scholarly journals Multimodality MRI-Based Radiomics For Aggressiveness Prediction In Papillary Thyroid Cancer

2021 ◽  
Author(s):  
Zedong Dai ◽  
Ran Wei ◽  
Hao Wang ◽  
Wenjuan Hu ◽  
Xilin Sun ◽  
...  
Keyword(s):  
Classification Model ◽  
Study Cohort ◽  
Papillary Thyroid ◽  
Mr Images ◽  
Machine Learning Model ◽  
Contrast Enhanced ◽  
Independent Test ◽  
Representation Method ◽  
And Diffusion ◽  
Pathology Training

Abstract Objective: To investigate the ability of a multimodality MRI-based radiomics model in predicting the aggressiveness of papillary thyroid carcinoma (PTC).Methods: This study included consecutive patients who underwent neck magnetic resonance (MR) scans and subsequent thyroidectomy during the study period. The pathological diagnosis of thyroidectomy specimens was the gold standard to determine the aggressiveness. Thyroid nodules were manually segmented on three modal MR images, and then radiomics features were extracted. A machine learning model was established to evaluate the prediction of PTC aggressiveness.Results: The study cohort included 107 patients with PTC confirmed by pathology (training cohort: n = 71; test cohort: n = 36). A total of 1584 features were extracted from contrast-enhanced T1-weighted (CE-T1 WI), T2-weighted (T2 WI) and diffusion weighted (DWI) images of each patient. Sparse representation method is used for radiation feature selection and classification model establishment. The accuracy of the independent test set that using only one mode, like CE-T1WI, T2WI or DWI was not particularly satisfactory. In contrast, the result of these three modes combined achieved 0.917.Conclusion: Our study shows that multimodality MR image based on radiomics model can accurately distinguish aggressiveness in PTC from non-aggressiveness PTC before operation. This method may be helpful to inform the treatment strategy and prognosis of patients with aggressiveness PTC.

Prediction of methylguanine methyltransferase promoter methylation in glioblastoma using dynamic contrast-enhanced magnetic resonance and diffusion tensor imaging

2014 ◽  
Vol 121 (2) ◽  
pp. 367-373 ◽  
Author(s):  
Sung Soo Ahn ◽  
Na-Young Shin ◽  
Jong Hee Chang ◽  
Se Hoon Kim ◽  
Eui Hyun Kim ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
Methylation Status ◽  
Imaging Features ◽  
Mgmt Methylation ◽  
Mr Images ◽  
Optimal Cutoff ◽  
Dce Mri ◽  
Contrast Enhanced ◽  
Methylguanine Methyltransferase ◽  
And Diffusion

Object The methylation status of the methylguanine methyltransferase (MGMT) promoter has been associated with treatment response in glioblastoma. The authors aimed to assess whether MGMT methylation status can be predicted by dynamic contrast-enhanced (DCE) MRI and diffusion tensor imaging (DTI). Methods This retrospective study included 43 patients with pathologically diagnosed glioblastoma who had undergone preoperative DCE-MRI and DTI and whose MGMT methylation status was available. The imaging features were qualitatively assessed using conventional MR images. Regions of interest analyses for DCE-MRI permeability parameters (transfer constant [Ktrans], rate transfer coefficient [Kep], and volume fraction of extravascular extracellular space [Ve]) and DTI parameters (apparent diffusion coefficient [ADC] and fractional anisotropy [FA]) were performed on the enhancing solid portion of the glioblastoma. Chi-square or Mann-Whitney tests were used to evaluate relationships between MGMT methylation and imaging parameters. The authors performed receiver operating characteristic curve analysis to find the optimal cutoff value for the presence of MGMT methylation. Results MGMT methylation was not significantly associated with any imaging features on conventional MR images. Ktrans values were significantly higher in the MGMT methylated group (median 0.091 vs 0.053 min−1, p = 0.018). However, Kep, Ve, ADC, and FA were not significantly different between the 2 groups. The optimal cutoff value for the presence of MGMT methylation was Ktrans > 0.086 min−1 with an area under the curve of 0.756, a sensitivity of 56.3%, and a specificity of 85.2%. Conclusions Ktrans may serve as a potential imaging biomarker to predict MGMT methylation status preoperatively in glioblastoma; however, further investigation with a larger cohort is necessary.

