Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods.

IF 2.2 4区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Tomography Pub Date : 2025-03-13 DOI:10.3390/tomography11030033

Amir Moslemi, Laurentius Oscar Osapoetra, Archya Dasgupta, Schontal Halstead, David Alberico, Maureen Trudeau, Sonal Gandhi, Andrea Eisen, Frances Wright, Nicole Look-Hong, Belinda Curpen, Michael Kolios, Gregory J Czarnota

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引用次数: 0

Abstract

Rationale: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response of NAC for patients with LABC before initiating treatment would be valuable to customize therapies and ensure the delivery of effective care.

Objective: Our objective was to develop predictive measures of tumor response to NAC prior to starting for LABC using machine learning and textural computed tomography (CT) features in different level of frequencies.

Materials and methods: A total of 851 textural biomarkers were determined from CT images and their wavelet coefficients for 117 patients with LABC to evaluate the response to NAC. A machine learning pipeline was designed to classify response to NAC treatment for patients with LABC. For training predictive models, three models including all features (wavelet and original image features), only wavelet and only original-image features were considered. We determined features from CT images in different level of frequencies using wavelet transform. Additionally, we conducted a comparison of feature selection methods including mRMR, Relief, Rref QR decomposition, nonnegative matrix factorization and perturbation theory feature selection techniques.

Results: Of the 117 patients with LABC evaluated, 82 (70%) had clinical-pathological response to chemotherapy and 35 (30%) had no response to chemotherapy. The best performance for hold-out data splitting was obtained using the KNN classifier using the Top-5 features, which were obtained by mRMR, for all features (accuracy = 77%, specificity = 80%, sensitivity = 56%, and balanced-accuracy = 68%). Likewise, the best performance for leave-one-out data splitting could be obtained by the KNN classifier using the Top-5 features, which was obtained by mRMR, for all features (accuracy = 75%, specificity = 76%, sensitivity = 62%, and balanced-accuracy = 72%).

Conclusions: The combination of original textural features and wavelet features results in a greater predictive accuracy of NAC response for LABC patients. This predictive model can be utilized to predict treatment outcomes prior to starting, and clinicians can use it as a recommender system to modify treatment.

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利用CT纹理特征和机器学习预测局部晚期乳腺癌患者治疗前化疗反应：特征选择方法的比较

理由：新辅助化疗（NAC）是局部晚期乳腺癌（LABC）治疗的关键要素。在开始治疗前预测 LABC 患者对 NAC 的反应对定制疗法和确保提供有效治疗非常重要：我们的目标是利用机器学习和不同频率水平的纹理计算机断层扫描（CT）特征，在开始治疗 LABC 之前，开发肿瘤对 NAC 反应的预测指标：从117名LABC患者的CT图像及其小波系数中共确定了851个纹理生物标志物，以评估对NAC的反应。设计了一个机器学习管道来对 LABC 患者的 NAC 治疗反应进行分类。在训练预测模型时，考虑了包括所有特征（小波和原始图像特征）、仅小波和仅原始图像特征在内的三种模型。我们使用小波变换从 CT 图像中确定不同频率水平的特征。此外，我们还比较了各种特征选择方法，包括 mRMR、Relief、Rref QR 分解、非负矩阵因式分解和扰动理论特征选择技术：在接受评估的 117 名 LABC 患者中，82 人（70%）对化疗有临床病理反应，35 人（30%）对化疗无反应。采用 KNN 分类器，使用 mRMR 得出的所有特征的前 5 个特征，获得了最佳的暂缓数据分割性能（准确率 = 77%，特异性 = 80%，灵敏度 = 56%，平衡准确率 = 68%）。同样，KNN 分类器使用 mRMR 得出的所有特征的前 5 个特征（准确率 = 75%、特异性 = 76%、灵敏度 = 62%、平衡准确率 = 72%），也能获得最佳的留空数据分割性能：原始纹理特征和小波特征的结合提高了对 LABC 患者 NAC 反应的预测准确性。该预测模型可用于在开始治疗前预测治疗结果，临床医生可将其用作修改治疗的推荐系统。

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

Tomography Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

2.70

自引率

10.50%

发文量

222

期刊介绍： TomographyTM publishes basic (technical and pre-clinical) and clinical scientific articles which involve the advancement of imaging technologies. Tomography encompasses studies that use single or multiple imaging modalities including for example CT, US, PET, SPECT, MR and hyperpolarization technologies, as well as optical modalities (i.e. bioluminescence, photoacoustic, endomicroscopy, fiber optic imaging and optical computed tomography) in basic sciences, engineering, preclinical and clinical medicine. Tomography also welcomes studies involving exploration and refinement of contrast mechanisms and image-derived metrics within and across modalities toward the development of novel imaging probes for image-based feedback and intervention. The use of imaging in biology and medicine provides unparalleled opportunities to noninvasively interrogate tissues to obtain real-time dynamic and quantitative information required for diagnosis and response to interventions and to follow evolving pathological conditions. As multi-modal studies and the complexities of imaging technologies themselves are ever increasing to provide advanced information to scientists and clinicians. Tomography provides a unique publication venue allowing investigators the opportunity to more precisely communicate integrated findings related to the diverse and heterogeneous features associated with underlying anatomical, physiological, functional, metabolic and molecular genetic activities of normal and diseased tissue. Thus Tomography publishes peer-reviewed articles which involve the broad use of imaging of any tissue and disease type including both preclinical and clinical investigations. In addition, hardware/software along with chemical and molecular probe advances are welcome as they are deemed to significantly contribute towards the long-term goal of improving the overall impact of imaging on scientific and clinical discovery.