Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI

Lv, Ning; Zhang, Yang; Zhang, Enlong; Zhang, Jiahui; Chow, Daniel; Chang, Peter D.; Yu, Hua; Yuan, Huishu; Su, Min‐Ying

doi:10.1016/j.mri.2019.02.013

Cited by 71 publications

(70 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, we trained four clinical-RMs and one clinical-DNN model based on a relatively large sample of data and found that clinical-LR performed best in the validation set. Similarly, Lang et al (16) found that the accuracy of radiomics analysis and convolutional neural network (CNN) was similar in the identification of spinal metastases originated from the lung and other tumors. LR is one of the most commonly used algorithms in radiomics analysis and has been proved to be effective (27)(28)(29)(30).…”

Section: Discussionmentioning

confidence: 92%

“…Previous studies have compared the performance of deep learning and radiomics in differentiating benign and malignant breast lesions (13,15), predicting lymph node metastases of breast cancer (14), identifying of spinal metastases originated from the lung and other cancers (16), predicting of survival of patients with high-grade gliomas (17), and predicting the (24) found that their DNN model was 80% accurate in predicting complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer, which was better than LR and SVM models. Due to the rarity of primary sacral tumors, only a few previous studies have identified sacral tumor types using machine learning methods (1,5,10).…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Deep neural network (DNN), as a deep architecture, has shown excellent performance in classification tasks and is increasingly being used in various areas of cancer research (11,12). Early studies on the application of deep learning to the detection or classification of lesions have shown that it performs better than traditional techniques and even better than radiologists on some tasks (13)(14)(15)(16)(17)(18). Ren et al (19) proposed a novel manifold regularized classification DNN to enhance CT image-based lung nodule classification.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors

et al. 2020

View full text Add to dashboard Cite

Purpose: To assess the performance of deep neural network (DNN) and machine learning based radiomics on 3D computed tomography (CT) and clinical characteristics to predict benign or malignant sacral tumors. Materials and methods: This single-center retrospective analysis included 459 patients with pathologically proven sacral tumors. After semi-automatic segmentation, 1,316 hand-crafted radiomics features of each patient were extracted. All models were built on training set (321 patients) and tested on validation set (138 patients). A DNN model and four machine learning classifiers (logistic regression [LR], random forest [RF], support vector machine [SVM] and k-nearest neighbor [KNN]) based on CT features and clinical characteristics were built, respectively. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models. Results: In total, 459 patients (255 males, 204 females; mean age of 42.1 ± 17.8 years, range 4-82 years) were enrolled in this study, including 206 cases of benign tumor and 253 cases of malignant tumor. The sex, age and tumor size had significant differences between the benign tumors and malignant tumors (c 2 sex = 10.854, Z age = −6.616, Z size = 2.843, P < 0.05). The radscore, sex, and age were important indicators for differentiating benign and malignant sacral tumors (odds ratio [OR]1 = 2.492, OR2 = 2.236, OR3 = 1.037, P < 0.01). Among the four clinical-radiomics models (RMs), clinical-LR had the best performance in the validation set (AUC = 0.84, ACC = 0.81). The clinical-DNN model also achieved a high performance (an AUC of 0.83 and an ACC of 0.76 in the validation set) in identifying benign and malignant sacral tumors. Conclusions: Both the clinical-LR and clinical-DNN models would have a high impact on assisting radiologists in their clinical diagnosis of sacral tumors.

show abstract

Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The area under the curve (AUC) started to show a substantial decrease after removing five features; therefore, the final model was built with 15 features. The detailed radiomics analysis and model‐building procedures were described in a recent publication . The analysis was done using programs written in MatLab 2013b (MathWorks, Natick, MA).…”

Section: Methodsmentioning

confidence: 99%

Diagnosis of Benign and Malignant Breast Lesions on DCE‐MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue

Zhou

Zhang

Chang

et al. 2019

Magnetic Resonance Imaging

Self Cite

138

123

View full text Add to dashboard Cite

Background: Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fullyautomatic diagnosis using deep learning is rarely reported. Purpose: To evaluate the diagnostic accuracy of mass lesions using region of interest (ROI)-based, radiomics and deeplearning methods, by taking peritumor tissues into consideration. Study Type: Retrospective. Population: In all, 133 patients with histologically confirmed 91 malignant and 62 benign mass lesions for training (74 patients with 48 malignant and 26 benign lesions for testing). Field Strength/Sequence: 3T, using the volume imaging for breast assessment (VIBRANT) dynamic contrast-enhanced (DCE) sequence. Assessment: 3D tumor segmentation was done automatically by using fuzzy-C-means algorithm with connectedcomponent labeling. A total of 99 texture and histogram parameters were calculated for each case, and 15 were selected using random forest to build a radiomics model. Deep learning was implemented using ResNet50, evaluated with 10-fold crossvalidation. The tumor alone, smallest bounding box, and 1.2, 1.5, 2.0 times enlarged boxes were used as inputs. Statistical Tests: The malignancy probability was calculated using each model, and the threshold of 0.5 was used to make a diagnosis. Results: In the training dataset, the diagnostic accuracy was 76% using three ROI-based parameters, 84% using the radiomics model, and 86% using ROI + radiomics model. In deep learning using the per-slice basis, the area under the receiver operating characteristic (ROC) was comparable for tumor alone, smallest and 1.2 times box (AUC = 0.97-0.99), which were significantly higher than 1.5 and 2.0 times box (AUC = 0.86 and 0.71, respectively). For per-lesion diagnosis, the highest accuracy of 91% was achieved when using the smallest bounding box, and that decreased to 84% for tumor alone and 1.2 times box, and further to 73% for 1.5 times box and 69% for 2.0 times box. In the independent testing dataset, the perlesion diagnostic accuracy was also the highest when using the smallest bounding box, 89%. Data Conclusion: Deep learning using ResNet50 achieved a high diagnostic accuracy. Using the smallest bounding box containing proximal peritumor tissue as input had higher accuracy compared to using tumor alone or larger boxes. Level of Evidence: 3 Technical Efficacy: Stage 2

show abstract

Use of Artificial Intelligence in Imaging for Bone Cancer

2024

Artificial Intelligence for Bone Disorder

View full text Add to dashboard Cite

Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI

Cited by 71 publications

References 42 publications

Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors

Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors

Diagnosis of Benign and Malignant Breast Lesions on DCE‐MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue

Use of Artificial Intelligence in Imaging for Bone Cancer

Contact Info

Product

Resources

About