on diffusion kurtosis imaging in a xenograft mouse model of esophageal carcinoma. Exp Ther Med 2021; 21:327. [PMID: 33732300 PMCID: PMC7903468 DOI: 10.3892/etm.2021.9758] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 11/20/2020] [Indexed: 02/06/2023] Open
Abstract
The aim of the present study was to assess the predictive value of diffusion kurtosis imaging (DKI) on the effects of radiotherapy in a xenograft model of esophageal cancer. A total of 40 tumor-bearing mice, established by injection of Eca-109 cells in nude mice, were used. The experimental group (n=24) received a single dose of 15 Gy (6 MV by X-ray), and the control group (n=16) did not receive any treatment. Tumor volume, apparent diffusion coefficient (ADC), mean kurtosis (MK) and mean diffusivity (MD) of the two groups were compared, and the expression of aquaporin (AQP) 3 and necrosis ratio at matched time points in xenografts were also observed. There was a significant difference between the two groups from the 7th day of radiotherapy onwards; the xenograft volume of the experimental group was significantly smaller compared with the control group (P<0.05). On the 3rd day, the ADC and MD of the experimental group was significantly higher compared with the control group, and MK was significantly lower compared with the control group (P<0.05). On the 3rd day, AQP3 expression in the experimental group was lower compared with the control group, and the proportion of necrotic cells was higher compared with the control group (P<0.05). Single large fraction dose radiotherapy inhibited the growth of a xenografted esophageal tumor. Changes in ADC, MK and MD were observed prior to morphological changes in the tumor. The change in AQP3 expression and necrosis ratio was in also agreement with the DKI parameters assessed. DKI may thus provide early predictive ability on the effect of radiotherapy in esophageal carcinoma.
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Affiliation(s)
- An-Du Zhang
- Department of Radiotherapy, Hebei Medical University Fourth Affiliated Hospital/Hebei Provincial Tumor Hospital, Shijiazhuang, Hebei 050011, P.R. China
| | - Xiao-Hua Su
- Department of Oncology, Hebei General Hospital, Shijiazhuang, Hebei 050011, P.R. China
| | - Yan-Fei Wang
- Department of CT and MRI, Hebei Medical University Fourth Affiliated Hospital/Hebei Provincial Tumor Hospital, Shijiazhuang, Hebei 050011, P.R. China
| | - Gao-Feng Shi
- Department of CT and MRI, Hebei Medical University Fourth Affiliated Hospital/Hebei Provincial Tumor Hospital, Shijiazhuang, Hebei 050011, P.R. China
| | - Chun Han
- Department of Radiotherapy, Hebei Medical University Fourth Affiliated Hospital/Hebei Provincial Tumor Hospital, Shijiazhuang, Hebei 050011, P.R. China
| | - Nan Zhang
- Department of Radiotherapy, Hebei Medical University Fourth Affiliated Hospital/Hebei Provincial Tumor Hospital, Shijiazhuang, Hebei 050011, P.R. China
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Zhao M, Zhao L, Yang H, Duan Y, Li G. Apparent diffusion coefficient for the prediction of tumor response to neoadjuvant chemo-radiotherapy in locally advanced rectal cancer. Radiat Oncol 2021; 16:17. [PMID: 33472660 PMCID: PMC7819172 DOI: 10.1186/s13014-020-01738-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 12/26/2020] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Patients with locally advanced rectal cancer generally have different response rates to preoperative neoadjuvant chemo-radiotherapy. This study investigated the value of the apparent diffusion coefficient (ADC) as a predictor to forecast the response to neoadjuvant chemo-radiotherapy in patients with locally advanced rectal cancer. METHODS Ninety-one locally advanced rectal cancer patients who underwent neoadjuvant chemo-radiotherapy between 2015 and 2018 were enrolled. Diffusion-weighted magnetic resonance imaging was performed before treatment and within 4 weeks after the completion of neoadjuvant chemo-radiotherapy. Mean ADC values of regions of interest were evaluated by two radiologists. The tumor response was evaluated according to RESCIST 1.1. The cut-off value for the mean ADC and increasing percentage (ΔADC%) after neoadjuvant chemo-radiotherapy was calculated using the receiver operating characteristic curve. The response rate of pre-ADC and ΔADC% above/below the cut-off values was determined using the chi-square test, respectively. Primary tumor progression-free survival (PFS) was analyzed using the Kaplan-Meier method, based on the pre-ADC and ΔADC% cut-off values. RESULTS The cut-off value of mean pre-ADC and ΔADC% was 0.94 × 10-3 mm2/s (80.36% sensitivity, 74.29% specificity) and 26.0% (73.21% sensitivity, 77.14% specificity), respectively. Lower mean pre-ADC values were related to a better response rate (83.3% vs 29.7%, P < 0.001) and PFS (26.12 vs 17.70 months, P = 0.004). ΔADC% above the cut-off value was also related to a better response rate (83.7% vs 35.7%, P < 0.001) and PFS (26.93 vs 15.65 months, P = 0.034). CONCLUSIONS The mean ADC pre-treatment value and ΔADC% were potential predictors for the tumor response in locally advanced rectal cancer patients treated with neoadjuvant chemo-radiotherapy.
