scholarly journals Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Takahiro Nakamoto ◽  
Wataru Takahashi ◽  
Akihiro Haga ◽  
Satoshi Takahashi ◽  
Shigeru Kiryu ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Malignant Glioma ◽  
Prediction Models ◽  
Magnetic Resonance Images ◽  
Validation Dataset ◽  
Independent Validation ◽  
Contrast Enhanced ◽  
Auc Value ◽  
The Mean ◽  
Grade Iii

AbstractWe conducted a feasibility study to predict malignant glioma grades via radiomic analysis using contrast-enhanced T1-weighted magnetic resonance images (CE-T1WIs) and T2-weighted magnetic resonance images (T2WIs). We proposed a framework and applied it to CE-T1WIs and T2WIs (with tumor region data) acquired preoperatively from 157 patients with malignant glioma (grade III: 55, grade IV: 102) as the primary dataset and 67 patients with malignant glioma (grade III: 22, grade IV: 45) as the validation dataset. Radiomic features such as size/shape, intensity, histogram, and texture features were extracted from the tumor regions on the CE-T1WIs and T2WIs. The Wilcoxon–Mann–Whitney (WMW) test and least absolute shrinkage and selection operator logistic regression (LASSO-LR) were employed to select the radiomic features. Various machine learning (ML) algorithms were used to construct prediction models for the malignant glioma grades using the selected radiomic features. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the prediction models in the primary dataset. The selected radiomic features for all folds in the LOOCV of the primary dataset were used to perform an independent validation. As evaluation indices, accuracies, sensitivities, specificities, and values for the area under receiver operating characteristic curve (or simply the area under the curve (AUC)) for all prediction models were calculated. The mean AUC value for all prediction models constructed by the ML algorithms in the LOOCV of the primary dataset was 0.902 ± 0.024 (95% CI (confidence interval), 0.873–0.932). In the independent validation, the mean AUC value for all prediction models was 0.747 ± 0.034 (95% CI, 0.705–0.790). The results of this study suggest that the malignant glioma grades could be sufficiently and easily predicted by preparing the CE-T1WIs, T2WIs, and tumor delineations for each patient. Our proposed framework may be an effective tool for preoperatively grading malignant gliomas.

scholarly journals Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 330
Author(s):  
Mio Adachi ◽  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Yuka Kikuchi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Diagnostic Performance ◽  
Maximum Intensity Projection ◽  
Breast Mri ◽  
Magnetic Resonance Images ◽  
Maximum Intensity ◽  
Validation Data ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

scholarly journals INDIRECT DECOMPRESSION BY LATERAL FUSION: ANALYSIS OF SAGITAL ALIGNMENT

2021 ◽  
Vol 20 (1) ◽  
pp. 50-54
Author(s):  
Thyago Guirelle Silva ◽  
Rodrigo Augusto do Amaral ◽  
Raphael Rezende Pratali ◽  
Luiz Pimenta
Keyword(s):  
Magnetic Resonance ◽  
Pelvic Incidence ◽  
Magnetic Resonance Images ◽  
Cross Sectional Area ◽  
Disc Height ◽  
Lower Limbs ◽  
Sectional Area ◽  
Cross Sectional ◽  
Indirect Decompression ◽  
The Mean

ABSTRACT Objective: To verify the effectiveness of indirect decompression after lateral access fusion in patients with high pelvic incidence. Methods: A retrospective, non-comparative, non-randomized analysis of 22 patients with high pelvic incidence who underwent lateral access fusion, 11 of whom were male and 11 female, with a mean age of 63 years (52-74), was conducted. Magnetic resonance exams were performed within one year after surgery. The cross-sectional area of the thecal sac, anterior and posterior disc heights, and bilateral foramen heights, measured pre- and postoperatively in axial and sagittal magnetic resonance images, were analyzed. The sagittal alignment parameters were measured using simple radiographs. The clinical results were evaluated using the ODI and VAS (back and lower limbs) questionnaires. Results: In all cases, the technique was performed successfully without neural complications. The mean cross-sectional area increased from 126.5 mm preoperatively to 174.3 mm postoperatively. The mean anterior disc height increased from 9.4 mm preoperatively to 12.8 mm postoperatively, while the posterior disc height increased from 6.3 mm preoperatively to 8.1 mm postoperatively. The mean height of the right foramen increased from 157.3 mm in the preoperative period to 171.2 mm in the postoperative period and that of the left foramen increased from 139.3 mm in the preoperative to 158.9 mm in the postoperative. Conclusions: This technique is capable of correcting misalignment in spinal deformity, achieving fusion and promoting the decompression of neural elements. Level of evidence III; Retrospective study.

