Title:Automated Diagnosis of Bone Metastasis by Classifying Bone Scintigrams Using
a Self-defined Deep Learning Model
Volume: 20
Author(s): Yubo Wang, Qiang Lin*, Shaofang Zhao, Xianwu Zeng*, Bowen Zheng, Yongchun Cao and Zhengxing Man
Affiliation:
- Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, China
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, China
- Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, China
- Department of Nuclear Medicine, Gansu Provincial Cancer Hospital, Lanzhou, China
Keywords:
SPECT imaging, Bone scintigrams, Metastasis detection, Lung cancer, Image classification, Deep learning.
Abstract:
Background:
Patients with cancer can develop bone metastasis when a solid tumor invades the bone, which is the third most commonly affected site by
metastatic cancer, after the lung and liver. The early detection of bone metastases is crucial for making appropriate treatment decisions and
increasing survival rates. Deep learning, a mainstream branch of machine learning, has rapidly become an effective approach to analyzing medical
images.
Objective:
To automatically diagnose bone metastasis with bone scintigraphy, in this work, we proposed to cast the bone metastasis diagnosis problem into
automated image classification by developing a deep learning-based automated classification model.
Methods:
A self-defined convolutional neural network consisting of a feature extraction sub-network and feature classification sub-network was proposed to
automatically detect lung cancer bone metastasis, with a feature extraction sub-network extracting hierarchal features from SPECT bone
scintigrams and feature classification sub-network classifying high-level features into two categories (i.e., images with metastasis and without
metastasis).
Results:
Using clinical data of SPECT bone scintigrams, the proposed model was evaluated to examine its detection accuracy. The best performance was
achieved if the two images (i.e., anterior and posterior scans) acquired from each patient were fused using pixel-wise addition operation on the
bladder-excluded images, obtaining the best scores of 0.8038, 0.8051, 0.8039, 0.8039, 0.8036, and 0.8489 for accuracy, precision, recall,
specificity, F-1 score, and AUC value, respectively.
Conclusion:
The proposed two-class classification network can predict whether an image contains lung cancer bone metastasis with the best performance as
compared to existing classical deep learning models. The high accumulation of 99mTc MDP in the urinary bladder has a negative impact on
automated diagnosis of bone metastasis. It is recommended to remove the urinary bladder before automated analysis.
