Background Radiomics has been applied to breast magnetic resonance imaging (MRI) for gene status prediction. However, the features of peritumoral regions were not thoroughly investigated. Purpose To evaluate the use of intratumoral and peritumoral regions from functional parametric maps based on breast dynamic contrast‐enhanced MRI (DCE‐MRI) for prediction of HER‐2 and Ki‐67 status. Study Type Retrospective. Population A total of 351 female patients (average age, 51 years) with pathologically confirmed breast cancer were assigned to the training (n = 243) and validation (n = 108) cohorts. Field Strength/Sequence 3.0T, T1 gradient echo. Assessment Radiomic features were extracted from intratumoral and peritumoral regions on six functional parametric maps calculated using time‐intensity curves of DCE‐MRI. The intraclass correlation coefficients (ICCs) were used to determine the reproducibility of feature extraction. Based on the intratumoral, peritumoral, and combined intra‐ and peritumoral regions, three radiomics signatures (RSs) were built using the least absolute shrinkage and selection operator (LASSO) logistic regression model, respectively. Statistical Tests Wilcoxon rank‐sum test, minimum redundancy maximum relevance, LASSO, receiver operating characteristic curve (ROC) analysis, and DeLong test. Results The intratumoral and peritumoral RSs for prediction of HER‐2 and Ki‐67 status achieved areas under the ROC (AUCs) of 0.683 (95% confidence interval [CI], 0.574–0.793) and 0.690 (95% CI, 0.577–0.804), and 0.714 (95% CI, 0.616–0.812) and 0.692 (95% CI, 0.590–0.794) in the validation cohort, respectively. The combined RSs yielded AUCs of 0.713 (95% CI, 0.604–0.823) and 0.749 (95% CI, 0.656–0.841), respectively. There were no significant differences in prediction performance among intratumoral, peritumoral, and combined RSs. Most (69.7%) of the features had good agreement (ICCs >0.8). Data Conclusion Radiomic features of intratumoral and peritumoral regions on functional parametric maps based on breast DCE‐MRI had the potential to identify HER‐2 and Ki‐67 status. Level of Evidence: 3 Technical Efficacy Stage: 2

Background Conditioned pain modulation (CPM) is impaired in people with chronic pain such as knee osteoarthritis (KOA). The purpose of this randomized, controlled clinical trial was to investigate whether strong electroacupuncture (EA) was more effective on chronic pain by strengthening the CPM function than weak EA or sham EA in patients with KOA. Methods In this multicenter, three-arm parallel, single-blind randomized controlled trial, 301 patients with KOA were randomly assigned. Patients were randomized into three groups based on EA current intensity: strong EA (> 2 mA), weak EA (< 0.5 mA), and sham EA (non-acupoint). Treatments consisted of five sessions per week, for 2 weeks. Primary outcome measures were visual analog scale (VAS), CPM function, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Results Three hundred one patients with KOA were randomly assigned, among which 271 (90.0%) completed the study (mean age 63.93 years old). One week of EA had a clinically important improvement in VAS and WOMAC but not in CPM function. After 2 weeks treatment, EA improved VAS, CPM, and WOMAC compared with baseline. Compared with sham EA, weak EA (3.8; 95% CI 3.45, 4.15; P < .01) and strong EA (13.54; 95% CI 13.23, 13.85; P < .01) were better in improving CPM function. Compared with weak EA, strong EA was better in enhancing CPM function (9.73; 95% CI 9.44, 10.02; P < .01), as well as in reducing VAS and total WOMAC score. Conclusion EA should be administered for at least 2 weeks to exert a clinically important effect on improving CPM function of KOA patients. Strong EA is better than weak or sham EA in alleviating pain intensity and inhibiting chronic pain. Trial registration This study was registered with the Chinese Clinical Trial Registry ( ChiCTR-ICR-14005411 ), registered on 31 October 2014. Electronic supplementary material The online version of this article (10.1186/s13075-019-1899-6) contains supplementary material, which is available to authorized users.

Images can not only display contents themselves, but also convey emotions, e.g., excitement, sadness. Affective image classification is useful and hot in many fields such as computer vision and multimedia. Current researches usually consider the relationship model between images and emotions as a black box. They extract the traditional discursive visual features such as SIFT and wavelet textures, and use them directly upon various classification algorithms. However, these visual features are not interpretable, and people cannot know why such a set of features induce a particular emotion. And due to the highly subjective nature of images, the classification accuracies on these visual features are not satisfactory for a long time. We propose the interpretable aesthetic features to describe images inspired by art theories, which are intuitive, discriminative and easily understandable. Affective image classification based on these features can achieve higher accuracy, compared with the state-of-the-art. Specifically, the features can also intuitively explain why an image tends to convey a certain emotion. We also develop an emotion guided image gallery to demonstrate the proposed feature collection.

