Background
Accurate glioma grading plays an important role in the clinical management of patients and is also the basis of molecular stratification nowadays.
Purpose/Hypothesis
To verify the ...superiority of radiomics features extracted from multiparametric MRI to glioma grading and evaluate the grading potential of different MRI sequences or parametric maps.
Study Type
Retrospective; radiomics.
Population
A total of 153 patients including 42, 33, and 78 patients with Grades II, III, and IV gliomas, respectively.
Field Strength/Sequence
3.0T MRI/T1‐weighted images before and after contrast‐enhanced, T2‐weighted, multi‐b‐value diffusion‐weighted and 3D arterial spin labeling images.
Assessment
After multiparametric MRI preprocessing, high‐throughput features were derived from patients' volumes of interests (VOIs). The support vector machine‐based recursive feature elimination was adopted to find the optimal features for low‐grade glioma (LGG) vs. high‐grade glioma (HGG), and Grade III vs. IV glioma classification tasks. Then support vector machine (SVM) classifiers were established using the optimal features. The accuracy and area under the curve (AUC) was used to assess the grading efficiency.
Statistical Tests
Student's t‐test or a chi‐square test were applied on different clinical characteristics to confirm whether intergroup significant differences exist.
Results
Patients' ages between LGG and HGG groups were significantly different (P < 0.01). For each patient, 420 texture and 90 histogram parameters were derived from 10 VOIs of multiparametric MRI. SVM models were established using 30 and 28 optimal features for classifying LGGs from HGGs and grades III from IV, respectively. The accuracies/AUCs were 96.8%/0.987 for classifying LGGs from HGGs, and 98.1%/0.992 for classifying grades III from IV, which were more promising than using histogram parameters or using the single sequence MRI.
Data Conclusion
Texture features were more effective for noninvasively grading gliomas than histogram parameters. The combined application of multiparametric MRI provided a higher grading efficiency. The proposed radiomic strategy could facilitate clinical decision‐making for patients with varied glioma grades.
Level of Evidence: 3
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2018;48:1518–1528
We aimed to study the effects of a methyltransferase 3 (METTL3) inhibitor on osteomyelitis. Bone marrow cells (BMs) and peripheral blood mononuclear cells (PBMCs) were isolated from osteomyelitis ...patients at our hospital. Primary BM-derived macrophages (BMDMs) were incubated with lipopolysaccharide (LPS), poly(I:C), or PAM3CSK4 after pretreatment with STM2457. S. aureus was injected into the intramedullary canal to construct an osteomyelitis C57BL/6 mice model, which was then treated with STM2457. Body weights, μCT three-dimensional analyses, and bacterial burdens of the mice were obtained. Up-regulated METTL3 expression was found in both BMs and PBMCs of osteomyelitis patients. LPS and PAM3CSK4-induced IL-6 and TNF-α secretion in BMDMs could be inhibited by STM2457 pretreatment, while STM2457 pretreatment did not affect the relative expression of NOS2, IL-6, and TNF-α after incubation with poly(I:C). STM2457 alleviated the symptoms of osteomyelitis in mice with increased body weights, diminished reactive bone formation and cortical bone loss, increased bacterial burdens, and decreased IL-6 and TNF-α secretion. STM2457 pretreatment down-regulated the relative expression of myeloid differentiation factor 88 (MyD88), p-TAK, and p-IKKα/β in LPS-stimulated BMDMs, while it did not show any effect on poly(I:C)-stimulated BMDMs. STM2457 alleviates the onset of osteomyelitis in mice by down-regulating the relative expression of MyD88 and NF-κB relevant inflammation molecules in macrophages.
Background
Data on the incidence, mortality, and other burden of oral cancer as well as their secular trends are necessary to provide policy‐makers with the information needed to allocate resources ...appropriately. The purpose of this study was to use the Global Burden of Disease (GBD) 2017 results to estimate the incidence, mortality, and disability‐adjusted life years (DALYs) for oral cancer from 1990 to 2017.
Methods
We collected detailed data on oral cancer from 1990 to 2017 from the GBD 2017. The global incidence, mortality, and DALYs attributable to oral cancer as well as the corresponding age‐standardized rates (ASRs) were calculated. The estimated annual percentage changes in the ASRs of incidence (ASRI) and mortality (ASRM) and age‐standardized DALYs of oral cancer were also calculated according to regions and countries to quantify the secular trends in these rates.
Results
We tracked the incidence, mortality, and DALYs of oral cancer in 195 countries/territories over 28 years. Globally, the incidence, mortality, and DALYs of oral cancer increased by about 1.0‐fold from 1990 to 2017. The ASRI of oral cancer showed a similar trend, increasing from 4.41 to 4.84 per 100,000 person‐years during the study period. The ASRM remained approximately stable at about 2.4 per 100,000 from 1990 to 2017, as did the age‐standardized DALYs, at about 64.0 per 100,000 person‐years. ASRI was highest in Pakistan (27.03/100,000, 95% CI = 22.13‐32.75/100,000), followed by Taiwan China, and lowest in Iraq (0.96/100,000, 95% CI = 0.86‐1.06/100,000). ASRM was highest in Pakistan (16.85/100,000, 95% CI = 13.92‐20.17/100,000) and lowest in Kuwait (0.51/100,000, 95% CI = 0.45‐0.58/100,000).
