عنوان انگلیسی مقاله:
Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI
ترجمه فارسی عنوان مقاله:
تمایز متاستازهای ستون فقرات ناشی از سرطانهای ریه و سایر سرطانها با استفاده از رادیولوژی و یادگیری عمیق بر اساس DCE-MRI
Sciencedirect - Elsevier - Magnetic Resonance Imaging,Corrected proof,doi:10:1016/j:mri:2019:02:013
Ning Langa, Yang Zhangb, Enlong Zhanga, Jiahui Zhanga, Daniel Chowb, Peter Changb, Hon J. Yub, Huishu Yuana,⁎, Min-Ying Sub,⁎⁎
Purpose: To differentiate metastatic lesions in the spine originated from primary lung cancer and other cancers
using radiomics and deep learning, compared to traditional hot-spot ROI analysis.
Methods: In a retrospective review of clinical spinal MRI database with a dynamic contrast enhanced (DCE)
sequence, a total of 61 patients without prior cancer diagnosis and later confirmed to have metastases (30 lung;
31 non-lung cancers) were identified. For hot-spot analysis, a manual ROI was placed to calculate three heuristic
parameters from the wash-in, maximum, and wash-out phases in the DCE kinetics. For each case, the 3D tumor
mask was generated by using the normalized-cut algorithm. Radiomics analysis was performed to extract histogram
and texture features from three DCE parametric maps. Deep learning was performed using these maps as
inputs into a conventional convolutional neural network (CNN), as well as using all 12 sets of DCE images into a
convolutional long short term memory (CLSTM) network.
Results: For hot-spot ROI analysis, mean wash-out slope was 0.25 ± 10% for lung metastases and
−9.8 ± 12.9% for other tumors. CHAID classification using a wash-out slope of −6.6% followed by wash-in
enhancement ratio of 98% achieved a diagnostic accuracy of 0.79. Radiomics analysis using features representing
tumor heterogeneity only reached the highest accuracy of 0.71. Classification using CNN achieved a
mean accuracy of 0.71 ± 0.043, whereas a CLSTM improved accuracy to 0.81 ± 0.034.
Conclusions: DCE-MRI machine-learning analysis methods have potential to predict lung cancer metastases in
the spine, which may be used to guide subsequent workup for confirmed diagnosis.
Keywords: DCE-MRI | Radiomics | Deep learning | Spinal metastases