Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors

Abstract

In the realm of managing malignant liver tumors, the convergence of radiomics and machine learning has redefined the landscape of medical practice. The field of radiomics employs advanced algorithms to extract thousands of quantitative features (including intensity, texture, and structure) from medical images. Machine learning, including its subset deep learning, aids in the comprehensive analysis and integration of these features from diverse image sources. This potent synergy enables the prediction of responses of malignant liver tumors to various treatments and outcomes. In this comprehensive review, we examine the evolution of the field of radiomics and its procedural framework. Furthermore, the applications of radiomics combined with machine learning in the context of personalized treatment for malignant liver tumors are outlined in aspects of surgical therapy and non-surgical treatments such as ablation, transarterial chemoembolization, radiotherapy, and systemic therapies. Finally, we discuss the current challenges in the amalgamation of radiomics and machine learning in the study of malignant liver tumors and explore future opportunities.

A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning

“…By harnessing advanced technologies such as deep learning, machine learning can autonomously extract crucial texture information beneficial for disease diagnosis and prediction, thereby enhancing the sensitivity and accuracy of potential disease detection (Yang et al, 2023 ). Furthermore, machine learning personalizes medical image analysis, tailoring diagnosis and treatment to patients' unique conditions and medical histories, thus escalating treatment precision and reducing needless interventions, ultimately improving medical outcomes (Chen et al, 2023 ; Sheng et al, 2023 ; Fisher et al, 2024 ). Machine learning and deep learning have demonstrated their potential in assisting the diagnosis of Parkinson's disease, capable of characterizing disease stages and patient functional impairments to better understand the brain mechanisms of PD (Abós et al, 2017 ; Sivaranjini and Sujatha, 2019 ; Guo et al, 2022 ).…”

“…Additionally, machine learning uncovers and learns hidden complex patterns and features within medical images during texture analysis. By harnessing advanced technologies such as deep learning, machine learning can autonomously extract crucial texture information beneficial for disease diagnosis and prediction, thereby enhancing the sensitivity and accuracy of potential disease detection (Yang et al, 2023). Furthermore, machine learning personalizes medical image analysis, tailoring diagnosis and treatment to patients' unique conditions and medical histories, thus escalating treatment precision and reducing needless interventions, ultimately improving medical outcomes (Chen et al, 2023;Sheng et al, 2023;Fisher et al, 2024).…”