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Related Experiment Video

Updated: Aug 18, 2025

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

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Machine Learning Radiomics Model for External and Internal Respiratory Motion Correlation Prediction in Lung Tumor.

Xiangyu Zhang¹, Xinyu Song^1,2, Guangjun Li¹

¹Radiotherapy Physics and Technology Center, Cancer Center, 34753West China Hospital, Sichuan University, Chengdu, China.

Technology in Cancer Research & Treatment

|December 8, 2022

Summary

This summary is machine-generated.

This study introduces a machine learning model using computed tomography (CT) radiomic features to predict lung tumor motion correlation. The model effectively extracts tumor motion characteristics, improving respiratory motion management strategies.

Keywords:

correlation lung tumor machine learning radiomics respiratory motion

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Area of Science:

Medical Imaging
Radiomics
Machine Learning

Background:

Lung tumor motion complexity necessitates individual external and internal correlation determination for indirect tumor tracking.
Pre-treatment clinical characteristics are insufficient for predicting this correlation.

Purpose of the Study:

To develop and validate a machine learning model for predicting external/internal lung tumor motion correlation.
To utilize computed tomography (CT) radiomic features for this prediction.

Main Methods:

Retrospective analysis of 4-dimensional CT (4DCT) images from 67 patients.
Calculation of lung tumor external/internal correlation using Spearman's rank correlation coefficient.
Feature selection using a light gradient boosting machine (LightGBM) with recursive elimination, followed by model establishment and performance assessment via receiver operating characteristics, sensitivity, and specificity.

Main Results:

Texture features were identified as crucial for predicting external/internal correlation.
The developed models demonstrated high performance, with Area Under the Curve (AUC) values reaching 0.946 for the 0.8 threshold model.
Sensitivities ranged from 0.800 to 0.864, and specificities ranged from 0.771 to 0.936 across different classification thresholds.

Conclusions:

Radiomics is a powerful tool for predicting respiratory motion correlation in lung tumors.
The proposed machine learning framework enhances lung tumor motion management strategies.