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Affiliation(s)
- Mengjing Zhao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Lihao Zhao
- Department of Chemoradiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Han Yang
- Department of Chemoradiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Yuxia Duan
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China.
| | - Gang Li
- Department of Chemoradiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China.
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Attademo L, Bernardini F, Verdolini N. Neural Correlates of Schizotypal Personality Disorder: a Systematic Review of Neuroimaging and EEG Studies. Curr Med Imaging 2021; 17:1283-1298. [PMID: 33459241 DOI: 10.2174/1573405617666210114142206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 10/20/2020] [Accepted: 11/12/2020] [Indexed: 01/22/2023]
Abstract
BACKGROUND Schizotypal personality disorder (SPD) is a cluster A personality disorder affecting 1.0% of general population, characterised by disturbances in cognition and reality testing dimensions, affect regulation, and interpersonal function. SPD shares similar but attenuated phenomenological, genetic, and neurobiological abnormalities with schizophrenia (SCZ) and is described as part of schizophrenia spectrum disorders. OBJECTIVE Aim of this work was to identify the major neural correlates of SPD. METHODS This is a systematic review conducted according to PRISMA statement. The protocol was prospectively registered in PROSPERO - International prospective register of systematic reviews. The review was performed to summarise the most comprehensive and updated evidence on functional neuroimaging and neurophysiology findings obtained through different techniques (DW-MRI, DTI, PET, SPECT, fMRI, MRS, EEG) in individuals with SPD. RESULTS Of the 52 studies included in this review, 9 were on DW-MRI and DTI, 11 were on PET and SPECT, 11 were on fMRI and MRS, and 21 were on EEG. It was complex to synthesise all the functional abnormalities found into a single, unified, pathogenetic pathway, but a common theme emerged: the dysfunction of brain circuits including striatal, frontal, temporal, limbic regions (and their networks) together with a dysregulation along the dopaminergic pathways. CONCLUSION Brain abnormalities in SPD are similar, but less marked, than those found in SCZ. Furthermore, different patterns of functional abnormalities in SPD and SCZ have been found, confirming the previous literature on the 'presence' of possible compensatory factors, protecting individuals with SPD from frank psychosis and providing diagnostic specificity.
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Affiliation(s)
- Luigi Attademo
- Hospital Psychiatric Service for Diagnosis and Care (S.P.D.C.) of Potenza, Department of Mental Health, ASP Basilicata, Italian National Health Service, Potenza. Italy
| | - Francesco Bernardini
- Hospital Psychiatric Service for Diagnosis and Care (S.P.D.C.) of Pordenone, Department of Mental Health, AsFO Friuli Occidentale, Italian National Health Service, Pordenone. Italy
| | - Norma Verdolini
- Barcelona Bipolar Disorders Program, Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, CIBERSAM, 170 Villarroel st., Barcelona, Catalunya. Spain
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Jacobs AH, Schelhaas S, Viel T, Waerzeggers Y, Winkeler A, Zinnhardt B, Gelovani J. Imaging of Gene and Cell-Based Therapies: Basis and Clinical Trials. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00060-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Cancer Detection and Quantification of Treatment Response Using Diffusion-Weighted MRI. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00068-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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Thust S, Micallef C, Okuchi S, Brandner S, Kumar A, Mankad K, Wastling S, Mancini L, Jäger HR, Shankar A. Imaging characteristics of H3 K27M histone-mutant diffuse midline glioma in teenagers and adults. Quant Imaging Med Surg 2021; 11:43-56. [PMID: 33392010 DOI: 10.21037/qims-19-954] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background To assess anatomical and quantitative diffusion-weighted MR imaging features in a recently classified lethal neoplasm, H3 K27M histone-mutant diffuse midline glioma [World Health Organization (WHO) IV]. Methods Fifteen untreated gliomas in teenagers and adults (median age 19, range, 14-64) with confirmed H3 K27M histone-mutant genotype were analysed at a national referral centre. Morphological characteristics including tumour epicentre(s), T2/FLAIR and Gadolinium enhancement patterns, calcification, haemorrhage and cyst formation were recorded. Multiple apparent diffusion coefficient (ADCmin, ADCmean) regions of