scholarly journals CT in the Differentiation of Gliomas from Brain Metastases: The Radiomics Analysis of the Peritumoral Zone

2022 ◽  
Vol 12 (1) ◽  
pp. 109
Author(s):  
Lucian Mărginean ◽  
Paul Andrei Ștefan ◽  
Andrei Lebovici ◽  
Iulian Opincariu ◽  
Csaba Csutak ◽  
...  
Keyword(s):  
Brain Metastases ◽  
Vasogenic Edema ◽  
Neoplastic Cell ◽  
Imaging Features ◽  
High Grade ◽  
Operating Characteristics ◽  
Local Infiltration ◽  
Texture Parameters ◽  
Variable Sensitivity ◽  
Similar Imaging

Due to their similar imaging features, high-grade gliomas (HGGs) and solitary brain metastases (BMs) can be easily misclassified. The peritumoral zone (PZ) of HGGs develops neoplastic cell infiltration, while in BMs the PZ contains pure vasogenic edema. As the two PZs cannot be differentiated macroscopically, this study investigated whether computed tomography (CT)-based texture analysis (TA) of the PZ can reflect the histological difference between the two entities. Thirty-six patients with solitary brain tumors (HGGs, n = 17; BMs, n = 19) that underwent CT examinations were retrospectively included in this pilot study. TA of the PZ was analyzed using dedicated software (MaZda version 5). Univariate, multivariate, and receiver operating characteristics analyses were used to identify the best-suited parameters for distinguishing between the two groups. Seven texture parameters were able to differentiate between HGGs and BMs with variable sensitivity (56.67–96.67%) and specificity (69.23–100%) rates. Their combined ability successfully identified HGGs with 77.9–99.2% sensitivity and 75.3–100% specificity. In conclusion, the CT-based TA can be a useful tool for differentiating between primary and secondary malignancies. The TA features indicate a more heterogenous content of the HGGs’ PZ, possibly due to the local infiltration of neoplastic cells.

scholarly journals Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone

2020 ◽  
Vol 10 (9) ◽  
pp. 638
Author(s):  
Csaba Csutak ◽  
Paul-Andrei Ștefan ◽  
Lavinia Manuela Lenghel ◽  
Cezar Octavian Moroșanu ◽  
Roxana-Adelina Lupean ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Brain Tumors ◽  
Brain Metastases ◽  
Texture Analysis ◽  
Univariate Analysis ◽  
Classification Error ◽  
Adjacent Area ◽  
High Grade ◽  
Texture Parameters ◽  
High Grade Gliomas

High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16; BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75–87.5% sensitivity, 53.85–88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs.

Myxofibrosarcoma: clinical and prognostic value of MRI features

2020 ◽  
Vol 16 ◽  
Author(s):  
Paolo Spinnato ◽  
Andrea Sambri ◽  
Tomohiro Fujiwara ◽  
Luca Ceccarelli ◽  
Roberta Clinca ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Local Recurrence ◽  
Contrast Enhancement ◽  
Imaging Modality ◽  
Soft Tissue Sarcomas ◽  
The Elderly ◽  
High Rate ◽  
Imaging Features ◽  
High Grade ◽  
Mri Features

: Myxofibrosarcoma is one of the most common soft tissue sarcomas in the elderly. It is characterized by an extremely high rate of local recurrence, higher than other soft tissue tumors, and a relatively low risk of distant metastases.Magnetic resonance imaging (MRI) is the imaging modality of choice for the assessment of myxofibrosarcoma and plays a key role in the preoperative setting of these patients.MRI features associated with high risk of local recurrence are: high myxoid matrix content (water-like appearance of the lesions), high grade of contrast enhancement, presence of an infiltrative pattern (“tail sign”). On the other hand, MRI features associated with worse sarcoma specific survival are: large size of the lesion, deep location, high grade of contrast enhancement. Recognizing the above-mentioned imaging features of myxofibrosarcoma may be helpful to stratify the risk for local recurrence and disease-specific survival. Moreover, the surgical planning should be adjusted according to the MRI features

scholarly journals Not all that glitters is COVID! Differential diagnosis of FDG-avid interstitial lung disease in low-prevalence regions

