Diagnosis of spinal lesions using perfusion parameters measured by DCE-MRI and metabolism parameters measured by PET/CT

Zhang, Jiahui; Chen, Yongye; Zhang, Yanyan; Zhang, Enlong; Yu, Hua; Yuan, Huishu; Zhang, Yang; Su, Min‐Ying; Lv, Ning

doi:10.1007/s00586-019-06213-9

Cited by 3 publications

(2 citation statements)

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“…Although 18F-FDG PET/CT is recommended as the optimal imaging modality in detecting MM, Zhang Jiahui et al. pointed out that vascular parameters measured by DCE-MRI and glucose metabolism measured by 18F-FDG PET/CT from the most aggressive tumor area did not show a significant correlation ( 1 , 25 ). In addition, DCE MRI is much cheaper and easily accessible with a much lower radiation exposure.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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PurposeMultiple 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.MethodsWe 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).ResultsAblation 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.ConclusionsThe 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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…Currently, vascular hemodynamics and metabolism are usually measured in two separate imaging scans, namely dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for evaluating tumor vascular hemodynamics [9] , [10] , [11] and [ 18 F]FDG-PET for metabolism measurement. In DCE-MRI, pharmacokinetic modeling of the microcirculation of contrast agents, usually gadolinium-based agents, shed light on underlying tumor vasculature properties, such as perfusion, permeability, and microvascular density properties of the tumor [12] .…”

Section: Introductionmentioning

confidence: 99%