***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: ABSTRACT Objective There is a rich amount of quantitative information in spectral data sets generated from dual-energy CT ( DECT ) . In this study , we compare the performance of texture analysis performed on multi-energy data sets to that of virtual monochromatic images ( VMIs ) at 65 keV only , using classification of the two most common benign parotid neoplasms as a testing paradigm . Methods Forty-two patients with pathologically proven Warthin tumour ( n = 25 ) or pleomorphic adenoma ( n = 17 ) were evaluated . Texture analysis was performed on VMIs ranging from 40 to 140 keV in 5-keV increments ( multi-energy analysis ) or 65-keV VMIs only , which is typically considered equivalent to single-energy CT . R and om forest ( RF ) models were constructed for outcome prediction using separate r and omly selected training and testing sets or the entire patient set . Results Using multi-energy texture analysis , tumour classification in the independent testing set had accuracy , sensitivity , specificity , positive predictive value , and negative predictive value of 92 % , 86 % , 100 % , 100 % , and 83 % , compared to 75 % , 57 % , 100 % , 100 % , and 63 % , respectively , for single-energy analysis . Conclusions Multi-energy texture analysis demonstrates superior performance compared to single-energy texture analysis of VMIs at 65 keV for classification of benign parotid tumours . Key Points• We present and vali date a paradigm for texture analysis of DECT scans.• Multi-energy data set texture analysis is superior to single-energy data set texture analysis .• DECT texture analysis has high accura\cy for diagnosis of benign parotid tumours.• DECT texture analysis with machine learning can enhance non-invasive diagnostic tumour evaluation Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics . Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity . In this work , 1615  radiomic features  ( quantifying tumour image intensity , shape , texture ) extracted from  pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences ( LR ) and distant metastases ( DM ) in head- and -neck cancer . Prediction models combining radiomic and clinical variables were constructed via r and om forests and imbalance-adjustment strategies using two of the four cohorts . Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts ( LR : AUC = 0.69 and CI = 0.67 ; DM : AUC = 0.86 and CI = 0.88 ) . Furthermore , the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups . This could have important clinical impact , notably by allowing for a better personalization of chemo-radiation treatments for head- and -neck cancer patients from different risk groups Purpose To evaluate a radiomics model of Breast Imaging Reporting and Data System ( BI-RADS ) 4 and 5 breast lesions extracted from breast-tissue-optimized kurtosis magnetic resonance ( MR ) imaging for lesion characterization by using a sensitivity threshold similar to that of biopsy . Material s and Methods This institutional study included 222 women at two independent study sites ( site 1 : training set of 95 patients ; mean age ± st and ard deviation , 58.6 years ± 6.6 ; 61 malignant and 34 benign lesions ; site 2 : independent test set of 127 patients ; mean age , 58.2 years ± 6.8 ; 61 malignant and 66 benign lesions ) . All women presented with a finding suspicious for cancer at x-ray mammography ( BI-RADS 4 or 5 ) and an indication for biopsy . Before biopsy , diffusion-weighted MR imaging ( b values , 0 - 1500 sec/mm2 ) was performed by using 1.5-T imagers from different MR imaging vendors . Lesions were segmented and voxel-based kurtosis fitting adapted to account for fat signal contamination was performed . A radiomics feature model was developed by using a r and om forest regressor . The fixed model was tested on an independent test set . Conventional interpretations of MR imaging were also assessed for comparison . Results The radiomics feature model reduced false-positive results from 66 to 20 ( specificity 70.0 % [ 46 of 66 ] ) at the predefined sensitivity of greater than 98.0 % [ 60 of 61 ] in the independent test set , with BI-RADS 4a and 4b lesions benefiting from the analysis ( specificity 74.0 % , [ 37 of 50 ] ; 60.0 % [ nine of 15 ] ) and BI-RADS 5 lesions showing no added benefit . The model significantly improved specificity compared with the median apparent diffusion coefficient ( P < .001 ) and apparent