Review on article of preoperative prediction in chronic hepatitis B virus patients using spectral computed tomography and machine learning

Abstract

This letter comments on the article that developed and tested a machine learning model that predicts lymphovascular invasion/perineural invasion status by combining clinical indications and spectral computed tomography characteristics accurately. We review the research content, methodology, conclusions, strengths and weaknesses of the study, and introduce follow-up research to this work.

Keywords: Gastric cancer; Spectral computed tomography; Perineural invasion; Lymphovascular invasion; Machine learning

Core Tip: Accurate preoperative assessment of gastric cancer staging and tumor aggressiveness is critical for the development of individualized treatment. Previous studies have shown that lymphovascular invasion (LVI) and perineural invasion (PNI) can predict tumor invasion and patient prognosis; therefore, preoperative LVI and PNI assessment can help oncologists identify high-risk categories of gastric cancer patients preoperatively and predict outcomes. This letter comments on a published study that showed that the accurate preoperative identification of LVI/PNI in gastric cancer can be achieved by merging clinical markers with portal venous and equilibrium phase spectral computed tomography characteristics.