scholarly journals A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis

Cancers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 5793
Author(s):  
Jialiang Wu ◽  
Fangrong Liang ◽  
Ruili Wei ◽  
Shengsheng Lai ◽  
Xiaofei Lv ◽  
...  
Keyword(s):  
Image Features ◽  
Multiparametric Mri ◽  
Peritumoral Edema ◽  
Mr Images ◽  
Discrimination Accuracy ◽  
Solitary Brain Metastasis ◽  
High Performing ◽  
Diagnostic Potential ◽  
Multi Disciplinary Team ◽  
Mr Sequences

This study aimed to evaluate the diagnostic potential of a novel RFO model in differentiating GBM and SBM with multiparametric MR sequences collected from 244 (131 GBM and 113 SBM) patients. Three basic volume of interests (VOIs) were delineated on the conventional axial MR images (T1WI, T2WI, T2_FLAIR, and CE_T1WI), including volumetric non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE). Using the RFO model, radiomics features extracted from different multiparametric MRI sequence(s) and VOI(s) were fused and the best sequence and VOI, or possible combinations, were determined. A multi-disciplinary team (MDT)-like fusion was performed to integrate predictions from the high-performing models for the final discrimination of GBM vs. SBM. Image features extracted from the volumetric ET (VOIET) had dominant predictive performances over features from other VOI combinations. Fusion of VOIET features from the T1WI and T2_FLAIR sequences via the RFO model achieved a discrimination accuracy of AUC = 0.925, accuracy = 0.855, sensitivity = 0.856, and specificity = 0.853, on the independent testing cohort 1, and AUC = 0.859, accuracy = 0.836, sensitivity = 0.708, and specificity = 0.919 on the independent testing cohort 2, which significantly outperformed three experienced radiologists (p = 0.03, 0.01, 0.02, and 0.01, and p = 0.02, 0.01, 0.45, and 0.02, respectively) and the MDT-decision result of three experienced experts (p = 0.03, 0.02, 0.03, and 0.02, and p = 0.03, 0.02, 0.44, and 0.03, respectively).

scholarly journals Noncontrast vestibular schwannoma surveillance imaging including an MR cisternographic sequence: is there a need for postcontrast imaging?

2019 ◽  
Vol 131 (2) ◽  
pp. 549-554 ◽  
Author(s):  
Karen Buch ◽  
Amy Juliano ◽  
Konstantina M. Stankovic ◽  
Hugh D. Curtin ◽  
Mary Beth Cunnane
Keyword(s):  
Vestibular Schwannoma ◽  
Relevant Information ◽  
Vestibular Schwannomas ◽  
Mr Images ◽  
Gadolinium Contrast ◽  
Additional Information ◽  
Significant Difference ◽  
T1 Hyperintensity ◽  
Temporal Bones ◽  
Mr Sequences

OBJECTIVEThe purpose of this study was to evaluate the use of a noncontrast MRI protocol that includes a cisternographic sequence (CISS/FIESTA/3D DRIVE) compared to a protocol that includes a gadolinium-enhanced sequence in order to determine whether a noncontrast approach could be utilized to follow vestibular schwannomas.METHODSA total of 251 patients with vestibular schwannomas who underwent MRI of the temporal bones that included both cisternographic sequence and postcontrast T1 imaging between January 2000 and January 2016 for surveillance were included in this retrospective study. The size of the vestibular schwannomas was independently assessed on a noncontrast MR cisternographic sequence and compared to size measurements on a postcontrast sequence. The evaluation of intralesional cystic components (identified as T2 signal hyperintensity) and hemorrhagic components (identified with intrinsic T1 hyperintensity) on noncontrast MR sequences was compared to evaluation on postcontrast MR sequences to determine whether additional information could be derived from the postcontrast sequences. Additionally, any potentially clinically significant, incidentally detected findings on the postcontrast T1 sequences were documented and compared with the detection of these findings on the precontrast images.RESULTSNo significant difference in vestibular schwannoma size was found when comparing measurements made on the images obtained with the MR cisternographic sequence and those made on images obtained with the postcontrast sequence (p = 0.99). Noncontrast MR images were better (detection rate of 87%) than postcontrast images for detection of cystic components. Noncontrast MR images were also better for identifying hemorrhagic components. No additional clinically relevant information regarding the tumors was identified on the postcontrast sequences.CONCLUSIONSBased on the results of this study, a noncontrast MR protocol that includes a cisternographic sequence would be sufficient for the accurate characterization of size and signal characteristics of vestibular schwannomas, obviating the need for gadolinium contrast administration for the routine surveillance of these lesions.