Purpose: To propose a new clustering method for the automatic detection of arterial input function (AIF) with high accuracy in dynamic susceptibility contrastmagnetic resonance imaging (DSC-MRI). Materials and Methods:A novel method for automatically determining the AIF was proposed to facilitate the analysis of MR perfusion, which relied on normalized cut (Ncut) clustering. Its performance was compared with those of two other previously reported clustering methods: k-means and fuzzy c-means (FCM) techniques, in terms of the detection accuracy and computational time. Both simulated perfusion data and data collected from 42 healthy human subjects were applied to investigate the feasibility of the proposed approach.Results: In the simulation study, the partial volume effect (PVE) level, peak value (PV), time to peak (TTP), full width at half maximum (FWHM), area under AIF curve (AUC), root mean square error (RMSE) between the estimated AIF and true AIF, and M value given by [PV/ (FWHMÂTTP)] were 45. 45, 4.2737, 29.92, 6.4563, 76.4836, 0.0519, and 0.0221 for Ncut-based AIF, 96.45, 3.8385, 31.74, 7.5133, 75.7364, 0.3295, and 0.0161 for FCM-based AIF, 91.18, 3.8990, 31.73, 7.4544, 76.0476, 0.3128, and 0.0165 for k-means-based AIF, 0, 4.4592, 29.51, 6.2016, 76.8669, 0, and Conclusion: Ncut clustering yield AIFs more in line with the expected AIF, and might be preferred to FCM and kmeans clustering methods sensitive to randomly selected initial centers.

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IntroductionArterial input function (AIF) plays an important role in the quantification of cerebral hemodynamics. The purpose of this study was to select the best reproducible clustering method for AIF detection by comparing three algorithms reported previously in terms of detection accuracy and computational complexity.MethodsFirst, three reproducible clustering methods, normalized cut (Ncut), hierarchy (HIER), and fast affine propagation (FastAP), were applied independently to simulated data which contained the true AIF. Next, a clinical verification was performed where 42 subjects participated in dynamic susceptibility contrast MRI (DSC-MRI) scanning. The manual AIF and AIFs based on the different algorithms were obtained. The performance of each algorithm was evaluated based on shape parameters of the estimated AIFs and the true or manual AIF. Moreover, the execution time of each algorithm was recorded to determine the algorithm that operated more rapidly in clinical practice.ResultsIn terms of the detection accuracy, Ncut and HIER method produced similar AIF detection results, which were closer to the expected AIF and more accurate than those obtained using FastAP method; in terms of the computational efficiency, the Ncut method required the shortest execution time.ConclusionNcut clustering appears promising because it facilitates the automatic and robust determination of AIF with high accuracy and efficiency.

During dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI), it has been demonstrated that the arterial input function (AIF) can be obtained using fuzzy c-means (FCM) and k-means clustering methods. However, due to the dependence on the initial centers of clusters, both clustering methods have poor reproducibility between the calculation and recalculation steps. To address this problem, the present study developed an alternative clustering technique based on the agglomerative hierarchy (AH) method for AIF determination. The performance of AH method was evaluated using simulated data and clinical data based on comparisons with the two previously demonstrated clustering-based methods in terms of the detection accuracy, calculation reproducibility, and computational complexity. The statistical analysis demonstrated that, at the cost of a significantly longer execution time, AH method obtained AIFs more in line with the expected AIF, and it was perfectly reproducible at different time points. In our opinion, the disadvantage of AH method in terms of the execution time can be alleviated by introducing a professional high-performance workstation. The findings of this study support the feasibility of using AH clustering method for detecting the AIF automatically.

Objective: To investigate the value of texture features derived from T2-weighted magnetic resonance imaging (T2WI) for predicting preoperative lymph node invasion (N stage) in rectal cancer. Materials and Methods: One hundred and eighty-two patients with histopathologically confirmed rectal cancer and preoperative magnetic resonance imaging were retrospectively analyzed, who were divided into high (N1-2) and low N stage (N0). Texture features were calculated from histogram, gray-level co-occurrence matrix, and gray-level run-length matrix from sagittal fat-suppression and oblique axial T2WI. Independent sample t-test or Mann-Whitney U-test were used for statistical analysis. Multivariate logistic regression analysis was conducted to build the predictive models. Predictive performance was evaluated by receiver operating characteristic (ROC) analysis. Results: Energy (ENE), entropy (ENT), information correlation (INC), long-run emphasis (LRE), and short-run low gray-level emphasis (SRLGLE) extracted from sagittal fat-suppression T2WI, and ENE, ENT, INC, low gray-level run emphasis (LGLRE), and SRLGLE from oblique axial T2WI were significantly different between stage N0 and stage N1-2 tumors. The multivariate analysis for features from sagittal fat-suppression T2WI showed that higher SRLGLE and lower ENE were independent predictors of lymph node invasion. The model reached an area under ROC curve (AUC) of 0.759. The analysis for features from oblique axial T2WI showed that higher INC and SRLGLE were independent predictors. The model achieved an AUC of 0.747. The analysis for all extracted features showed that lower ENE from sagittal fat-suppression T2WI and higher INC and SRLGLE from oblique axial T2WI were independent predictors. The model showed an AUC of 0.772. Conclusions: Texture features derived from T2WI could provide valuable information for identifying the status of lymph node invasion in rectal cancer.