kurtosis coefficient ( P = .02 ) alone . Conventional reading of dynamic contrast material -enhanced MR imaging provided sensitivity of 91.8 % ( 56 of 61 ) and a specificity of 74.2 % ( 49 of 66 ) . Accounting for fat signal intensity during fitting significantly improved the area under the curve of the model ( P = .001 ) . Conclusion A radiomics model based on kurtosis diffusion-weighted imaging performed by using MR imaging machines from different vendors allowed for reliable differentiation between malignant and benign breast lesions in both a training and an independent test data set . © RSNA , 2018 Online supplemental material is available for this article Abstract Purpose : An association between radiomic features extracted from CT and local tumor control in the head and neck squamous cell carcinoma ( HNSCC ) has been shown . This study investigated the value of pretreatment functional imaging ( 18F-FDG PET ) radiomics for modeling of local tumor control . Material and Methods : Data from HNSCC patients ( n = 121 ) treated with definitive radiochemotherapy  were used for model training . In total , 569 radiomic features were extracted from both contrast-enhanced CT and 18F-FDG PET images in the primary tumor region . CT , PET and combined PET/CT radiomic models to assess local tumor control were trained separately . Five feature selection and three classification methods were implemented . The performance of the models was quantified using concordance index ( CI ) in 5-fold cross validation in the training cohort . The best models , per image modality , were compared and verified in the independent validation cohort ( n = 51 ) . The difference in CI was investigated using bootstrapping . Additionally , the observed and radiomics-based estimated probabilities of local tumor control were compared between two risk groups . Results : The feature selection using principal component analysis and the classification based on the multivariabale Cox regression with backward selection of the variables result ed in the best models for all image modalities ( CICT = 0.72 , CIPET = 0.74 , CIPET/CT = 0.77 ) . Tumors more homogenous in CT density ( decreased GLSZMsize_zone_entropy ) and with a focused region of high FDG uptake ( higher GLSZMSZLGE ) indicated better prognosis . No significant difference in the performance of the models in the validation cohort was observed ( CICT = 0.73 , CIPET = 0.71 , CIPET/CT = 0.73 ) . However , the CT radiomics-based model overestimated the probability of tumor control in the poor prognostic group ( predicted = 68 % , observed = 56 % ) . Conclusions : Both CT and PET radiomics showed equally good discriminative power for local tumor control modeling in HNSCC . However , CT-based predictions overestimated the local control rate in the poor prognostic validation cohort , and thus , we recommend to base the local control modeling on the 18F-FDG PET Objectives The aim of this study was to investigate whether radiomic analysis with r and om survival forests ( RSFs ) can predict overall survival from T1-weighted contrast-enhanced baseline magnetic resonance imaging ( MRI ) scans in a cohort of glioblastoma multiforme ( GBM ) patients with uniform treatment . Material s and Methods This retrospective study was approved by the institutional review board and informed consent was waived . The  MRI scans from 66 patients with newly diagnosed GBM from a previous prospect i ve study were analyzed . Tumors were segmented manually on contrast-enhanced 3-dimensional T1-weighted images . Using these segmentations , P = 208 quantitative image features characterizing tumor shape , signal intensity , and texture were calculated in an automated fashion . On this data set , an RSF was trained using 10-fold cross validation to establish a link between image features and overall survival , and the individual risk for each patient was predicted . The mean concordance index was assessed as a measure of prediction accuracy . Association of individual risk with overall survival was assessed using Kaplan-Meier analysis and a univariate proportional hazards model . Results Mean overall survival was 14 months ( range , 0.8–85 months ) . Mean concordance index of the 10-fold cross-vali date d RSF was 0.67 . Kaplan-Meier analysis clearly distinguished 2 patient groups with high and low predicted individual risk ( P = 5.5 × 10−5 ) . Low predicted individual mortality was found to be a favorable prognostic