scholarly journals Investigation of Significant Features Based on Image Texture Analysis for Automated Denoising in MR Images

2015 ◽  
pp. 001-005
Author(s):  
Lin Yu-Ju ◽  
Chang Herng-Hua
Keyword(s):  
Texture Features ◽  
Noise Removal ◽  
Optimal Number ◽  
Image Features ◽  
T Test ◽  
Image Texture ◽  
Discrete Wavelet ◽  
Mr Images ◽  
Noise Levels ◽  
Paired Samples

Introduction: In magnetic resonance (MR) image analysis, noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing such as tissue classification, segmentation and registration. Consequently, noise removal in MR images is important and essential for a wide variety of subsequent processing applications. In the literature, abundant denoising algorithms have been proposed, most of which require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. However, this will induce another problem of seeking appropriate meaningful attributes among a huge number of image characteristics for the automation process. This paper is in an attempt to systematically investigate significant attributes from image texture features to facilitate subsequent automation processes. Methods: In our approach, a total number of 60 image texture attributes are considered that are based on three categories: 1) Image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) 2-D discrete wavelet transform (DWT). To obtain the most significant attributes, a paired-samples t-test is applied to each individual image features computed in every image. The evaluation is based on the distinguishing ability between noise levels, intensity distributions, and anatomical geometries. Results: A wide variety of images were adopted including the BrainWeb image data with various levels of noise and intensity non-uniformity to evaluate the proposed methods. Experimental results indicated that an optimal number of seven image features performed best in distinguishing MR images with various combinations of noise levels and slice positions. They were the contrast and dissimilarity features from the GLCM category and five norm energy and standard deviation features from the 2-D DWT category. Conclusions: We have introduced a new framework to systematically investigate significant attributes from various image features and textures for the automation process in denoising MR images. Sixty image texture features were computed in every image followed by a paired-samples t-test for the discrimination evaluation. Seven texture features with two from the GLCM category and five from the 2-D DWT category performed best, which can be incorporated into denoising procedures for the automation purpose in the future.

Differentiation of paediatric posterior fossa tumours by the multiregional and multiparametric MRI radiomics approach: A study on the selection of optimal multiple sequences and multiregions

2021 ◽  
Author(s):  
Jie Dong ◽  
Suxiao Li ◽  
Lei Li ◽  
Shengxiang Liang ◽  
Bin Zhang ◽  
...  
Keyword(s):  
Posterior Fossa ◽  
Area Under The Curve ◽  
Tumour Volume ◽  
Multiparametric Mri ◽  
Added Value ◽  
Support Vector ◽  
Diffusion Weighted Images ◽  
Posterior Fossa Tumours ◽  
Mri Features ◽  
Mr Sequences

Objective: To evaluate the diagnostic performance of a radiomics model based on multiregional and multiparametric magnetic resonance imaging (MRI) to classify paediatric posterior fossa tumours (PPFTs), explore the contribution of different MR sequences and tumour subregions in tumour classification, and examine whether contrast-enhanced T1-weighted (T1C) images have irreplaceable added value. Methods: This retrospective study of 136 PPFTs extracted 11,958 multiregional (enhanced, non-enhanced, and total tumour) features from multiparametric MRI (T1- and T2-weighted, T1C, fluid-attenuated inversion recovery, and diffusion-weighted images). These features were subjected to fast correlation-based feature selection and classified by a support vector machine based on different tasks. Diagnostic performances of multiregional and multiparametric MRI features, different sequences, and different tumoral regions were evaluated using multiclass and one-versus-rest strategies. Results: The established model achieved an overall area under the curve (AUC) of 0.977 in the validation cohort. The performance of PPFTs significantly improved after replacing T1C with apparent diffusion coefficient maps added into the plain scan sequences (AUC from 0.812 to 0.917). When oedema features were added to contrast-enhancing tumour volume, the performance did not significantly improve. Conclusion: The radiomics model built by multiregional and multiparametric MRI features allows for the excellent distinction of different PPFTs and provides valuable references for the rational adoption of MR sequences. Advances in knowledge: This study emphasized that T1C has limited added value in predicting PPFTs and should be cautiously adopted. Selecting optimal MR sequences may help guide clinicians to better allocate acquisition sequences and reduce medical costs.

