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Quantitative Susceptibility Mapping in Skull Base Chordoma: In Silico Analysis and In Vivo Application Towards Indirect Hypoxia Assessment.

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Title: Quantitative Susceptibility Mapping in Skull Base Chordoma: In Silico Analysis and In Vivo Application Towards Indirect Hypoxia Assessment.
Authors: Fenech, P.1 (AUTHOR) paolo.fenech@polimi.it, Morelli, L.1 (AUTHOR), Parrella, G.1 (AUTHOR), Imparato, S.2 (AUTHOR), Iannalfi, A.2 (AUTHOR), Lillo, S.2,3 (AUTHOR), Orlandi, E.2,4 (AUTHOR), Baroni, G.1 (AUTHOR), Paganelli, C.1 (AUTHOR)
Source: Magnetic Resonance in Medicine. Apr2026, Vol. 95 Issue 4, p2092-2105. 14p.
Subjects: Magnetic susceptibility, Skull tumors, Hypoxemia, Cell proliferation, Computer-assisted image analysis (Medicine), Tumors, Simulation methods & models
Abstract: Purpose: To evaluate quantitative susceptibility mapping (QSM) beyond the brain through realistic simulations and to explore preliminary evidence that may be indicative of hypoxia in skull base chordomas (SBC). Methods: Each step of the QSM pipeline was optimized within an in silico framework consisting of (i) phase unwrapping, (ii) background field removal, and (iii) dipole field inversion, which were tested on a realistic phantom to generate accurate susceptibility maps. The optimized pipeline was then applied to seven SBC patients, analyzing tumor heterogeneity and correlating QSM features with the proliferation index (Ki‐67), towards hypoxia assessment. A binary classifier was developed to distinguish low‐ and high‐proliferation tumors based on first‐order QSM features. Results: The optimal phase unwrapping method combined with dipole inversion provided an error of 38.36 ppm. The best strategy for background field removal exhibited the lowest error (from 49 to 53 Hz). In SBC patients, tumor heterogeneity was observed, and a statistically significant correlation (p < 0.05) was measured between Ki‐67 versus QSM maximum value and interquartile coefficient of variation within the tumor volume (Spearman's coefficients of 0.8 and −0.8, respectively). The classifier achieved 85.7% accuracy. Conclusion: This study provides a foundation for characterizing SBC through QSM, enabling indirect, non‐invasive identification of potentially hypoxic tumor regions. Further histological validation with specific hypoxia markers, such as HIF‐1α, is nevertheless required. [ABSTRACT FROM AUTHOR]
Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Quantitative Susceptibility Mapping in Skull Base Chordoma: In Silico Analysis and In Vivo Application Towards Indirect Hypoxia Assessment.
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  Data: Purpose: To evaluate quantitative susceptibility mapping (QSM) beyond the brain through realistic simulations and to explore preliminary evidence that may be indicative of hypoxia in skull base chordomas (SBC). Methods: Each step of the QSM pipeline was optimized within an in silico framework consisting of (i) phase unwrapping, (ii) background field removal, and (iii) dipole field inversion, which were tested on a realistic phantom to generate accurate susceptibility maps. The optimized pipeline was then applied to seven SBC patients, analyzing tumor heterogeneity and correlating QSM features with the proliferation index (Ki‐67), towards hypoxia assessment. A binary classifier was developed to distinguish low‐ and high‐proliferation tumors based on first‐order QSM features. Results: The optimal phase unwrapping method combined with dipole inversion provided an error of 38.36 ppm. The best strategy for background field removal exhibited the lowest error (from 49 to 53 Hz). In SBC patients, tumor heterogeneity was observed, and a statistically significant correlation (p &lt; 0.05) was measured between Ki‐67 versus QSM maximum value and interquartile coefficient of variation within the tumor volume (Spearman&#39;s coefficients of 0.8 and −0.8, respectively). The classifier achieved 85.7% accuracy. Conclusion: This study provides a foundation for characterizing SBC through QSM, enabling indirect, non‐invasive identification of potentially hypoxic tumor regions. Further histological validation with specific hypoxia markers, such as HIF‐1α, is nevertheless required. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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        Value: 10.1002/mrm.70193
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        Text: English
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    Subjects:
      – SubjectFull: Magnetic susceptibility
        Type: general
      – SubjectFull: Skull tumors
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      – SubjectFull: Hypoxemia
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      – SubjectFull: Cell proliferation
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      – SubjectFull: Simulation methods & models
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      – TitleFull: Quantitative Susceptibility Mapping in Skull Base Chordoma: In Silico Analysis and In Vivo Application Towards Indirect Hypoxia Assessment.
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              Text: Apr2026
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              Y: 2026
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