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Annalisa Papa ◽  
Chiara Pozzessere ◽  
Francesco Cicone ◽  
Fabiola Rizzuto ◽  
Giuseppe Lucio Cascini
Keyword(s):  
Differential Diagnosis ◽  
Interstitial Lung Diseases ◽  
Routine Activity ◽  
Imaging Features ◽  
Pet Ct ◽  
Oncologic Patients ◽  
The Many ◽  
Fdg Pet Ct ◽  
Low Prevalence ◽  
Similar Imaging

Abstract Coronavirus disease-19 (COVID-19) is only one of the many possible infectious and non-infectious diseases that may occur with similar imaging features in patients undergoing [18F]-fluorodeoxyglucose (18FDG) monitoring, particularly in the most fragile oncologic patients. We briefly summarise some key radiological elements of differential diagnosis of interstitial lung diseases which, in our opinion, could be extremely useful for physicians reporting 18FDG PET/CT scans, not only during the COVID-19 pandemic, but also for their normal routine activity.

scholarly journals Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter study

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Endre Grøvik ◽  
Darvin Yi ◽  
Michael Iv ◽  
Elizabeth Tong ◽  
Line Brennhaug Nilsen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Metastases ◽  
Multicenter Study ◽  
Roc Curve ◽  
Inversion Recovery ◽  
Fast Spin Echo ◽  
Operating Characteristics ◽  
Test Set ◽  
Mri Sequences ◽  
Input Level

AbstractThe purpose of this study was to assess the clinical value of a deep learning (DL) model for automatic detection and segmentation of brain metastases, in which a neural network is trained on four distinct MRI sequences using an input-level dropout layer, thus simulating the scenario of missing MRI sequences by training on the full set and all possible subsets of the input data. This retrospective, multicenter study, evaluated 165 patients with brain metastases. The proposed input-level dropout (ILD) model was trained on multisequence MRI from 100 patients and validated/tested on 10/55 patients, in which the test set was missing one of the four MRI sequences used for training. The segmentation results were compared with the performance of a state-of-the-art DeepLab V3 model. The MR sequences in the training set included pre-gadolinium and post-gadolinium (Gd) T1-weighted 3D fast spin echo, post-Gd T1-weighted inversion recovery (IR) prepped fast spoiled gradient echo, and 3D fluid attenuated inversion recovery (FLAIR), whereas the test set did not include the IR prepped image-series. The ground truth segmentations were established by experienced neuroradiologists. The results were evaluated using precision, recall, Intersection over union (IoU)-score and Dice score, and receiver operating characteristics (ROC) curve statistics, while the Wilcoxon rank sum test was used to compare the performance of the two neural networks. The area under the ROC curve (AUC), averaged across all test cases, was 0.989 ± 0.029 for the ILD-model and 0.989 ± 0.023 for the DeepLab V3 model (p = 0.62). The ILD-model showed a significantly higher Dice score (0.795 ± 0.104 vs. 0.774 ± 0.104, p = 0.017), and IoU-score (0.561 ± 0.225 vs. 0.492 ± 0.186, p < 0.001) compared to the DeepLab V3 model, and a significantly lower average false positive rate of 3.6/patient vs. 7.0/patient (p < 0.001) using a 10 mm3 lesion-size limit. The ILD-model, trained on all possible combinations of four MRI sequences, may facilitate accurate detection and segmentation of brain metastases on a multicenter basis, even when the test cohort is missing input MRI sequences.

Abstract 18795: Resting Patient- and Vessel-Specific Myocardial Perfusion Attenuation Patterns Improves Diagnostic Performance of Coronary Computed Tomographic Angiography

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Guanglei Xiong ◽  
Iksung Cho ◽  
Heidi Gransar ◽  
Deeksha Kola ◽  
Kimberly Elmore ◽  
...  
Keyword(s):  
Myocardial Perfusion ◽  
Diagnostic Accuracy ◽  
Coronary Stenosis ◽  
Diagnostic Performance ◽  
Ct Perfusion ◽  
Quantitative Coronary Angiography ◽  
High Grade ◽  
Operating Characteristics ◽  
Luminal Diameter ◽  
Coronary Stenoses