scholarly journals NI-19 USEFULNESS OF AMIDE PROTON TRANSFER IMAGE IN IMAGING DIAGNOSIS OF GLIOMA

2019 ◽  
Vol 1 (Supplement_2) ◽  
pp. ii29-ii29
Author(s):  
Masami Shirota ◽  
Masayuki Nitta ◽  
Takashi Maruyama ◽  
Taiichi Saitou ◽  
Syunsuke Tsuduki ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Amide Group ◽  
Amide Proton ◽  
Imaging Diagnosis ◽  
Discrimination Accuracy ◽  
Accumulation Ratio ◽  
Non Invasive ◽  
Molecular Image ◽  
Diagnostic Potential ◽  
Met Pet

Abstract INTRODUCTION APT image is one of the imaging methods in MRI, and it is a molecular image that images the concentration of an amide group having an amino acid increasing in a tumor, and is expected to be clinically applied in the imaging diagnosis of glioma. on the other hand, MET-PET is useful for diagnosis of glioma because it is well accumulated in tumor cells. Based on the results of pathological diagnosis, we compared the two and verified that APT image is useful. METHOD The study included 36 patients who underwent APT image and MET-PET. (Glioma WHO2016 Grade:GII/III/IV,and Pseudoprogression). MET-PET was administered 370MBq/kg, and the accumulation ratio (TNR) of the tumor part to the normal part was measured. APT image measured APT signal with the region of interest at the tumor site. RESULTS APT signal in all 36 cases was correlated with 2.19±0.94 and TNR with 2.61±1.55 (r=0.67,p<0.001).The discrimination accuracy between GII/III/IV and Pseudoprogression by APT signal was 84% sensitivity and 100% specificity at threshold 2.0.GII APT signal 2.30±0.43, TNR 4.02±2.12, GIII APT signal 2.67±0.69, TNR 2.81±0.72, GIV APT signal 2.78±0.61, TNR 3.37±1.28 in grade diagnosis At high grade, APT signal and TNR were high. The APT signal of the oligodendroglioma line (GII/III) was 2.44±0.7, the TNR was 3.78±1.51, the APT signal of the astrocytoma line (GII/III) was 2.69±0.51, and the TNR was 2.43±0.98.The oligodendroglioma lineage was lower in APT signal than the astrocytoma lineage, and the TNR was higher. DISCUSSION APT images are non-invasive, can easily provide important information, and have the same diagnostic potential as MET-PET. Although TNR of oligodendroglioma (GII/III) tends to be high, the APT signal which is not affected by the blood-brain barrier is consistent in measurement value and is useful for diagnostic imaging of glioma.

scholarly journals Intraoperative fiducial-less patient registration using volumetric 3D ultrasound: a prospective series of 32 neurosurgical cases

2015 ◽  
Vol 123 (3) ◽  
pp. 721-731 ◽  
Author(s):  
Xiaoyao Fan ◽  
David W. Roberts ◽  
Songbai Ji ◽  
Alex Hartov ◽  
Keith D. Paulsen
Keyword(s):  
Operating Room ◽  
Anterior Commissure ◽  
Computational Cost ◽  
3D Ultrasound ◽  
Image Features ◽  
Intensity Difference ◽  
Image Transformation ◽  
Mr Images ◽  
Registration Error ◽  
Patient Registration

