Abstract
BackgroundWhole-body bone scan is the widely used tool for surveying bone metastases caused by various primary solid tumors including lung cancer. Scintigraphic images are characterized by low specificity, bringing a significant challenge to manual analysis of images by nuclear medicine physicians. Convolutional neural network can be used to develop automated classification of images by automatically extracting hierarchal features and classifying high-level features into classes.ResultsUsing convolutional neural network, a multi-class classification model has been developed to detect skeletal metastasis caused by lung cancer using clinical whole-body scintigraphic images. The proposed method consisted of image aggregation, hierarchal feature extraction, and high-level feature classification. Experimental evaluations on a set of clinical scintigraphic images have shown that the proposed multi-class classification network is workable for automated detection of lung cancer-caused metastasis, with achieving average scores of 0.7782, 0.7799, 0.7823, 0.7764, and 0.8364 for accuracy, precision, recall, F-1 score, and AUC value, respectively.ConclusionsThe proposed multi-class classification model can not only predict whether an image contains lung cancer-caused metastasis, but also differentiate between subclasses of lung cancer (i.e., adenocarcinoma and non-adenocarcinoma). On the context of two-class (i.e., the metastatic and non-metastatic) classification, the proposed model obtained a higher score of 0.8310 for accuracy metric.
Highlights
Whole-body bone scan is the widely used tool for surveying bone metastases caused by various primary solid tumors including lung cancer
Targeting at automated detection of skeletal metastasis caused by lung cancer, in this work, we propose a Convolutional neural network (CNN)-based multiclass classification network to classify whole-body scintigraphic images acquired from patients with clinically diagnosed lung cancer using a Single Photon Emission Computed Tomography (SPECT) imaging device (i.e., GE SPECT Millennium MPR)
Each SPECT image was stored in a DICOM (Digital Imaging and Communications in Medicine) file with the imaging size of 256 × 1024
Summary
Whole-body bone scan is the widely used tool for surveying bone metastases caused by various primary solid tumors including lung cancer. Scintigraphic images are characterized by low specificity, bringing a significant challenge to manual analysis of images by nuclear medicine physicians. Skeletal metastasis is common in several of prevalent cancers including prostate, breast, and lung cancers [1], with 80% of all skeletal metastatic lesions originating from one of these primary sites [2]. Skeletal scintigraphy (bone scan) and positron emission tomography (PET) are commonly used for surveying bone metastasis [5, 6]. Bone scan is typically characterized by high sensitivity but low specificity, bringing significant challenge to manual analysis of bone scan images by nuclear medicine physicians. The reasons of low specificity are multi-fold, mainly including low spatial resolution, accumulation of radiopharmaceutical in normal skeletal structures, soft tissues or viscera, and uptake in benign processes [7]
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