背景:识别客观的疼痛生物标志物有助于提高对疼痛的理解,以及其预后和更好的管理。因此,它有可能改善癌症患者的生活质量。人工智能可以帮助提取患有骨转移(BMs)的癌症患者的客观疼痛生物标志物。
目的:本研究旨在开发和评估可扩展的自然语言处理(NLP)和基于影像组学的机器学习管道,以使用从基于病灶中心点的感兴趣区域(ROI)提取的成像特征(生物标志物)来区分模拟计算机断层扫描(CT)图像中的无痛和疼痛的BM病灶。
方法:这项回顾性研究包括2016年1月至2019年9月在我们的综合癌症中心接受胸椎BM姑息性放疗的患者。使用NLP管道从放射肿瘤学咨询笔记中自动提取医师报告的疼痛评分。BM中心点由放射肿瘤学家在CT图像上手动精确定位。在这些专家识别的BM中心点周围自动描绘了具有各种直径的嵌套ROI,并从每个ROI中提取影像组学特征。合成少数过采样技术重采样,最小绝对收缩和选择算子特征选择方法,并使用精度评估各种机器学习分类器,召回,F1分数,和接收器工作特性曲线下的面积。
结果:本研究纳入了176例胸椎BM患者(平均年龄66岁,SD14岁;男性95例)的放射治疗咨询记录和模拟CT图像。BM中心点识别后,使用pyradiogomics从每个球形ROI中提取107个radiomics特征。数据分为70%和30%的训练和坚持测试集,分别。在测试集中,准确性,灵敏度,特异性,我们表现最好的模型(集成ROI上的神经网络分类器)的接收器工作特征曲线下的面积为0.82(132/163),0.59(16/27),0.85(116/136),和0.83。
结论:我们基于NLP和影像组学的机器学习管道成功地区分了疼痛和无痛的BM病变。通过使用NLP从临床记录中提取疼痛评分并且通过仅需要中心点来识别CT图像中的BM病变,其本质上是可扩展的。
BACKGROUND: The identification of objective pain biomarkers can contribute to an improved understanding of pain, as well as its prognosis and better management. Hence, it has the potential to improve the quality of life of patients with cancer. Artificial intelligence can aid in the extraction of objective pain biomarkers for patients with cancer with bone metastases (BMs).
OBJECTIVE: This
study aimed to develop and evaluate a scalable natural language processing (NLP)- and radiomics-based machine learning pipeline to differentiate between painless and painful BM lesions in simulation computed tomography (CT) images using imaging features (biomarkers) extracted from lesion center point-based regions of interest (ROIs).
METHODS: Patients treated at our comprehensive cancer center who received palliative radiotherapy for thoracic spine BM between January 2016 and September 2019 were included in this retrospective
study. Physician-reported pain scores were extracted automatically from radiation oncology consultation notes using an NLP pipeline. BM center points were manually pinpointed on CT images by radiation oncologists. Nested ROIs with various diameters were automatically delineated around these expert-identified BM center points, and radiomics features were extracted from each ROI. Synthetic Minority Oversampling Technique resampling, the Least Absolute Shrinkage And Selection Operator feature selection method, and various machine learning classifiers were evaluated using precision, recall, F1-score, and area under the receiver operating characteristic curve.
RESULTS: Radiation therapy consultation notes and simulation CT images of 176 patients (mean age 66, SD 14 years; 95 males) with thoracic spine BM were included in this
study. After BM center point identification, 107 radiomics features were extracted from each spherical ROI using pyradiomics. Data were divided into 70% and 30% training and hold-out test sets, respectively. In the test set, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of our best performing model (neural network classifier on an ensemble ROI) were 0.82 (132/163), 0.59 (16/27), 0.85 (116/136), and 0.83, respectively.
CONCLUSIONS: Our NLP- and radiomics-based machine learning pipeline was successful in differentiating between painful and painless BM lesions. It is intrinsically scalable by using NLP to extract pain scores from clinical notes and by requiring only center points to identify BM lesions in CT images.