OBJECT Fiducial-based registration (FBR) is used widely for patient registration in image-guided neurosurgery. The authors of this study have developed an automatic fiducial-less registration (FLR) technique to find the patient-to-image transformation by directly registering 3D ultrasound (3DUS) with MR images without incorporating prior information. The purpose of the study was to evaluate the performance of the FLR technique when used prospectively in the operating room and to compare it with conventional FBR. METHODS In 32 surgical patients who underwent conventional FBR, preoperative T1-weighted MR images (pMR) with attached fiducial markers were acquired prior to surgery. After craniotomy but before dural opening, a set of 3DUS images of the brain volume was acquired. A 2-step registration process was executed immediately after image acquisition: 1) the cortical surfaces from pMR and 3DUS were segmented, and a multistart sum-of-squared-intensity-difference registration was executed to find an initial alignment between down-sampled binary pMR and 3DUS volumes; and 2) the alignment was further refined by a mutual information-based registration between full-resolution grayscale pMR and 3DUS images, and a patient-to-image transformation was subsequently extracted. RESULTS To assess the accuracy of the FLR technique, the following were quantified: 1) the fiducial distance error (FDE); and 2) the target registration error (TRE) at anterior commissure and posterior commissure locations; these were compared with conventional FBR. The results showed that although the average FDE (6.42 ± 2.05 mm) was higher than the fiducial registration error (FRE) from FBR (3.42 ± 1.37 mm), the overall TRE of FLR (2.51 ± 0.93 mm) was lower than that of FBR (5.48 ± 1.81 mm). The results agreed with the intent of the 2 registration techniques: FBR is designed to minimize the FRE, whereas FLR is designed to optimize feature alignment and hence minimize TRE. The overall computational cost of FLR was approximately 4–5 minutes and minimal user interaction was required. CONCLUSIONS Because the FLR method directly registers 3DUS with MR by matching internal image features, it proved to be more accurate than FBR in terms of TRE in the 32 patients evaluated in this study. The overall efficiency of FLR in terms of the time and personnel involved is also improved relative to FBR in the operating room, and the method does not require additional image scans immediately prior to surgery. The performance of FLR and these results suggest potential for broad clinical application.

Multiparametric MRI-based radiomics analysis: differentiation of subtypes of cervical cancer in the early stage

2021 ◽  
pp. 028418512110141
Author(s):  
Wei Wang ◽  
YiNing Jiao ◽  
LiChi Zhang ◽  
Caixia Fu ◽  
XiaoLi Zhu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Early Stage ◽  
Area Under The Curve ◽  
Multiparametric Mri ◽  
Pelvic Mri ◽  
Non Invasive ◽  
Contrast Enhanced ◽  
Apparent Diffusion ◽  
Magnetic Resonance Imaging Mri ◽  
Mr Sequences

Background There are significant differences in outcomes for different histological subtypes of cervical cancer (CC). Yet, it is difficult to distinguish CC subtypes using non-invasive methods. Purpose To investigate whether multiparametric magnetic resonance imaging (MRI)-based radiomics analysis can differentiate CC subtypes and explore tumor heterogeneity. Material and Methods This study retrospectively analyzed 96 patients with CC (squamous cell carcinoma [SCC] = 50, adenocarcinoma [AC] = 46) who underwent pelvic MRI before surgery. Radiomics features were extracted from the tumor volumes on five sequences (sagittal T2-weighted imaging [T2SAG], transverse T2-weighted imaging [T2TRA], sagittal contrast-enhanced T1-weighted imaging [CESAG], transverse contrast-enhanced T1-weighted imaging [CETRA], and apparent diffusion coefficient [ADC]). Clustering and logistic regression were used to examine the distinguishing capabilities of radiomics features extracted from five different MR sequences. Results Among the 105 extracted radiomics features, there were 51, 38, 37, and 2 features that showed intergroup differences for T2SAG, T2TRA, ADC, and CESAG, respectively (all P < 0.05). AC had greater textural heterogeneity than SCC ( P < 0.05). Upon unsupervised clustering of significantly different features, T2SAG achieved the highest accuracy (0.844; sensitivity = 0.920; specificity = 0.761). The largest area under the curve (AUC) for classification ability was 0.86 for T2SAG. Hence, the radiomics model from five combined MR sequences (AUC = 0.89; accuracy = 0.81; sensitivity = 0.67; specificity = 0.94) exhibited better differentiation ability than any MR sequence alone. Conclusion Multiparametric MRI-based radiomics models may be a promising method to differentiate AC and SCC. AC showed more heterogeneous features than SCC.

scholarly journals Computed Tomography and Magnetic Resonance Imaging Features of the Temporomandibular Joint in Two Normal Camels

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Alberto Arencibia ◽  
Diego Blanco ◽  
Nelson González ◽  
Miguel A. Rivero
Keyword(s):  
Computed Tomography ◽  
Magnetic Resonance ◽  
Temporomandibular Joint ◽  
Superconducting Magnet ◽  
Image Features ◽  
Imaging Features ◽  
Mr Images ◽  
Dromedary Camels ◽  
Mr Image ◽  
Imaging Processing

Computed tomography (CT) and magnetic resonance (MR) image features of the temporomandibular joint (TMJ) and associated structures in two mature dromedary camels were obtained with a third-generation equipment CT and a superconducting magnet RM at 1.5 Tesla. Images were acquired in sagittal and transverse planes. Medical imaging processing with imaging software was applied to obtain postprocessing CT and MR images. Relevant anatomic structures were identified and labelled. The resulting images provided excellent anatomic detail of the TMJ and associated structures. Annotated CT and MR images from this study are intended as an anatomical reference useful in the interpretation for clinical CT and MR imaging studies of the TMJ of the dromedary camels.

