| |  Online-Ressource |
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| Verfasst von: | Schmitz, Fabian [VerfasserIn]  |
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| | Voigtländer, Hendrik [VerfasserIn] |
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| | Jang, Hyungseok [VerfasserIn] |
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| | Schlemmer, Heinz-Peter [VerfasserIn] |
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| | Kauczor, Hans-Ulrich [VerfasserIn] |
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| | Sedaghat, Sam [VerfasserIn] |
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| Titel: | Predicting the malignancy grade of soft tissue sarcomas on MRI using conventional image reading and radiomics |
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| Verf.angabe: | Fabian Schmitz, Hendrik Voigtländer, Hyungseok Jang, Heinz-Peter Schlemmer, Hans-Ulrich Kauczor and Sam Sedaghat |
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| E-Jahr: | 2024 |
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| Jahr: | 5 October 2024 |
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| Fussnoten: | Gesehen am 02.04.2025 |
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| Weitere Titel: | Titel des special issue: Soft Tissue Sarcoma: From Diagnosis to Prognosis |
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| Titel Quelle: | Enthalten in: Diagnostics |
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| Ort Quelle: | Basel : MDPI, 2011 |
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| Jahr Quelle: | 2024 |
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| Band/Heft Quelle: | 14(2024), 19, special issue, Artikel-ID 2220, Seite 1-17$z17 |
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| ISSN Quelle: | 2075-4418 |
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| Abstract: | Objectives: This study aims to investigate MRI features predicting the grade of STS malignancy using conventional image reading and radiomics. Methods: Pretherapeutic imaging data regarding size, tissue heterogeneity, peritumoral changes, necrosis, hemorrhage, and cystic degeneration were evaluated in conventional image reading. Furthermore, the tumors’ apparent diffusion coefficient (ADC) values and radiomics features were extracted and analyzed. A random forest machine learning algorithm was trained and evaluated based on the extracted features. Results: A total of 139 STS cases were included in this study. The mean tumor ADC and the ratio between tumor ADC to healthy muscle ADC were significantly lower in high-grade tumors (p = 0.001 and 0.005, respectively). Peritumoral edema (p < 0.001) and peritumoral contrast enhancement (p < 0.001) were significantly more extensive in high-grade tumors. Tumor heterogeneity was significantly increased in high-grade sarcomas, particularly in T2w- and contrast-enhanced sequences using conventional image reading (p < 0.001) as well as in the radiomics analysis (p < 0.001). Our trained random forest machine learning model predicted high-grade status with an area under the curve (AUC) of 0.97 and an F1 score of 0.93. Biopsy-underestimated tumors exhibited differences in tumor heterogeneity and peritumoral changes. Conclusions: Tumor heterogeneity is a key characteristic of high-grade STSs, which is discernible through conventional imaging reading and radiomics analysis. Higher STS grades are also associated with low ADC values, peritumoral edema, and peritumoral contrast enhancement. |
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| DOI: | doi:10.3390/diagnostics14192220 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
kostenfrei: Volltext: https://doi.org/10.3390/diagnostics14192220 |
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| | kostenfrei: Volltext: https://www.mdpi.com/2075-4418/14/19/2220 |
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| | DOI: https://doi.org/10.3390/diagnostics14192220 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | machine learning |
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| | malignancy grade |
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| | MRI |
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| | radiomics |
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| | soft tissue sarcoma |
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| K10plus-PPN: | 1921159359 |
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| Verknüpfungen: | → Zeitschrift |
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Predicting the malignancy grade of soft tissue sarcomas on MRI using conventional image reading and radiomics / Schmitz, Fabian [VerfasserIn]; 5 October 2024 (Online-Ressource)
