Cirrhosis detection from CT scans
Hepatic cirrhosis, also known as liver cirrhosis, is an advanced liver disease, affecting about 1 in 400 adults in the US. The standard examination used for patient diagnosis and treatment is abdominal computed tomography (CT). However, an accurate assessment of the liver’s condition takes time, depends on the user (hence lacks reproducibility), and may be inaccurate. In one of our projects, we addressed these research gaps and proposed an end-to-end and reproducible approach for detecting cirrhosis from CT.
What we did
In our approach, we benefit from clinically-inspired features that reflect the patient’s characteristics which are often investigated by experienced radiologists during the screening process.
Such features, including – among others – the volume distribution within the CT scan, liver bluntness or the liver surface nodularity, are coupled with the radiomic ones extracted from the liver, and from the suggested region of interest which captures the liver’s boundary (afterwards, we select a subset of the most discriminative features to enhance the interpretability of the system).
We introduced an algorithm to extract the region of interest corresponding to the rectified boundary of the liver, from which the radiomic features could be extracted – we hypothesized that the features that relate to the various stages of fibrosis are manifested near the liver’s boundary.
To quantify the capabilities of the algorithm, we followed rigorous cross-validation over two patient cohorts, spanning across 241 portal venous CT scans acquired in a clinical trial for 241 patients, whereas the public benchmark included 32 portal venous CT scans obtained for 32 healthy potential liver donors.
The detailed results are reported and thoroughly discussed in the paper: Krzysztof Kotowski, Damian Kucharski, Bartosz Machura, Szymon Adamski, Benjamín Gutierrez Becker, Agata Krason, Lukasz Zarudzki, Jean Tessier, Jakub Nalepa, Detecting liver cirrhosis in computed tomography scans using clinically-inspired and radiomic features, Computers in Biology and Medicine, Volume 152, 2023, 106378, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2022.106378.