interest were sited in solid tumour and normal appearing white matter (ADCNAWM) using post-processing software (Olea Sphere v2.3, Olea Medical). ADC histogram data (2nd, 5th, 10th percentile, median, mean, kurtosis, skewness) were calculated from volumetric tumour segmentations and tested against the regions of interest (ROI) data (Wilcoxon signed rank test). Results The median interval from imaging to tissue diagnosis was 9 (range, 0-74) days. The structural MR imaging findings varied between individuals and within tumours, often featuring signal heterogeneity on all MR sequences. All gliomas demonstrated contact with the brain midline, and 67% exhibited rim-enhancing necrosis. The mean ROI ADCmin value was 0.84 (±0.15 standard deviation, SD) ×10-3 mm2/s. In the largest tumour cross-section (excluding necrosis), an average ADCmean value of 1.12 (±0.25)×10-3 mm2/s was observed. The mean ADCmin/NAWM ratio was 1.097 (±0.149), and the mean ADCmean/NAWM ratio measured 1.466 (±0.299). With the exception of the 2nd centile, no statistical difference was observed between the regional and histogram derived ADC results. Conclusions H3 K27M-mutant gliomas demonstrate variable morphology and diffusivity, commonly featuring moderately low ADC values in solid tumour. Regional ADC measurements appeared representative of volumetric histogram data in this study.
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Affiliation(s)
- Stefanie Thust
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK.,Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Caroline Micallef
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK.,Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Sachi Okuchi
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK.,Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Sebastian Brandner
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology and Division of Neuropathology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, UK
| | - Atul Kumar
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology and Division of Neuropathology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, UK
| | - Kshitij Mankad
- Department of Radiology, Great Ormond Street Hospital for Children, London, UK
| | - Stephen Wastling
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK.,Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Laura Mancini
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK.,Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Hans Rolf Jäger
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK.,Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Ananth Shankar
- Teenage and Young Persons' Cancer Unit, Department of Paediatric Oncology, University College London Hospitals NHS Foundation Trust, London, UK
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Diffusion-weighted magnetic resonance imaging (DWMRI) of head and neck squamous cell carcinoma: could it be an imaging biomarker for prediction of response to chemoradiation therapy. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2020. [DOI: 10.1186/s43055-020-00323-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Chemoradiation therapy (CRT) has become a primary definitive treatment modality for head and neck squamous cell carcinoma (HNSCC); however, not all patients respond completely to treatment. Ability to identify those patients, who would not achieve complete response, before or early during the course of CRT will allow treatment modifications to improve outcome and overall survival. The aim of this prospective study was to assess the usefulness of diffusion-weighted imaging (DWI) in prediction of early therapeutic response of HNSCC after CRT.
Results
Local control was achieved in 22 patients out of 46 patients with pathologically proven HNSCC treated by chemoradiation therapy and local failure was detected in 24 patients out of 46 patients. Pretreatment mean apparent diffusion coefficient (ADCpre) was significantly higher in local failure group (1.1 ± 0.2 × 10−3 mm2/s) than local control group (0.89 ± 0.1 × 10−3 mm2/s). An optimal cut-off value of more than 0.94 × 10−3 mm2/s was predictive of local failure with sensitivity 83.33%, specificity 59.9%, PPV 69%, NPV 76.5%. Early intra-treatment percentage change of ADC (ΔADC) was significantly lower in local failure group (21.8% ± 21.3) than in local control group (45.2% ± 27.8). An optimal cut-off value of ≤ 33% was predictive of local failure after CRT with sensitivity of 71.34%, specificity of 60%, PPV of 62.5%, and NPV of 69.2%.
Conclusions
Diffusion-weighted MRI could be a potential predictive biomarker for therapeutic response of HNSCC to CRT. Primary tumors with higher pretreatment mean ADC, and a smaller early intratreatment percentage increase of mean ADC would be more likely to fail treatment.