Temporal covariance analysis of first-pass contrast-enhanced myocardial magnetic resonance images

2001 ◽  
Vol 31 (2) ◽  
pp. 133-142 ◽  
Author(s):  
Abdel-Ouahab Boudraa ◽  
Faiza Behloul ◽  
Marc Janier ◽  
Emmanuelle Canet ◽  
Jacques Champier ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Covariance Analysis ◽  
Contrast Enhanced ◽  
First Pass

scholarly journals Effusion attenuates the effect of synovitis on radiographic progression in patients with hand osteoarthritis: a longitudinal magnetic resonance imaging study

2020 ◽  
Vol 40 (1) ◽  
pp. 315-319
Author(s):  
W. Damman ◽  
R. Liu ◽  
M. Reijnierse ◽  
F. R. Rosendaal ◽  
J. L. Bloem ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Radiographic Progression ◽  
Imaging Study ◽  
Magnetic Resonance Images ◽  
High Signal ◽  
Hand Osteoarthritis ◽  
Mr Images ◽  
Contrast Enhanced ◽  
Key Points

AbstractAn exploratory study to determine the role of effusion, i.e., fluid in the joint, in pain, and radiographic progression in patients with hand osteoarthritis. Distal and proximal interphalangeal joints (87 patients, 82% women, mean age 59 years) were assessed for pain. T2-weighted and Gd-chelate contrast-enhanced T1-weighted magnetic resonance images were scored for enhanced synovial thickening (EST, i.e., synovitis), effusion (EST and T2-high signal intensity [hsi]) and bone marrow lesions (BMLs). Effusion was defined as follows: (1) T2-hsi > 0 and EST = 0; or 2) T2-hsi = EST but in different joint locations. Baseline and 2-year follow-up radiographs were scored following Kellgren-Lawrence, increase ≥ 1 defined progression. Associations between the presence of effusion and pain and radiographic progression, taking into account EST and BML presence, were explored on the joint level. Effusion was present in 17% (120/691) of joints, with (63/120) and without (57/120) EST. Effusion on itself was not associated with pain or progression. The association with pain and progression, taking in account other known risk factors, was stronger in the absence of effusion (OR [95% CI] 1.7 [1.0–2.9] and 3.2 [1.7–5.8]) than in its presence (1.6 [0.8–3.0] and 1.3 [0.5–3.1]). Effusion can be assessed on MR images and seems not to be associated with pain or radiographic progression but attenuates the association between synovitis and progression. Key Points• Effusion is present apart from synovitis in interphalangeal joints in patients with hand OA.• Effusion in finger joints can be assessed as a separate feature on MR images.• Effusion seems to be of importance for its attenuating effect on the association between synovitis and radiographic progression.

scholarly journals Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19

2020 ◽  
Author(s):  
Anjali Dhall ◽  
Sumeet Patiyal ◽  
Neelam Sharma ◽  
Salman Sadullah Usmani ◽  
Gajendra P S Raghava
Keyword(s):  
Scientific Community ◽  
Prediction Models ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
Independent Validation ◽  
Immune Epitope ◽  
Learning Techniques ◽  
Wide Range ◽  
Immune Epitope Database

Abstract Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https://webs.iiitd.edu.in/raghava/il6pred/).

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