Conclusions
The ASRI of oral cancer has increased slightly worldwide, while the ASRM and age‐standardized DALY have remained stable. However, these characteristics vary between countries, suggesting that current prevention strategies should be reoriented, and much more targeted and specific strategies should be established in some countries to forestall the increase in oral cancer.
Accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. But previous studies on magnetic resonance imaging (MRI) images were not effective ...enough. According to the remarkable performance of convolutional neural network (CNN) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the World Health Organization (WHO) low grade and high grade gliomas.
One hundred and thirteen glioma patients were retrospectively included. Tumor images were segmented with a rectangular region of interest (ROI), which contained about 80% of the tumor. Then, 20% data were randomly selected and leaved out at patient-level as test dataset. AlexNet and GoogLeNet were both trained from scratch and fine-tuned from models that pre-trained on the large scale natural image database, ImageNet, to magnetic resonance images. The classification task was evaluated with five-fold cross-validation (CV) on patient-level split.
The performance measures, including validation accuracy, test accuracy and test area under curve (AUC), averaged from five-fold CV of GoogLeNet which trained from scratch were 0.867, 0.909, and 0.939, respectively. With transfer learning and fine-tuning, better performances were obtained for both AlexNet and GoogLeNet, especially for AlexNet. Meanwhile, GoogLeNet performed better than AlexNet no matter trained from scratch or learned from pre-trained model.
In conclusion, we demonstrated that the application of CNN, especially trained with transfer learning and fine-tuning, to preoperative glioma grading improves the performance, compared with either the performance of traditional machine learning method based on hand-crafted features, or even the CNNs trained from scratch.
Background
The aim of this study is to estimate the incidence, mortality, and disability‐adjusted life years (DALYs) of nasopharyngeal carcinoma from 1990 to 2017.
Methods
We collected detailed ...information on nasopharyngeal carcinoma from 1990 to 2017 based on data from Global Burden of Disease (GBD) study 2017. The global incidence, mortality, and DALYs attributable to nasopharyngeal carcinoma was reported, as well as the age‐standardized rates (ASRs).
Results
The ASR of nasopharyngeal carcinoma incidence decreased from 1.88 (95% UI: 1.76‐2.00) in 1990 to 1.35 (95% UI: 1.28‐1.42) in 2017. The ASR of mortality decreased from 1.19 (95% UI: 1.13‐1.25) in 1990 to 0.86 (95% UI: 0.82‐0.89) in 2017, while ASR‐DALYs decreased from 38.2 (95% UI: 35.9‐40.2) in 1990 to 25.4 (95% UI: 24.4‐26.5) in 2017.
Conclusions
The ASR of incidence, mortality, and DALYs of nasopharyngeal carcinoma have decreased slightly worldwide. East Asia carried the heaviest burden of nasopharyngeal carcinoma. The majority of nasopharyngeal carcinoma burden was observed in men, especially among male aged 55 to 69 years.
Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance ...imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.
Background
Accurate glioma grading plays an important role in patient treatment.
Purpose
To investigate the influence of varied texture retrieving models on the efficacy of grading glioma with ...support vector machine (SVM).
Study Type
Retrospective.
Population
In all, 117 glioma patients including 25, 29, and 63 grade II, III, and IV gliomas, respectively, based on WHO 2007.
Field Strength/Sequence
3.0T MRI/ T1WI, T2 fluid‐attenuated inversion recovery, contrast enhanced T1, arterial spinal labeling, diffusion‐weighted imaging (0, 30, 50, 100, 200, 300, 500, 800, 1000, 1500, 2000, 3000, and 3500 sec/mm2), and dynamic contrast‐enhanced.
Assessment
Texture attributes from 30 parametric maps were retrieved using four models, including Global, gray‐level co‐occurrence matrix (GLCM), gray‐level run‐length matrix (GLRLM), and gray‐level size‐zone matrix (GLSZM). Attributes derived from varied models were input into radial basis function SVM (RBF‐SVM) combined with attribute selection using SVM‐recursive feature elimination (SVM‐RFE). The SVM model was trained and established with 80% randomly selected data of each category using 10‐fold crossvalidation. The model performance was further tested using the remaining 20% data.
Statistical Tests
Ten‐fold crossvalidation was used to validate the model performance.
Results
Based on 30 parametric maps, 90, 240, 390, or 390 texture attributes were retrieved using the Global, GLCM, GLRLM, or GLSZM model, respectively. SVM‐RFE was able to reduce attribute redundancy as well as improve RBF‐SVM performance. Training data were oversampled by applying the Synthetic Minority Oversampling Technique (SMOTE) method to overcome the data imbalance problem; test results were able to further demonstrate the classifying performance of the final models. GLSZM using gray‐level 64 was the optimal model to retrieve powerful image texture attributes to produce enough classifying power with an accuracy / area under the curve of 0.760/0.867 for the training and 0.875/0.971 for the independent test. Fifteen attributes were selected with SVM‐RFE to provide comparable classifying efficacy.