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Reichardt W, von Elverfeldt D. Preclinical Applications of Magnetic Resonance Imaging in Oncology. Recent Results Cancer Res 2020; 216:405-437. [PMID: 32594394 DOI: 10.1007/978-3-030-42618-7_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The evolving possibilities of molecular imaging (MI) are fundamentally changing the way we look at cancer, with imaging paradigms now shifting away from basic morphological measures toward the longitudinal assessment of functional, metabolic, cellular, and molecular information in vivo. Recent developments of imaging methodology and probe molecules utilizing the vast number of novel animal models of human cancers have enhanced our ability to non-invasively characterize neoplastic tissue and follow anticancer treatments. While preclinical molecular imaging offers a whole palette of excellent methodology to choose from, we will focus on magnetic resonance imaging (MRI) techniques, since they provide excellent molecular imaging capabilities and bear high potential for clinical translation. Prerequisites and consequences of using animal models as surrogates of human cancers in preclinical molecular imaging are outlined. We present physical principles, values, and limitations of MRI as molecular imaging modality and comment on its high potential to non-invasively assess information on metabolism, hypoxia, angiogenesis, and cell trafficking in preclinical cancer research.
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Affiliation(s)
- Wilfried Reichardt
- Medical Physics, Department of Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany. .,German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany. .,German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Dominik von Elverfeldt
- Medical Physics, Department of Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Perucho JAU, Chang HCC, Vardhanabhuti V, Wang M, Becker AS, Wurnig MC, Lee EYP. B-Value Optimization in the Estimation of Intravoxel Incoherent Motion Parameters in Patients with Cervical Cancer. Korean J Radiol 2020; 21:218-227. [PMID: 31997597 PMCID: PMC6992446 DOI: 10.3348/kjr.2019.0232] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 10/30/2019] [Indexed: 12/25/2022] Open
Abstract
Objective This study aimed to find the optimal number of b-values for intravoxel incoherent motion (IVIM) imaging analysis, using simulated and in vivo data from cervical cancer patients. Materials and Methods Simulated data were generated using literature pooled means, which served as reference values for simulations. In vivo data from 100 treatment-naïve cervical cancer patients with IVIM imaging (13 b-values, scan time, 436 seconds) were retrospectively reviewed. A stepwise b-value fitting algorithm calculated optimal thresholds. Feed forward selection determined the optimal subsampled b-value distribution for biexponential IVIM fitting, and simplified IVIM modeling using monoexponential fitting was attempted. IVIM parameters computed using all b-values served as reference values for in vivo data. Results In simulations, parameters were accurately estimated with six b-values, or three b-values for simplified IVIM, respectively. In vivo data showed that the optimal threshold was 40 s/mm2 for patients with squamous cell carcinoma and a subsampled acquisition of six b-values (scan time, 198 seconds) estimated parameters were not significantly different from reference parameters (individual parameter error rates of less than 5%). In patients with adenocarcinoma, the optimal threshold was 100 s/mm2, but an optimal subsample could not be identified. Irrespective of the histological subtype, only three b-values were needed for simplified IVIM, but these parameters did not retain their discriminative ability. Conclusion Subsampling of six b-values halved the IVIM scan time without significant losses in accuracy and discriminative ability. Simplified IVIM is possible with only three b-values, at the risk of losing diagnostic information.
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Affiliation(s)
| | | | | | - Mandi Wang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong
| | - Anton Sebastian Becker
- Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Switzerland
| | - Moritz Christoph Wurnig
- Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Switzerland
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McHugh DJ, Lipowska‐Bhalla G, Babur M, Watson Y, Peset I, Mistry HB, Hubbard Cristinacce PL, Naish JH, Honeychurch J, Williams KJ, O'Connor JPB, Parker GJM. Diffusion model comparison identifies distinct tumor sub-regions and tracks treatment response. Magn Reson Med 2020; 84:1250-1263. [PMID: 32057115 PMCID: PMC7317874 DOI: 10.1002/mrm.28196] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 01/13/2020] [Accepted: 01/13/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE MRI biomarkers of tumor response to treatment are typically obtained from parameters derived from a model applied to pre-treatment and post-treatment data. However, as tumors are spatially and temporally heterogeneous, different models may be necessary in different tumor regions, and model suitability may change over time. This work evaluates how the suitability of two diffusion-weighted (DW) MRI models varies spatially within tumors at the voxel level and in response to radiotherapy, potentially allowing inference of qualitatively different tumor microenvironments. METHODS DW-MRI data were acquired in CT26 subcutaneous allografts before and after radiotherapy. Restricted and time-independent diffusion models were compared, with regions well-described by the former hypothesized to reflect cellular tissue, and those well-described by the latter expected to reflect necrosis or oedema. Technical and biological validation of the percentage of tissue described by the restricted diffusion microstructural model (termed %MM) was performed through simulations and histological comparison. RESULTS Spatial and radiotherapy-related variation in model suitability was observed. %MM decreased from a mean of 64% at baseline to 44% 6 days post-radiotherapy in the treated group. %MM correlated negatively with the percentage of necrosis from histology, but overestimated it due to noise. Within MM regions, microstructural parameters were sensitive to radiotherapy-induced changes. CONCLUSIONS There is spatial and radiotherapy-related variation in different models' suitability for describing diffusion in tumor tissue, suggesting the presence of different and changing tumor sub-regions. The biological and technical validation of the proposed %MM cancer imaging biomarker suggests it correlates with, but overestimates, the percentage of necrosis.