Discriminating Multiple Sclerosis from other Diseases of Similar Presentation

1994 ◽  
Vol 7 (1) ◽  
pp. 37-45 ◽  
Author(s):  
G.H. Du Boulay ◽  
B.A. Teather ◽  
D. Teather ◽  
N.P. Jeffery ◽  
M.A. Higgott ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Cerebrovascular Disease ◽  
Clinical Presentation ◽  
Image Features ◽  
Mr Images ◽  
Mr Image ◽  
Diagnostic Certainty ◽  
Computer Based ◽  
Preliminary Study ◽  
Image Descriptions

All patients presenting to an MR Imaging Centre during the periods of study, either in 1988 or 1991, have had their records of signs, symptoms and history prior to scanning reviewed, all abnormal MR images archived and an attempt has been made to follow their subsequent course up to the allocation of a confirmed or working diagnosis by their physicians and surgeons. Using a detailed, menu-driven, computer-based dialogue, abnormal images have been described, blind to all other data, in order to identify image features that may be significant in discriminating between diagnoses. This preliminary study addresses the differential diagnosis of multiple sclerosis from cerebrovascular disease, a problem often confounded by the similar multi-centric, multi-episode clinical presentation. Although the numbers starting with similar clinical presentation, with confirmed or working diagnosis at an acceptable level of certainty, and with completed image descriptions are small (45 Multiple Sclerosis, 6 Cerebrovascular disease), there are strong indications that certain MR image features are more helpful than is generally realised. The association of these features during statistical analysis may improve differential diagnostic certainty. Attention is drawn to the detailed appearances of individual lesions, the prevalence of one or more lesion types in individual patients, the sizes of lesions and the association of lesions affecting arcuate fibres with those affecting grey matter.

scholarly journals Prediction of Microvascular Invasion in Hepatocellular Carcinoma With a Multi-Disciplinary Team-Like Radiomics Fusion Model on Dynamic Contrast-Enhanced Computed Tomography

2021 ◽  
Vol 11 ◽  
Author(s):  
Wanli Zhang ◽  
Ruimeng Yang ◽  
Fangrong Liang ◽  
Guoshun Liu ◽  
Amei Chen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Hepatocellular Carcinoma ◽  
Image Features ◽  
Microvascular Invasion ◽  
Fusion Model ◽  
Model Fusion ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Multi Disciplinary Team ◽  
Testing Set

ObjectiveTo investigate microvascular invasion (MVI) of HCC through a noninvasive multi-disciplinary team (MDT)-like radiomics fusion model on dynamic contrast enhanced (DCE) computed tomography (CT).MethodsThis retrospective study included 111 patients with pathologically proven hepatocellular carcinoma, which comprised 57 MVI-positive and 54 MVI-negative patients. Target volume of interest (VOI) was delineated on four DCE CT phases. The volume of tumor core (Vtc) and seven peripheral tumor regions (Vpt, with varying distances of 2, 4, 6, 8, 10, 12, and 14 mm to tumor margin) were obtained. Radiomics features extracted from different combinations of phase(s) and VOI(s) were cross-validated by 150 classification models. The best phase and VOI (or combinations) were determined. The top predictive models were ranked and screened by cross-validation on the training/validation set. The model fusion, a procedure analogous to multidisciplinary consultation, was performed on the top-3 models to generate a final model, which was validated on an independent testing set.ResultsImage features extracted from Vtc+Vpt(12mm) in the portal venous phase (PVP) showed dominant predictive performances. The top ranked features from Vtc+Vpt(12mm) in PVP included one gray level size zone matrix (GLSZM)-based feature and four first-order based features. Model fusion outperformed a single model in MVI prediction. The weighted fusion method achieved the best predictive performance with an AUC of 0.81, accuracy of 78.3%, sensitivity of 81.8%, and specificity of 75% on the independent testing set.ConclusionImage features extracted from the PVP with Vtc+Vpt(12mm) are the most reliable features indicative of MVI. The MDT-like radiomics fusion model is a promising tool to generate accurate and reproducible results in MVI status prediction in HCC.

Sign in / Sign up

Export Citation Format

Share Document