Data Conclusion
When using image textures‐based SVM classification of gliomas, the GLSZM model in combination with gray‐level 64 and attribute selection may be an optimized solution.
Level of Evidence: 2
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2019;49:1263–1274.
The purpose of this study was to explore the performance of MRI radiomics in predicting the pathologic classification and TNM staging of thymic epithelial tumors (TETs).
Clinical and MRI data for 189 ...patients with TETs were retrospectively collected. A total of 2088 radiomics features were extracted from T2-weighted images and T2-weighted fat-suppressed (FS) images. With the use of a support vector machine with recursive feature elimination, the optimal feature subsets were selected and used to construct two predictive models for pathologic classification and TNM staging. In multivariable logistic regression analysis, we incorporated the radiomics model, conventional MRI findings, and clinical variables to develop a radiomics nomogram for predicting risk stratification of advanced TETs.
Of the extracted features, 125 features were selected to construct the radiomics model for predicting pathologic classification, and 69 features were selected to construct the radiomics model for predicting TNM staging. The models achieved AUC values of 0.880 and 0.948 in the training cohort and 0.771 and 0.908 in the test cohort, respectively, for distinguishing among low-risk thymomas, high-risk thymomas, and thymic carcinomas and differentiating between early-stage and advanced-stage TETs. The radiomics model, symptom, and pericardial effusion constituted a radiomics nomogram, with an AUC value of 0.967 (95% CI, 0.891-0.989) in the training cohort and 0.957 (95% CI, 0.842-0.974) in the test cohort.
MRI radiomics analysis has the potential to differentiate the pathologic classification and TNM staging of TETs. A radiomics nomogram provides a useful tool for in dividualized prediction of the risk of advanced-stage TET before a patient undergoes treatment.
Thiopental sodium (TPTS) is a barbiturate general anesthetic, while its effects on hypoxia/reoxygenation (H/R)-induced injury are still unclear. This study aimed to investigate whether TPTS exerts ...protective effects against the H/R-induced osteoblast cell injury and explore the underlying mechanisms. Osteoblast cell injury model was induced by the H/R condition, which was treated with or without TPTS. Cell viability and lactate dehydrogenase (LDH) release were determined by the corresponding commercial kits. The levels of oxidative stress were determined in the experimental groups. Cell apoptosis and Caspase-3 activities were determined by propidium iodide staining and substrate-based assay, respectively. Western blotting and qRT-PCR were performed to measure the mRNA and protein levels, respectively. Treatment with TPTS was able to increase cell viability and reduce LDH release in H/R-induced osteoblasts. Additionally, TPTS regulated oxidative stress in H/R-induced osteoblasts by suppressing malondialdehyde (MDA) and reactive oxygen species (ROS) as well as boosting superoxide dismutase (SOD). TPTS was able to suppress cell apoptosis by suppressing Caspase-3 activity and cleavage. TPTS exerted protective effects against cell injury and apoptosis induced by the H/R conditions, which were associated with its regulation of Akt signaling. Moreover, TPTS induced osteoblast differentiation under the H/R condition. In summary, TPTS attenuates H/R-induced injury in osteoblasts by regulating AKT signaling.
The methylation status of oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter has been associated with treatment response in glioblastoma(GBM). Using pre-operative MRI techniques to predict ...MGMT promoter methylation status remains inconclusive. In this study, we investigated the value of features from structural and advanced imagings in predicting the methylation of MGMT promoter in primary glioblastoma patients.
Ninety-two pathologically confirmed primary glioblastoma patients underwent preoperative structural MR imagings and the efficacy of structural image features were qualitatively analyzed using Fisher's exact test. In addition, 77 of the 92 patients underwent additional advanced MRI scans including diffusion-weighted (DWI) and 3-diminsional pseudo-continuous arterial spin labeling (3D pCASL) imaging. Apparent diffusion coefficient (ADC) and relative cerebral blood flow (rCBF) values within the manually drawn region-of-interest (ROI) were calculated and compared using independent sample t test for their efficacies in predicting MGMT promoter methylation. Receiver operating characteristic curve (ROC) analysis was used to investigate the predicting efficacy with the area under the curve (AUC) and cross validations. Multiple-variable logistic regression model was employed to evaluate the predicting performance of multiple variables.
MGMT promoter methylation was associated with tumor location and necrosis (P < 0.05). Significantly increased ADC value (P < 0.001) and decreased rCBF (P < 0.001) were associated with MGMT promoter methylation in primary glioblastoma. The ADC achieved the better predicting efficacy than rCBF (ADC: AUC, 0.860; sensitivity, 81.1%; specificity, 82.5%; vs rCBF: AUC, 0.835; sensitivity, 75.0%; specificity, 78.4%; P = 0.032). The combination of tumor location, necrosis, ADC and rCBF resulted in the highest AUC of 0.914.
ADC and rCBF are promising imaging biomarkers in clinical routine to predict the MGMT promoter methylation in primary glioblastoma patients.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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