Artificial Intelligence Algorithm in Classification and Recognition of Primary Hepatic Carcinoma Images under Magnetic Resonance Imaging

Abstract

This study aimed to discuss the application value of the bias field correction algorithm in magnetic resonance imaging (MRI) images of patients with primary hepatic carcinoma (PHC). In total, 52 patients with PHC were selected as the experimental group and divided into three subgroups: mild (15 cases), moderate (19 cases), and severe (18 cases) according to pathological grading. Another 52 patients with hepatic nodules in the same period were included in the control group. All the patients underwent dynamic contrast-enhanced (DCE) MRI examination, and the image qualities of MRI before and after bias field correction were compared. The DCE-MRI perfusion parameters were measured, including the transport constant Ktrans, reverse rate constant Kep, extravascular extracellular volume fraction (Ve), plasma volume (Vp), microvascular density (MVD), hepatic artery perfusion index (HPI), mean transit time of contrast agent (MTT), time to peak (TTP), blood volume (BV), hepatic arterial perfusion (HAP), full perfusion (FP), and portal venous perfusion (PVP). It was found that the sensitivity (93.63%), specificity (71.62%), positive predictive value (95.63%), negative predictive value (71.62%), and accuracy (90.01%) of MRI examination processed by the bias field correction algorithm were all significantly greater than those before processing P < 0.05 . The Ktrans, Kep, Ve, Vp, and MVD of patients in the experimental group were significantly larger than those of the control group, and severe group> moderate group> mild group P < 0.05 . HPI, MTT, TTP, BV, and HAP of patients in the experimental group were also significantly greater than those of the control group, which was shown as severe group > moderate group > mild group P < 0.05 . FP and PVP of the experimental group were significantly lower than those of the control group, and severe group < moderate group < mild group P < 0.05 . It was suggested that in MRI images of patients with PHC, the bias field correction algorithm could significantly improve the diagnosis rate. Each perfusion parameter was related to the pathological grading, which could be used to evaluate the prognosis of patients.

Artificial Intelligence and Machine Learning in Hepatocellular Carcinoma Screening, Diagnosis and Treatment - A Comprehensive Systematic Review

“…He et al, demonstrated a notable improvement in the accuracy of diagnosing primary hepatic cancer (PHC) in patients by employing a bias field correction algorithm in MRI images. This underscores the promising capability of AI algorithms in enhancing the diagnosis and prognostic evaluation of MRI scans [29]. In another study, Zeng et al, illustrated that the AI model accurately estimated the expression of ABRS from histological slides, and this estimation correlated with increased progression-free survival in patients treated with atezolizumab-bevacizumab [30].…”

“…In a study, researchers refined the diagnosis of primary hepatic cancer (PHC) by applying bias field modification to magnetic resonance imaging. The study revealed that artificial intelligence enhances the accuracy of imaging diagnosis [29]. Another study assessed the responsiveness of hepatocellular carcinoma (HCC) patients to atezolizumab-bevacizumab treatment, employing an artificial intelligence (AI) pathology model.…”

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