We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Differentiation between spinal multiple myeloma and metastases originated from lung using multi-view attention-guided network.
- Authors
Kaili Chen; Jiashi Cao; Xin Zhang; Xiang Wang; Xiangyu Zhao; Qingchu Li; Song Chen; Peng Wang; Tielong Liu; Juan Du; Shiyuan Liu; Lichi Zhang
- Abstract
Purpose: Multiple myeloma (MM) and metastasis originated are the two common malignancy diseases in the spine. They usually show similar imaging patterns and are highly demanded to differentiate for precision diagnosis and treatment planning. The objective of this study is therefore to construct a novel deep-learning-based method for effective differentiation of two diseases, with the comparative study of traditional radiomics analysis. Methods: We retrospectively enrolled a total of 217 patients with 269 lesions, who were diagnosed with spinal MM (79 cases, 81 lesions) or spinal metastases originated from lung cancer (138 cases, 188 lesions) confirmed by postoperative pathology. Magnetic resonance imaging (MRI) sequences of all patients were collected and reviewed. A novel deep learning model of the Multi-view Attention-Guided Network (MAGN) was constructed based on contrast-enhanced T1WI (CET1) sequences. The constructed model extracts features from three views (sagittal, coronal and axial) and fused them for a more comprehensive differentiation analysis, and the attention guidance strategy is adopted for improving the classification performance, and increasing the interpretability of the method. The diagnostic efficiency among MAGN, radiomics model and the radiologist assessment were compared by the area under the receiver operating characteristic curve (AUC). Results: Ablation studies were conducted to demonstrate the validity of multiview fusion and attention guidance strategies: It has shown that the diagnostic model using multi-view fusion achieved higher diagnostic performance [ACC (0.79), AUC (0.77) and F1-score (0.67)] than those using single-view (sagittal, axial and coronal) images. Besides, MAGN incorporating attention guidance strategy further boosted performance as the ACC, AUC and F1-scores reached 0.81, 0.78 and 0.71, respectively. In addition, the MAGN outperforms the radiomics methods and radiologist assessment. The highest ACC, AUC and F1-score for the latter two methods were 0.71, 0.76 & 0.54, and 0.69, 0.71, & 0.65, respectively. Conclusions: The proposed MAGN can achieve satisfactory performance in differentiating spinal MM between metastases originating from lung cancer, which also outperforms the radiomics method and radiologist assessment.
- Subjects
MULTIPLE myeloma; RECEIVER operating characteristic curves; MAGNETIC resonance imaging; RADIOMICS; METASTASIS
- Publication
Frontiers in Oncology, 2022, Vol 12, p1
- ISSN
2234-943X
- Publication type
Article
- DOI
10.3389/fonc.2022.981769