JMIR Publications

ABSTRACT

Background:

Over the past few years, radiomics for the detection of intrahepatic cholangiocarcinoma (ICC) has been extensively studied. However, systematic evidence is lacking in the utilization of radiomics in this domain, which hinders its further development.

Objective:

To address this gap, our study delved into the status-quo and application value of radiomics in ICC and aimed to offer evidence-based support to promote its systematic application in this field.

Methods:

PubMed, Web of Science, Cochrane Library, and Embase were comprehensively retrieved to determine relevant original studies. The study quality was appraised through the Radiomics Quality Score (RQS). Additionally, subgroup analyses were undertaken according to datasets (training and validation sets), imaging sources, and model types.

Results:

58 studies encompassing 12,903 patients were eligible, with an average RQS of 9.21. Radiomics-based machine learning (ML) was mainly used to diagnose ICC, microvascular invasion (MVI), gene mutations, perineural invasion (PNI), lymph node (LN) positivity, and tertiary lymphoid structures (TLSs), and predict overall survival (OS) and recurrence. The c-index, sensitivity, and specificity of ML constructed utilizing both radiomics and clinical features (CFs) for diagnosing ICC were 0.912 (95% CI: 0.889-0.935), 0.77 (95% CI: 0.72-0.81), and 0.90 (95% CI: 0.86-0.92). Additional analyses showed that radiomics achieved promising accuracy in other task outcomes.

Conclusions:

Radiomics-based ML demonstrates excellent accuracy in the clinical diagnosis of ICC. However, the number of included studies for specific tasks, such as diagnosing PNI and TLSs, remain limited. Future researchers should delve into the application of radiomics in these areas to enhance its clinical utility.