Research Topic

Machine Learning Algorithm and Radiomics Analysis Based on Radiologic Imaging in Diagnosis and Prediction of Liver Tumors

About this Research Topic

Early and accurate diagnosis of liver tumors is of vital importance in clinical decision-making and treatment. More recently, as an emerging method for medical image processing, machine learning algorithms, and radiomics analysis are used to convert medical images into high-dimensional, mineable features that reflect underlying pathophysiological information. Radiomics employs a variety of state-of-the-art machine learning or deep learning techniques to complete a variety of clinical tasks, which has great potential for the diagnosis and treatment of liver tumors. In the future, these researches might greatly push forward the development of precision medicine.

This Research Topic aims to bring discussions on advances in a machine learning or radiomics analysis system for the diagnosis and clinical behavior prediction of liver tumors based on multi-parametric ultrasound, CT, or MRI imaging methods. We welcome submissions of Original Research and Review articles, focusing on but not limited to the following sub-topics:

• Machine learning and radiomics analysis in the preoperative diagnosis of liver tumors
• Radiomics analysis in predicting the outcomes of liver surgery or minimally invasive treatment
• Application of machine learning and AI algorithm during surgical treatment of liver cancers
• Radiogenomics in liver tumors
• Radiomics in the precision medicine research of liver tumors


Keywords: Liver Tumors, Radiological Imaging, Machine Learning, Radiomics, Diagnosis, Prediction


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Early and accurate diagnosis of liver tumors is of vital importance in clinical decision-making and treatment. More recently, as an emerging method for medical image processing, machine learning algorithms, and radiomics analysis are used to convert medical images into high-dimensional, mineable features that reflect underlying pathophysiological information. Radiomics employs a variety of state-of-the-art machine learning or deep learning techniques to complete a variety of clinical tasks, which has great potential for the diagnosis and treatment of liver tumors. In the future, these researches might greatly push forward the development of precision medicine.

This Research Topic aims to bring discussions on advances in a machine learning or radiomics analysis system for the diagnosis and clinical behavior prediction of liver tumors based on multi-parametric ultrasound, CT, or MRI imaging methods. We welcome submissions of Original Research and Review articles, focusing on but not limited to the following sub-topics:

• Machine learning and radiomics analysis in the preoperative diagnosis of liver tumors
• Radiomics analysis in predicting the outcomes of liver surgery or minimally invasive treatment
• Application of machine learning and AI algorithm during surgical treatment of liver cancers
• Radiogenomics in liver tumors
• Radiomics in the precision medicine research of liver tumors


Keywords: Liver Tumors, Radiological Imaging, Machine Learning, Radiomics, Diagnosis, Prediction


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

31 January 2021 Abstract
31 May 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

31 January 2021 Abstract
31 May 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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