Radiomics-based machine learning classifiers have shown potential for detecting bone metastases (BM) and for evaluating BM response to radiotherapy (RT). However, current radiomics models require large datasets of images with expert-segmented 3D regions of interest (ROIs). Full ROI segmentation is time consuming and oncologists often outline just RT treatment fields in clinical practice. This presents a challenge for real-world radiomics research. As such, a method that simplifies BM identification but does not compromise the power of radiomics is needed. The objective of this study was to investigate the feasibility of radiomics models for BM detection using lesion-center-based geometric ROIs. The planning-CT images of 170 patients with non-metastatic lung cancer and 189 patients with spinal BM were used. The point locations of 631 BM and 674 healthy bone (HB) regions were identified by experts. ROIs with various geometric shapes were centered and automatically delineated on the identified locations, and 107 radiomics features were extracted. Various feature selection methods and machine learning classifiers were evaluated. Our point-based radiomics pipeline was successful in differentiating BM from HB. Lesion-center-based segmentation approach greatly simplifies the process of preparing images for use in radiomics studies and avoids the bottleneck of full ROI segmentation.
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 cancer patients. Artificial intelligence can aid in the extraction of objective pain biomarkers for cancer patients with bone metastases.
OBJECTIVE
To develop and evaluate a scalable natural language processing (NLP) and radiomics-based machine learning pipeline to differentiate between painless and painful bone metastasis (BM) lesions in simulation-CT images using imaging features (biomarkers) extracted from lesion-centerpoint-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 centerpoints were manually pinpointed on CT images by radiation oncologists. Nested ROIs with various diameters were automatically delineated around these expert-identified BM centerpoints, and radiomics features were extracted from each ROI. The Synthetic Minority Oversampling Technique re-sampling technique, 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 (AUC).
RESULTS
Radiation therapy consultation notes and simulation-CT images of 176 (mean age ± SD, 66 ± 14 y; 95 male) thoracic spine BM patients were used in this study. After BM centerpoint 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 AUC of our best performing model (Neural Network classifier on an ensemble ROI) were 0.82 (132 of 163), 0.59 (16 of 27), 0.85 (116 of 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 just center points to identify BM lesions in CT images.
Radiomics-based machine learning classifiers have shown potential for detecting bone metastases (BM) and for evaluating BM response to radiotherapy (RT). However, current radiomics pipelines require large datasets of images with expert-segmented 3D regions of interest (ROIs). Full ROI segmentation is time consuming and oncologists often outline just RT treatment fields in clinical practice. This presents a challenge for real-world radiomics research. As such, a method that simplifies BM identification but does not compromise the power of radiomics is needed. The objective of this study was to investigate the feasibility of a radiomics pipeline for BM detection using lesion-center-based geometric ROIs. The simulation-CT images of 170 patients with non-metastatic lung cancer and 189 patients with spinal BM were used. The point locations of 631 BM and 674 healthy bone (HB) regions were identified by experts. ROIs with various geometric shapes were centered and automatically delineated on the identified locations, and 107 radiomics features were extracted. Various re-sampling techniques, feature selection methods, and machine learning classifiers were evaluated. Our point-based radiomics pipeline was successful in differentiating BM from HB. This approach greatly simplifies the process of preparing images for use in radiomics studies and avoids the bottleneck of full ROI segmentation.
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