Introduction: Coronary CT angiography (CCTA) demonstrates improved performance for diagnosis of high-grade coronary stenoses, but may be affected by artifacts and overestimation of stenosis severity. Whether the addition of resting myocardial perfusion attenuation patterns subtended by stenosis seen on CCTA improves diagnostic performance has not been examined to date. Methods: We evaluated 127 patients (mean age 53.0, 54.3% male) who underwent CCTA and ICA. Percentage of coronary stenosis was assessed by quantitative coronary angiography (QCA), which served as the reference comparator to CCTA. CCTA stenosis was categorized as 0%, 1-24%, 25-49%, 50-69%, 70-99%, and 100% luminal diameter reduction. Automated software (SmartHeart, Redwood City, CA) was used to measure resting CT perfusion attenuation patterns in myocardial segments by AHA 17-segment model. Segmental CT attenuation values were assigned to territories subtended by left anterior descending (LAD), left circumflex (LCX), and right coronary arteries (RCA). Per-patient and per-vessel analyses were based on highest severity (maximal stenosis, minimal attenuation). On both per-patient and per-vessel basis, logistic regression was devised for CCTA stenosis alone and for CCTA plus resting myocardial attenuation. Diagnostic accuracy and area under the receiver operating characteristics curve (AUC) were determined. Results: Diagnostic accuracy of CCTA alone was 84.0%, 85.5%, 90.4%, and 88.6%, at per-patient, per-LAD, per-LCX and per-RCA level, respectively. In comparison, the accuracy of CCTA plus myocardial attenuation were 89.6%, 91.9%, 95.2%, and 92.7%. The AUCs using CCTA alone to discriminate QCA-confirmed coronary stenoses >70% were 0.823 (95% CI: 0.737-0.909), 0.782 (95% CI: 0.667-0.898), 0.690 (95% CI: 0.503-0.878), and 0.793 (95% CI: 0.640-0.945) for per-patient, per-LAD, per-LCX, and per-RCA analysis, respectively. The AUCs using CCTA plus myocardial attenuation improved to 0.864 (95% CI: 0.765-0.962), 0.881 (95% CI: 0.793-0.968), 0.772 (95% CI: 0.535-1.000), and 0.820 (95% CI: 0.685-0.954). Conclusions: The addition of resting CT myocardial perfusion attenuation patterns improves identification and discrimination of high-grade coronary stenosis by CCTA.

Diagnostic value of apparent diffusion coefficient in differentiating between high-grade gliomas and brain metastases

2017 ◽  
Vol 59 (5) ◽  
pp. 599-605 ◽  
Author(s):  
Ionut Caravan ◽  
Cristiana Augusta Ciortea ◽  
Alexandra Contis ◽  
Andrei Lebovici
Keyword(s):  
Diffusion Coefficient ◽  
Brain Metastases ◽  
Apparent Diffusion Coefficient ◽  
Curve Analysis ◽  
High Grade ◽  
Peritumoral Edema ◽  
Roc Curve Analysis ◽  
Apparent Diffusion ◽  
Adc Values ◽  
The Mean

Background High-grade gliomas (HGGs) and brain metastases (BMs) can display similar imaging characteristics on conventional MRI. In HGGs, the peritumoral edema may be infiltrated by the malignant cells, which was not observed in BMs. Purpose To determine whether the apparent diffusion coefficient values could differentiate HGGs from BMs. Material and Methods Fifty-seven patients underwent conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) before treatment. The minimum and mean ADC in the enhancing tumor (ADCmin, ADCmean) and the minimum ADC in the peritumoral region (ADCedema) were measured from ADC maps. To determine whether there was a statistical difference between groups, ADC values were compared. A receiver operating characteristic (ROC) curve analysis was used to determine the cutoff ADC value for distinguishing between HGGs and BMs. Results The mean ADCmin values in the intratumoral regions of HGGs were significantly higher than those in BMs. No differences were observed between groups regarding ADCmean values. The mean ADCmin values in the peritumoral edema of HGGs were significantly lower than those in BMs. According to ROC curve analysis, a cutoff value of 1.332 × 10−3 mm2/s for the ADCedema generated the best combination of sensitivity (95%) and specificity (84%) for distinguishing between HGGs and BMs. The same value showed a sensitivity of 95.6% and a specificity of 100% for distinguishing between GBMs and BMs. Conclusion ADC values from DWI were found to distinguish between HGGs and solitary BMs. The peritumoral ADC values are better than the intratumoral ADC values in predicting the tumor type.

scholarly journals Diagnostic Value of CT- and MRI-Based Texture Analysis and Imaging Findings for Grading Cartilaginous Tumors in Long Bones

