Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning
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...
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| Hauptverfasser: | , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2023
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| In: |
Cancer imaging
Year: 2023, Jahrgang: 23, Pages: 1-9 |
| ISSN: | 1470-7330 |
| DOI: | 10.1186/s40644-023-00612-4 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s40644-023-00612-4
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| Verfasserangaben: | 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 |
MARC
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| 245 | 1 | 0 | |a Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning |c 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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| 520 | |a 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. | ||
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Gastrointestinal | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Metastases | |
| 650 | 4 | |a Radiomics | |
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