Related Experiment Video
Updated: May 20, 2025
Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography
Published on: November 8, 2024
A novel skeletal muscle quantitative method and deep learning-based sarcopenia diagnosis for cervical cancer patients
Zhe Wu1,2,3, Lihua Deng1, Wanyang Wu4
1Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China.
Background:
Sarcopenia is associated with decreased survival in cervical cancer patients treated with radiotherapy. Cone-beam computed tomography (CBCT) was widely used in image-guided radiotherapy. Sarcopenia is assessed by the skeletal muscle index (SMI) of third lumbar vertebra (L3). Whereas, L3 is usually not included on the cervical cancer radiotherapy CBCT images.
Purpose:
We aimed to explore the usefulness of CBCT for evaluating SMI and deep learning (DL)-based automatic segmentation and sarcopenia diagnosis for cervical cancer radiotherapy patients. We evaluated the SMI through fifth lumbar vertebra (L5).
Methods:
First, L3, L5 skeletal muscle area (SMA) were measured on CT and CBCT. The agreement of L5 skeletal muscle segmentation on CBCT was evaluated using the intraclass correlation coefficient (ICC). The relationships between L5-SMICT and L3-SMICT, L5-SMICBCT were established and assessed by Pearson analysis, Bland-Altman plots. Second, the consequent CBCT images of 248 cervical cancer radiotherapy patients with whole L5 were collected as DL-based automatic segmentation. An independent external validation dataset was used. We proposed an end-to-end anatomical distance-guided dual branch feature fusion network to segment L5 skeletal muscle on CBCT images. The automatic segmentation results were used for sarcopenia diagnosis evaluation.
Results:
The ICC values were greater than 0.95. The Pearson correlation coefficients (PCC) between L5-SMICT and L3-SMICT is 0.894. The PCC between L5-SMICT and L5-SMICBCT is 0.917. The L3-SMICT could be estimated through L5-SMICBCT by a linear regression equation. The adjusted R2 values were greater than 0.7. The dice similarity coefficient of automatic segmentation is 87.09%. Our proposed DL network predicted sarcopenia with 84.38% accuracy and 85.71% F1-score. In external validation dataset, the sarcopenia diagnosis accuracy and F1-score are 80% and 82.61%, respectively.
Conclusion:
The SMI quantitative measurement using CBCT for cervical cancer patients is feasible. And the DL network has the potential to assist in the sarcopenia diagnosis using CBCT images.
