| |  Online-Ressource |
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| Verfasst von: | Tharmaseelan, Hishan [VerfasserIn]  |
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| | Vellala, Abhinay K. [VerfasserIn] |
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| | Hertel, Alexander [VerfasserIn] |
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| | Tollens, Fabian [VerfasserIn] |
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| | Rotkopf, Lukas Thomas [VerfasserIn] |
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| | Rink, Johann [VerfasserIn] |
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| | Woźnicki, Piotr [VerfasserIn] |
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| | Ayx, Isabelle [VerfasserIn] |
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| | Bartling, Sönke [VerfasserIn] |
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| | Nörenberg, Dominik [VerfasserIn] |
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| | Schönberg, Stefan [VerfasserIn] |
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| | Froelich, Matthias F. [VerfasserIn] |
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| Titel: | Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning |
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| Verf.angabe: | Hishan Tharmaseelan, Abhinay K. Vellala, Alexander Hertel, Fabian Tollens, Lukas T. Rotkopf, Johann Rink, Piotr Woźnicki, Isabelle Ayx, Sönke Bartling, Dominik Nörenberg, Stefan O. Schoenberg and Matthias F. Froelich |
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| Jahr: | 2023 |
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| Umfang: | 9 S. |
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| Fussnoten: | Veröffentlicht: 5. Oktober 2023 ; Gesehen am 23.11.2023 |
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| Titel Quelle: | Enthalten in: Cancer imaging |
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| Ort Quelle: | London : BioMed Central, 2000 |
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| Jahr Quelle: | 2023 |
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| Band/Heft Quelle: | 23(2023), Artikel-ID 95, Seite 1-9 |
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| ISSN Quelle: | 1470-7330 |
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| Abstract: | Objectives: The goal of this study is to demonstrate the performance of radiomics and CNN-based classifiers in determining the primary origin of gastrointestinal liver metastases for visually indistinguishable lesions.Methods: In this retrospective, IRB-approved study, 31 pancreatic cancer patients with 861 lesions (median age [IQR]: 65.39 [56.87, 75.08], 48.4% male) and 47 colorectal cancer patients with 435 lesions (median age [IQR]: 65.79 [56.99, 74.62], 63.8% male) were enrolled. A pretrained nnU-Net performed automated segmentation of 1296 liver lesions. Radiomics features for each lesion were extracted using pyradiomics. The performance of several radiomics-based machine-learning classifiers was investigated for the lesions and compared to an image-based deep-learning approach using a DenseNet-121. The performance was evaluated by AUC/ROC analysis. Results: The radiomics-based K-nearest neighbor classifier showed the best performance on an independent test set with AUC values of 0.87 and an accuracy of 0.67. In comparison, the image-based DenseNet-121-classifier reached an AUC of 0.80 and an accuracy of 0.83. Conclusions: CT-based radiomics and deep learning can distinguish the etiology of liver metastases from gastrointestinal primary tumors. Compared to deep learning, radiomics based models showed a varying generalizability in distinguishing liver metastases from colorectal cancer and pancreatic adenocarcinoma. |
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| DOI: | doi:10.1186/s40644-023-00612-4 |
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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/10.1186/s40644-023-00612-4 |
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| | DOI: https://doi.org/10.1186/s40644-023-00612-4 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Deep learning |
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| | Gastrointestinal |
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| | Machine learning |
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| | Metastases |
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| | Radiomics |
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| K10plus-PPN: | 187100358X |
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| Verknüpfungen: | → Zeitschrift |
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Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning / Tharmaseelan, Hishan [VerfasserIn]; 2023 (Online-Ressource)
