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Updated: Dec 17, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Machine learning in haematological malignancies.

Nathan Radakovich¹, Matthew Nagy¹, Aziz Nazha²

¹Cleveland Clinic Lerner College of Medicine, Cleveland Clinic, Case Western Reserve University, Cleveland OH, USA.

The Lancet. Haematology

|June 27, 2020

Summary

This summary is machine-generated.

Machine learning (ML) aids hematologic malignancy research and care by analyzing diverse data. Understanding ML is crucial for clinicians and researchers in hematology.

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Area of Science:

Computer Science
Statistics
Medicine

Background:

Machine learning (ML) offers predictive and descriptive models from data, enhancing medical research and clinical practice.
ML applications are expanding in hematologic malignancy, impacting pathology, radiology, genomics, and electronic health records.

Purpose of the Study:

To introduce fundamental machine learning concepts to those unfamiliar with the field.
To detail current machine learning applications in hematologic malignancy.
To equip clinicians with knowledge for evaluating ML-based research in hematology.

Main Methods:

This narrative review synthesizes information on machine learning principles.
It examines existing literature on ML applications in hematologic malignancy.
Key concepts for clinicians appraising ML research are summarized.

Main Results:

Machine learning is increasingly integrated into hematology research and practice.
ML tools are becoming more accessible due to lower computational costs.
The review highlights ML's growing role in diagnosis, prognosis, and treatment of blood cancers.

Conclusions:

Machine learning holds significant potential to advance hematologic malignancy care.
Both researchers and clinicians should understand ML to leverage its benefits.
This review serves as a guide for navigating ML in hematology.