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 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 a

SUBMITTER: Tharmaseelan H 

PROVIDER: S-EPMC10557291 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature