Quantitative imaging in medicine and surgery · 2025
Automatic and observable spinal cord compression grading system for lung cancer spinal metastasis.
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Wang Gengyuan, Lai Guanquan, Wang Genghui, Zhang Yu
Аннотация
BACKGROUND: Epidural spinal cord compression (ESCC) assessment is a critical step in surgical intervention for lung cancer spinal metastasis. However, clinicians can only assess compression grades through manual observation, which is subjective and inefficient. The metastatic cancer features on magnetic resonance imaging (MRI) slices are complex and difficult to identify, and there are no publicly labeled datasets currently. The objective of this study was to develop an automatic and efficient system for assessing spinal cord compression grading based on multi-tissue and feature segmentation.
METHODS: We constructed a dataset containing 577 lung cancer spinal metastasis MRI slices with multi-tissue semantic segmentation masks and ESCC grading. We then proposed a progressive approach for grading ESCC via segmentation and transfer learning (TL) classification to improve clinical assessment accuracy and efficiency. First, we used an improved encoder-decoder segmentation network with a pyramid pooling module (PPM) on the bottleneck and cross-attention modules on the skip connections to segment the compression-related tissues, such as the spine, spinal cord, and bone tumors. Secondly, we proposed a dual attention network (DAN) with position and channel attention modules, and pre-trained it on ImageNet1000 to improve network performance. Finally, we used the multi-tissue features of the previous segmentation as training data and fine-tuned the DAN via TL to achieve accurate compression grading.
RESULTS: The segmentation results achieved an average pixel accuracy of 0.819 and an average intersection over union of 0.711. The classification accuracy reached 0.95, and the classification precision was 0.89. These results demonstrate the superiority of the proposed few-shot feature recognition and classification method in multi-target recognition. Compared to the traditional segmentation and classification models, our model exhibits enhanced performance and effectiveness in grading spinal compression.
CONCLUSIONS: The spinal cord compression grading in lung cancer spinal metastasis is an important surgical indication. The system developed herein has been verified with an accuracy of 0.95 in compression grading, which will be helpful for orthopedic surgeons to determine reasonable treatment plans for patients.
METHODS: We constructed a dataset containing 577 lung cancer spinal metastasis MRI slices with multi-tissue semantic segmentation masks and ESCC grading. We then proposed a progressive approach for grading ESCC via segmentation and transfer learning (TL) classification to improve clinical assessment accuracy and efficiency. First, we used an improved encoder-decoder segmentation network with a pyramid pooling module (PPM) on the bottleneck and cross-attention modules on the skip connections to segment the compression-related tissues, such as the spine, spinal cord, and bone tumors. Secondly, we proposed a dual attention network (DAN) with position and channel attention modules, and pre-trained it on ImageNet1000 to improve network performance. Finally, we used the multi-tissue features of the previous segmentation as training data and fine-tuned the DAN via TL to achieve accurate compression grading.
RESULTS: The segmentation results achieved an average pixel accuracy of 0.819 and an average intersection over union of 0.711. The classification accuracy reached 0.95, and the classification precision was 0.89. These results demonstrate the superiority of the proposed few-shot feature recognition and classification method in multi-target recognition. Compared to the traditional segmentation and classification models, our model exhibits enhanced performance and effectiveness in grading spinal compression.
CONCLUSIONS: The spinal cord compression grading in lung cancer spinal metastasis is an important surgical indication. The system developed herein has been verified with an accuracy of 0.95 in compression grading, which will be helpful for orthopedic surgeons to determine reasonable treatment plans for patients.