Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning

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 p...

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Bibliographic Details
Main Authors: 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, Matthias F. Froelich
Format: Article
Language:English
Published: BMC 2023-10-01
Series:Cancer Imaging
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Online Access:https://doi.org/10.1186/s40644-023-00612-4
Description
Summary: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.
ISSN:1470-7330