Cancer Imaging (Oct 2023)

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

  • 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

DOI
https://doi.org/10.1186/s40644-023-00612-4
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 9

Abstract

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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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