BackgroundMRI‐based radiomics has been used to diagnose breast lesions; however, little research combining quantitative pharmacokinetic parameters of dynamic contrast‐enhanced MRI (DCE‐MRI) and diffusion kurtosis imaging (DKI) exists.PurposeTo develop and validate a multimodal MRI‐based radiomics model for the differential diagnosis of benign and malignant breast lesions and analyze the discriminative abilities of different MR sequences.Study TypeRetrospective.PopulationIn all, 207 female patients with 207 histopathology‐confirmed breast lesions (95 benign and 112 malignant) were included in the study. Then 159 patients were assigned to the training group, and 48 patients comprised the validation group.Field Strength/SequenceT2‐weighted (T2W), T1‐weighted (T1W), diffusion‐weighted MR imaging (b‐values = 0, 500, 800, and 2000 seconds/mm2) and quantitative DCE‐MRI were performed on a 3.0T MR scanner.AssessmentRadiomics features were extracted from T2WI, T1WI, DKI, apparent diffusion coefficient (ADC) maps, and DCE pharmacokinetic parameter maps in the training set. Models based on each sequence or combinations of sequences were built using a support vector machine (SVM) classifier and used to differentiate benign and malignant breast lesions in the validation set.Statistical TestsOptimal feature selection was performed by Spearman's rank correlation coefficients and the least absolute shrinkage and selection operator algorithm (LASSO). Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the radiomics models in the validation set.ResultsThe area under the ROC curve (AUC) of the optimal radiomics model, including T2WI, DKI, and quantitative DCE‐MRI parameter maps was 0.921, with an accuracy of 0.833. The AUCs of the models based on T1WI, T2WI, ADC map, DKI, and DCE pharmacokinetic parameter maps were 0.730, 0.791, 0.770, 0.788, and 0.836, respectively.Data ConclusionThe model based on radiomics features from T2WI, DKI, and quantitative DCE pharmacokinetic parameter maps has a high discriminatory ability for benign and malignant breast lesions.Level of Evidence3Technical Efficacy Stage2 J. Magn. Reson. Imaging 2020;52:596–607.

Nasopharyngeal carcinoma (NPC) is an Epstein-Barr virus-associated malignancy occurring at high incidence in Southeast Asia and southern China. In spite of the good response to radio- and chemo-therapy at the early stage, resistance and recurrence develop in NPC patients in the advanced setting. Cancer stem cells (CSCs) play an important role in drug resistance and cancer recurrence. Here we report that lovastatin, a natural compound and a lipophilic statin that has already been used in the clinic to treat hypercholesterolemia, inhibited the CSC properties and induced apoptosis and cell cycle arrest in sphere-forming cells derived from the 5-8F and 6-10B NPC cell lines. Furthermore, lovastatin conferred enhanced sensitivity to the chemotherapeutic and photodynamic agents in NPC CSCs. Together our findings suggest that targeting CSCs by lovastatin in combination with routine chemotherapeutic drugs or photodynamic therapy might be a promising approach to the treatment of NPC.

Purpose Recent studies have illustrated that the peritumoral regions of medical images have value for clinical diagnosis. However, the existing approaches using peritumoral regions mainly focus on the diagnostic capability of the single region and ignore the advantages of effectively fusing the intratumoral and peritumoral regions. In addition, these methods need accurate segmentation masks in the testing stage, which are tedious and inconvenient in clinical applications. To address these issues, we construct a deep convolutional neural network that can adaptively fuse the information of multiple tumoral‐regions (FMRNet) for breast tumor classification using ultrasound (US) images without segmentation masks in the testing stage. Methods To sufficiently excavate the potential relationship, we design a fused network and two independent modules to extract and fuse features of multiple regions simultaneously. First, we introduce two enhanced combined‐tumoral (EC) region modules, aiming to enhance the combined‐tumoral features gradually. Then, we further design a three‐branch module for extracting and fusing the features of intratumoral, peritumoral, and combined‐tumoral regions, denoted as the intratumoral, peritumoral, and combined‐tumoral module. Especially, we design a novel fusion module by introducing a channel attention module to adaptively fuse the features of three regions. The model is evaluated on two public datasets including UDIAT and BUSI with breast tumor ultrasound images. Two independent groups of experiments are performed on two respective datasets using the fivefold stratified cross‐validation strategy. Finally, we conduct ablation experiments on two datasets, in which BUSI is used as the training set and UDIAT is used as the testing set. Results We conduct detailed ablation experiments about the proposed two modules and comparative experiments with other existing representative methods. The experimental results show that the proposed method yields state‐of‐the‐art performance on both two datasets. Especially, in the UDIAT dataset, the proposed FMRNet achieves a high accuracy of 0.945 and a specificity of 0.945, respectively. Moreover, the precision (PRE = 0.909) even dramatically improves by 21.6% on the BUSI dataset compared with the existing method of the best result. Conclusion The proposed FMRNet shows good performance in breast tumor classification with US images, and proves its capability of exploiting and fusing the information of multiple tumoral‐regions. Furthermore, the FMRNet has potential value in classifying other types of cancers using multiple tumoral‐regions of other kinds of medical images.