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Affiliation(s)
- Damien J. McHugh
- Quantitative Biomedical Imaging LaboratoryThe University of ManchesterManchesterUK
- Division of Cancer SciencesThe University of ManchesterManchesterUK
| | - Grazyna Lipowska‐Bhalla
- Quantitative Biomedical Imaging LaboratoryThe University of ManchesterManchesterUK
- Division of Cancer SciencesThe University of ManchesterManchesterUK
| | - Muhammad Babur
- Division of Pharmacy & OptometryThe University of ManchesterManchesterUK
| | - Yvonne Watson
- Quantitative Biomedical Imaging LaboratoryThe University of ManchesterManchesterUK
| | - Isabel Peset
- Imaging and Flow CytometryCancer Research UK Manchester InstituteManchesterUK
| | - Hitesh B. Mistry
- Division of Cancer SciencesThe University of ManchesterManchesterUK
| | | | - Josephine H. Naish
- Division of Cardiovascular SciencesThe University of ManchesterManchesterUK
- Bioxydyn Ltd.ManchesterUK
| | | | - Kaye J. Williams
- Division of Pharmacy & OptometryThe University of ManchesterManchesterUK
| | - James P. B. O'Connor
- Quantitative Biomedical Imaging LaboratoryThe University of ManchesterManchesterUK
- Division of Cancer SciencesThe University of ManchesterManchesterUK
| | - Geoffrey J. M. Parker
- Bioxydyn Ltd.ManchesterUK
- Division of Neuroscience and Experimental PsychologyThe University of ManchesterManchesterUK
- Centre for Medical Image ComputingUniversity College LondonLondonUK
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Chen W, Wang YT, Guo L, Zhu Z, Zhou HF, Bai G. Predictive Value of Early Response to Chemoradiotherapy in Advanced Esophageal Squamous Cell Carcinoma by Diffusion-Weighted MR Imaging. Technol Cancer Res Treat 2020; 19:1533033820943220. [PMID: 32720592 PMCID: PMC7388082 DOI: 10.1177/1533033820943220] [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] [Indexed: 11/23/2022] Open
Abstract
Objective: To explore the value of diffusion-weighted imaging for early response
detection of locally advanced esophageal squamous cell carcinoma with
concurrent chemoradiotherapy. Methods: Fifty-five (42 males, 13 females) patients with locally advanced esophageal
cancer who were undergoing chemoradiotherapy were recruited for this study.
Diffusion-weighted imaging was performed in all patients before therapy, at
the first weekend, the second weekend, and the end of chemoradiotherapy. The
rate of change in apparent diffusion coefficient value and the maximum
diameter between pretherapy and posttherapy were calculated. Results: Fifty-five patients with locally advanced esophageal squamous cell carcinoma
were classified as responders (40 cases) and nonresponders (15 cases).
Before chemoradiotherapy, the responders group had a significantly lower
apparent diffusion coefficient values than the nonresponders group
(t = −4.815, P = .000). At the 3 time
points after chemoradiotherapy (first weekend, second weekend, and the end
of chemoradiotherapy), there was no statistically significant difference in
apparent diffusion coefficient values between responders and nonresponders
(P > .05). The responders group had a significantly
higher rate of change in apparent diffusion coefficient value than the
nonresponders group at each time point (P < .05). At the
first weekend of chemoradiotherapy, the rate of change in the maximum
diameter was not significantly different in the 2 groups (t
= 0.928, P = .357). There was a negative correlation
between the tumor apparent diffusion coefficient value of pretherapy and the
reduction ratio of tumor maximum diameter at the end of chemoradiotherapy
(r = −0.592, P = .000). Conclusions: The change rate of apparent diffusion coefficient value by the end of the
first week after beginning chemoradiotherapy may be a sensitive indicator to
detect the early response to locally advanced esophageal squamous cell
carcinoma.
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Affiliation(s)
- Wei Chen
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China
| | - Ya-Ting Wang
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China
| | - Lili Guo
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China
| | - Zhaohuan Zhu
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China
| | - Hai-Fei Zhou
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China
| | - Genji Bai
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China
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