2021 ◽  
Vol 11 ◽  
Author(s):  
Xue-Ying Deng ◽  
Hai-Yan Chen ◽  
Jie-Ni Yu ◽  
Xiu-Liang Zhu ◽  
Jie-Yu Chen ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Feature Model ◽  
Imaging Feature ◽  
Low Grade ◽  
Long Bones ◽  
Imaging Features ◽  
High Grade ◽  
Accuracy Rate ◽  
Predictive Values ◽  
Cartilaginous Tumors

ObjectiveTo confirm the diagnostic performance of computed tomography (CT)-based texture analysis (CTTA) and magnetic resonance imaging (MRI)-based texture analysis for grading cartilaginous tumors in long bones and to compare these findings to radiological features.Materials and MethodsTwenty-nine patients with enchondromas, 20 with low-grade chondrosarcomas and 16 with high-grade chondrosarcomas were included retrospectively. Clinical and radiological information and 9 histogram features extracted from CT, T1WI, and T2WI were evaluated. Binary logistic regression analysis was performed to determine predictive factors for grading cartilaginous tumors and to establish diagnostic models. Another 26 patients were included to validate each model. Receiver operating characteristic (ROC) curves were generated, and accuracy rate, sensitivity, specificity and positive/negative predictive values (PPV/NPV) were calculated.ResultsOn imaging, endosteal scalloping, cortical destruction and calcification shape were predictive for grading cartilaginous tumors. For texture analysis, variance, mean, perc.01%, perc.10%, perc.99% and kurtosis were extracted after multivariate analysis. To differentiate benign cartilaginous tumors from low-grade chondrosarcomas, the imaging features model reached the highest accuracy rate (83.7%) and AUC (0.841), with a sensitivity of 75% and specificity of 93.1%. The CTTA feature model best distinguished low-grade and high-grade chondrosarcomas, with accuracies of 71.9%, and 80% in the training and validation groups, respectively; T1-TA and T2-TA could not distinguish them well. We found that the imaging feature model best differentiated benign and malignant cartilaginous tumors, with an accuracy rate of 89.2%, followed by the T1-TA feature model (80.4%).ConclusionsThe imaging feature model and CTTA- or MRI-based texture analysis have the potential to differentiate cartilaginous tumors in long bones by grade. MRI-based texture analysis failed to grade chondrosarcomas.

scholarly journals Characterization of Enhancing MS Lesions by Dynamic Texture Parameter Analysis of Dynamic Susceptibility Perfusion Imaging

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Rajeev K. Verma ◽  
Johannes Slotboom ◽  
Cäcilia Locher ◽  
Mirjam R. Heldner ◽  
Christian Weisstanner ◽  
...  
Keyword(s):  
Perfusion Imaging ◽  
Parameter Analysis ◽  
Dynamic Susceptibility ◽  
Imaging Features ◽  
Dynamic Texture ◽  
Feature Maps ◽  
Texture Parameter ◽  
Mr Perfusion Imaging ◽  
Texture Parameters ◽  
Main Effects

Purpose. The purpose of this study was to investigate statistical differences with MR perfusion imaging features that reflect the dynamics of Gadolinium-uptake in MS lesions using dynamic texture parameter analysis (DTPA).Methods. We investigated 51 MS lesions (25 enhancing, 26 nonenhancing lesions) of 12 patients. Enhancing lesions (n=25) were prestratified into enhancing lesions with increased permeability (EL+;n=11) and enhancing lesions with subtle permeability (EL−;n=14). Histogram-based feature maps were computed from the raw DSC-image time series and the corresponding texture parameters were analyzed during the inflow, outflow, and reperfusion time intervals.Results. Significant differences (p<0.05) were found between EL+ and EL− and between EL+ and nonenhancing inactive lesions (NEL). Main effects between EL+ versus EL− and EL+ versus NEL were observed during reperfusion (mainly in mean and standard deviation (SD): EL+ versus EL− and EL+ versus NEL), while EL− and NEL differed only in their SD during outflow.Conclusion. DTPA allows grading enhancing MS lesions according to their perfusion characteristics. Texture parameters of EL− were similar to NEL, while EL+ differed significantly from EL− and NEL. Dynamic texture analysis may thus be further investigated as noninvasive endogenous marker of lesion formation and restoration.

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