The Added Value of Inflow-Based Vascular-Space-Occupancy and Diffusion-Weighted Imaging in Preoperative Grading of Gliomas

2021 ◽  
pp. 1-8
Author(s):  
Haimei Cao ◽  
Xiang Xiao ◽  
Jun Hua ◽  
Guanglong Huang ◽  
Wenle He ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Roc Curve ◽  
Diffusion Weighted Imaging ◽  
Low Grade ◽  
High Grade ◽  
Mr Images ◽  
Vascular Space ◽  
Diffusion Weighted ◽  
High Grade Gliomas ◽  
And Diffusion

Objectives: The present study aimed to study whether combined inflow-based vascular-space-occupancy (iVASO) MR imaging (MRI) and diffusion-weighted imaging (DWI) improve the diagnostic accuracy in the preoperative grading of gliomas. Methods: Fifty-one patients with histopathologically confirmed diffuse gliomas underwent preoperative structural MRI, iVASO, and DWI. We performed 2 qualitative consensus reviews: (1) structural MR images alone and (2) structural MR images with iVASO and DWI. Relative arteriolar cerebral blood volume (rCBVa) and minimum apparent diffusion coefficient (mADC) were compared between low-grade and high-grade gliomas. Receiver operating characteristic (ROC) curve analysis was performed to compare the tumor grading efficiency of rCBVa, mADC, and the combination of the two parameters. Results: Two observers diagnosed accurate tumor grade in 40 of 51 (78.4%) patients in the first review and in 46 of 51 (90.2%) in the second review. Both rCBVa and mADC showed significant differences between low-grade and high-grade gliomas. ROC analysis gave a threshold value of 1.52 for rCBVa and 0.85 × 10−3 mm2/s for mADC to provide a sensitivity and specificity of 88.0 and 81.2% and 100.0 and 68.7%, respectively. The area under the ROC curve (AUC) was 0.87 and 0.85 for rCBVa and mADC, respectively. The combination of rCBVa and mADC values increased the AUC to 0.92. Conclusion: The combined application of iVASO and DWI may improve the diagnostic accuracy of glioma grading.

Fully automated contrast and non-contrast cardiac view detection in echocardiography a multi-centre, multi-vendor study

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Gao ◽  
D Stojanovski ◽  
A Parker ◽  
P Marques ◽  
S Heitner ◽  
...  
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Classification Model ◽  
Validation Dataset ◽  
Funding Source ◽  
Private Company ◽  
Validation Data ◽  
Independent Test ◽  
Model Training ◽  
Confusion Matrices

Abstract Background Correctly identifying views acquired in a 2D echocardiographic examination is paramount to post-processing and quantification steps often performed as part of most clinical workflows. In many exams, particularly in stress echocardiography, microbubble contrast is used which greatly affects the appearance of the cardiac views. Here we present a bespoke, fully automated convolutional neural network (CNN) which identifies apical 2, 3, and 4 chamber, and short axis (SAX) views acquired with and without contrast. The CNN was tested in a completely independent, external dataset with the data acquired in a different country than that used to train the neural network. Methods Training data comprised of 2D echocardiograms was taken from 1014 subjects from a prospective multisite, multi-vendor, UK trial with the number of frames in each view greater than 17,500. Prior to view classification model training, images were processed using standard techniques to ensure homogenous and normalised image inputs to the training pipeline. A bespoke CNN was built using the minimum number of convolutional layers required with batch normalisation, and including dropout for reducing overfitting. Before processing, the data was split into 90% for model training (211,958 frames), and 10% used as a validation dataset (23,946 frames). Image frames from different subjects were separated out entirely amongst the training and validation datasets. Further, a separate trial dataset of 240 studies acquired in the USA was used as an independent test dataset (39,401 frames). Results Figure 1 shows the confusion matrices for both validation data (left) and independent test data (right), with an overall accuracy of 96% and 95% for the validation and test datasets respectively. The accuracy for the non-contrast cardiac views of >99% exceeds that seen in other works. The combined datasets included images acquired across ultrasound manufacturers and models from 12 clinical sites. Conclusion We have developed a CNN capable of automatically accurately identifying all relevant cardiac views used in “real world” echo exams, including views acquired with contrast. Use of the CNN in a routine clinical workflow could improve efficiency of quantification steps performed after image acquisition. This was tested on an independent dataset acquired in a different country to that used to train the model and was found to perform similarly thus indicating the generalisability of the model. Figure 1. Confusion matrices Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Ultromics Ltd.

scholarly journals Differentiating benign and malignant inflammatory breast lesions: Value of T2 weighted and diffusion weighted MR images

2018 ◽  
Vol 50 ◽  
pp. 38-44 ◽  
Author(s):  
Shotaro Kanao ◽  
Masako Kataoka ◽  
Mami Iima ◽  
Debra Masako Ikeda ◽  
Masakazu Toi ◽  
...  
Keyword(s):  
Breast Lesions ◽  
Mr Images ◽  
Diffusion Weighted ◽  
And Diffusion
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