factor for overall survival in a univariate Cox proportional hazards model ( hazards ratio , 1.038 ; 95 % confidence interval , 1.015–1.062 ; P = 0.0059 ) . Conclusions This study demonstrates that baseline MRI  in GBM patients contains prognostic information , which can be accessed by radiomic analysis using RSFs Background P53 mutation status is a pivotal biomarker for gliomas . Here , we developed a machine-learning model to predict p53 status in lower- grade gliomas based on radiomic features extracted from conventional magnetic resonance ( MR ) images . Methods Preoperative MR images were retrospectively obtained from 272 patients with primary grade II/III gliomas . The patients were r and omly allocated in a 2:1 ratio to a training ( n = 180 ) or validation ( n = 92 ) set . A total of 431 radiomic features were extracted from each patient . The lest absolute shrinkage and selection operator ( LASSO ) method was used for feature selection and radiomic signature construction . Subsequently , a machine-learning model to predict p53 status was established using the selected features and a Support Vector Machine classifier . The predictive performance of all individual features and the model was calculated using receiver operating characteristic curves in both the training and validation sets . Results The p53-related radiomic signature was built using the LASSO algorithm ; this procedure consisted of four first-order statistics or related wavelet features ( including Maximum , Median , Minimum , and Uniformity ) , a shape and size-based feature ( Spherical Disproportion ) , and ten textural features or related wavelet features ( including Correlation , Run Percentage , and Sum Entropy ) . The prediction accuracies based on the area under the curve were 89.6 % in the training set and 76.3 % in the validation set , which were better than individual features . Conclusions These results demonstrate that MR image texture features are predictive of p53 mutation status in lower- grade gliomas . Thus , our procedure can be conveniently used to facilitate presurgical molecular pathological diagnosis Objectives : Human papillomavirus ( HPV ) positive oropharyngeal cancer ( oropharyngeal squamous cell carcinoma , OPSCC ) is biologically and clinical ly different from HPV negative OPSCC . Here , we evaluate the use of a radiomic approach to identify the HPV status of OPSCC . Methods : Four independent cohorts , totaling 778 OPSCC patients with HPV determined by p16 were collected . We r and omly assigned 80 % of all data for model training ( N = 628 ) and 20 % for validation ( N = 150 ) . On the pre-treatment CT images , 902 radiomic features were calculated from the gross tumor volume . Multivariable modeling was performed using least absolute shrinkage and selection operator . To assess the impact of CT artifacts in predicting HPV ( p16 ) , a model was developed on all training data ( Mall ) and on the artifact-free subset of training data ( Mno art ) . Models were vali date d on all validation data ( Vall ) , and the subgroups with ( Vart ) and without ( Vno art ) artifacts . Kaplan – Meier survival analysis was performed to compare HPV status based on p16 and radiomic model predictions . Results : The area under the receiver operator curve for Mall and Mno art ranged between 0.70 and 0.80 and was not significantly different for all validation data sets . There was a consistent and significant split between survival curves with HPV status determined by p16 [ p = 0.007 ; hazard ratio ( HR ) : 0.46 ] , Mall ( p = 0.036 ; HR : 0.55 ) and Mno art ( p = 0.027 ; HR : 0.49 ) . Conclusion : This study provides proof of concept that molecular information can be derived from st and ard medical images and shows potential for radiomics as imaging biomarker of HPV status . Advances in knowledge : Radiomics has the potential to identify clinical ly relevant molecular phenotypes Several studies have linked codeletion of chromosome arms 1p/19q in low- grade gliomas ( LGG ) with positive response to treatment and longer progression-free survival . Hence , predicting 1p/19q status is crucial for effective treatment planning of LGG . In this study , we predict the 1p/19q status from MR images using convolution 
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Conclusions The results of the studies are promising but still not mature enough for image mining tools to be implemented in the clinical setting and be widely used .