Treatment response prediction in rectal cancer patients: A radiomics study of multimodality imaging methods

IF 2.3 4区医学 Q3 ENGINEERING, BIOMEDICAL

Medical Engineering & Physics Pub Date : 2025-09-11 DOI:10.1016/j.medengphy.2025.104434

Yan Huang , Le Lin , Shuke Sun , Huande Hong

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引用次数: 0

Abstract

Purpose

The present work aims to assess the correlation of radiomics textural features derived from computed tomography (CT), magnetic resonance imaging (MRI), and endorectal ultrasound (EUS) images, combined with dosimetric and clinical features, to predict treatment response in patients with rectal cancer using machine learning algorithms.

Methods

Data from 84 individuals diagnosed with locally advanced rectal cancer (LARC) were utilized, and radiomic features were extracted from the specified region of interest. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), and Recursive Feature Elimination (RFE). Predictive modeling employed machine learning algorithms, including Support Vector Machine (SVM) and Logistic Regression (LR). Model performance was assessed based on metrics including accuracy (ACC), area under the receiver operating characteristic curve (AUC), precision, sensitivity, and specificity.

Results

For CT images, the MRMR method (for original images) and RFE (with a wavelet filter), combined with the LR model, achieved the best performance (ACC: 0.79; AUC: 0.78). The highest predictive performance for MRI radiomic features was obtained using MRMR and the SVM model for original images (ACC: 0.88; AUC: 0.87). Furthermore, for images with the wavelet filter, the combination of RFE and the LR model yielded the best results (ACC: 0.78; AUC: 0.87). For EUS images, the MRMR and LR models showed the best predictive performance for both original (ACC: 0.89; AUC: 0.89) and filtered images (ACC: 0.81; AUC: 0.80).

Conclusion

The findings indicate that radiomics features obtained from pretreatment CT, MRI, and EUS images have the potential to accurately predict treatment response in patients with LARC. The SVM and LR classifiers, when combined with MRMR and RFE feature selection algorithms and the wavelet filter, demonstrated robust predictive performance. Among the different imaging modalities, EUS produced the best results in terms of ACC and AUC values.

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直肠癌患者的治疗反应预测：多模态成像方法的放射组学研究

目的本研究旨在评估计算机断层扫描（CT）、磁共振成像（MRI）和直肠内超声（EUS）图像的放射组学纹理特征的相关性，并结合剂量学和临床特征，利用机器学习算法预测直肠癌患者的治疗反应。方法利用84例局部晚期直肠癌（LARC）患者的数据，提取特定感兴趣区域的放射学特征。使用最小绝对收缩和选择算子（Lasso），最小冗余最大相关性（MRMR）和递归特征消除（RFE）进行特征选择。预测建模采用机器学习算法，包括支持向量机（SVM）和逻辑回归（LR）。根据准确度（ACC）、受试者工作特征曲线下面积（AUC）、精确度、灵敏度和特异性等指标评估模型的性能。结果对于CT图像，MRMR（原始图像）和RFE（带小波滤波）结合LR模型获得了最佳性能（ACC: 0.79; AUC: 0.78）。使用MRMR和SVM模型对原始图像的MRI放射学特征预测性能最高（ACC: 0.88; AUC: 0.87）。此外，对于小波滤波后的图像，RFE和LR模型的组合效果最好（ACC: 0.78; AUC: 0.87）。对于EUS图像，MRMR和LR模型对原始图像（ACC: 0.89; AUC: 0.89）和过滤图像（ACC: 0.81; AUC: 0.80）都显示出最佳的预测性能。结论从CT、MRI和EUS图像预处理获得的放射组学特征有可能准确预测LARC患者的治疗反应。当SVM和LR分类器与MRMR和RFE特征选择算法以及小波滤波器相结合时，显示出鲁棒的预测性能。在不同的成像方式中，EUS在ACC和AUC值方面的效果最好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Medical Engineering & Physics 工程技术-工程：生物医学

CiteScore

4.30

自引率

4.50%

发文量

172

审稿时长

3.0 months

期刊介绍： Medical Engineering & Physics provides a forum for the publication of the latest developments in biomedical engineering, and reflects the essential multidisciplinary nature of the subject. The journal publishes in-depth critical reviews, scientific papers and technical notes. Our focus encompasses the application of the basic principles of physics and engineering to the development of medical devices and technology, with the ultimate aim of producing improvements in the quality of health care.Topics covered include biomechanics, biomaterials, mechanobiology, rehabilitation engineering, biomedical signal processing and medical device development. Medical Engineering & Physics aims to keep both engineers and clinicians abreast of the latest applications of technology to health care.