Differentiation between spinal multiple myeloma and metastases originated from lung using multi-view attention-guided network

Kaili Chen; Jiashi Cao; Xin Zhang; Xiang Wang; Xiangyu Zhao; Qingchu Li; Song Chen; Peng Wang; Tielong Liu; Juan Du; Shiyuan Liu; Lichi Zhang

doi:10.3389/fonc.2022.981769

Differentiation between spinal multiple myeloma and metastases originated from lung using multi-view attention-guided network

Front Oncol. 2022 Sep 8:12:981769. doi: 10.3389/fonc.2022.981769. eCollection 2022.

Authors

Kaili Chen¹, Jiashi Cao^{2

3}, Xin Zhang⁴, Xiang Wang⁵, Xiangyu Zhao⁴, Qingchu Li⁵, Song Chen⁵, Peng Wang⁵, Tielong Liu³, Juan Du¹, Shiyuan Liu⁵, Lichi Zhang⁴

Affiliations

¹ Department of Hematology, Myeloma & Lymphoma Center, Shanghai Changzheng Hospital, Changzheng Hospital of the Naval Medical University, Huangpu, China.
² Department of Orthopedics, No. 455 Hospital of Chinese People's Liberation Army, Shanghai 455 Hospital, Navy Medical University, Shanghai, China.
³ Department of Orthopaedic Oncology, Spine Tumor Center, Shanghai Changzheng Hospital, Changzheng Hospital of the Navy Medical University, Huangpu, China.
⁴ Institute for Medical Image Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
⁵ Department of Radiology, Changzheng Hospital, Shanghai Changzheng Hospital, Navy Medical University, Huangpu, China.

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 multi-view 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.

Keywords: attention guidance strategy; deep learning; lung cancer; multiple myeloma (MM); radiomics; spinal metastases.