Breast cancer stem cells (BCSCs) have been considered responsible for cancer progression, recurrence, metastasis and drug resistance. However, the mechanisms by which cells acquire self‐renewal and chemoresistance properties are remaining largely unclear. Herein, we evaluated the role of miR‐708 and metformin in BCSCs, and found that the expression of miR‐708 is significantly down‐regulated in BCSCs and tumour tissues, and correlates with chemotherapy response and prognosis. Moreover, miR‐708 markedly inhibits sphere formation, CD44+/CD24− ratio, and tumour initiation and increases chemosensitivity of BCSCs. Mechanistically, miR‐708 directly binds to cluster of differentiation 47 (CD47), and regulates tumour‐associated macrophage‐mediated phagocytosis. On the other hand, CD47 is essential for self‐renewal, tumour initiation and chemoresistance of BCSCs, and correlates with the prognosis of breast cancer patients. In addition, the anti‐type II diabetes drug metformin are found to be involved in the miR‐708/CD47 signalling pathway. Therefore, our study demonstrated that miR‐708 plays an important tumour suppressor role in BCSCs self‐renewal and chemoresistance, and the miR‐708/CD47 regulatory axis may represent a novel therapeutic mechanism of metformin in BCSCs.
Magnetic resonance imaging exhibits high sensitivity but low specificity for breast cancer. The present study aimed to investigate whether combining morphology, texture features and kinetic features with diffusion-weighted imaging using quantitative analysis improves the accuracy of discriminating malignant from benign breast masses. In total, 104 and 171 malignant lesions in 205 women were included. Additionally, 13 texture and 11 morphology features were computed from each lesion using a semi-automated segmentation method. To increase prediction accuracy, a newly designed classification model, difference-weighted local hyperplane, was used for statistical analysis of the combined effects of the features for predicting lesion type. The mean apparent diffusion coefficient (ADC) value for each lesion was calculated. Diagnostic performances of morphology and texture features, kinetic features and ADC alone and the combination of them were evaluated using receiver operating characteristics analysis. Malignant lesions had lower mean ADCs than benign lesions. By using 10-fold cross validation scheme, combined morphological and kinetic features achieved a diagnostic average accuracy of 0.87. Adding an ADC threshold of 1.37×10−3 mm2/sec increased the overall averaged accuracy to 0.90. A multivariate model combining ADC values with 6 morphological and kinetic parameters best discriminated malignant from benign lesions. Incorporating morphology and texture features, kinetic features and ADC into a multivariable diagnostic model improves the discriminatory power of breast lesions.
BackgroundAccurate prediction of recurrence is crucial for personalized treatment in breast cancer, and whether the radiomics features of ultrasound (US) could be used to predict recurrence of breast cancer is still uncertain. Here, we developed a radiomics signature based on preoperative US to predict disease-free survival (DFS) in patients with invasive breast cancer and assess its additional value to the clinicopathological predictors for individualized DFS prediction.MethodsWe identified 620 patients with invasive breast cancer and randomly divided them into the training (n = 372) and validation (n = 248) cohorts. A radiomics signature was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression in the training cohort and validated in the validation cohort. Univariate and multivariate Cox proportional hazards model and Kaplan–Meier survival analysis were used to determine the association of the radiomics signature and clinicopathological variables with DFS. To evaluate the additional value of the radiomics signature for DFS prediction, a radiomics nomogram combining the radiomics signature and clinicopathological predictors was constructed and assessed in terms of discrimination, calibration, reclassification, and clinical usefulness.ResultsThe radiomics signature was significantly associated with DFS, independent of the clinicopathological predictors. The radiomics nomogram performed better than the clinicopathological nomogram (C-index, 0.796 vs. 0.761) and provided better calibration and positive net reclassification improvement (0.147, P = 0.035) in the validation cohort. Decision curve analysis also demonstrated that the radiomics nomogram was clinically useful.ConclusionUS radiomics signature is a potential imaging biomarker for risk stratification of DFS in invasive breast cancer, and US-based radiomics nomogram improved accuracy of DFS prediction.
Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper, deep convolutional neural networks (DCNN) were employed for breast segmentation and mass detection in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). First, the region of the breasts was segmented from the remaining body parts by building a fully convolutional neural network based on U-Net++. Using the method of deep learning to extract the target area can help to reduce the interference external to the breast. Second, a faster region with convolutional neural network (Faster RCNN) was used for mass detection on segmented breast images. The dataset of DCE-MRI used in this study was obtained from 75 patients, and a 5-fold cross validation method was adopted. The statistical analysis of breast region segmentation was carried out by computing the Dice similarity coefficient (DSC), Jaccard coefficient, and segmentation sensitivity. For validation of breast mass detection, the sensitivity with the number of false positives per case was computed and analyzed. The Dice and Jaccard coefficients and the segmentation sensitivity value for breast region segmentation were 0.951, 0.908, and 0.948, respectively, which were better than those of the original U-Net algorithm, and the average sensitivity for mass detection achieved 0.874 with 3.4 false positives per case.
PURPOSE: To evaluate whether intravoxel incoherent motion (IVIM)–related parameters could be used to differentiate malignant from benign focal liver lesions (FLLs) and to improve diagnostic efficiency. METHODS: Seventy-four patients with 75 lesions, including 51 malignant FLLs and 24 benign FLLs, underwent liver 3.0-T magnetic resonance imaging for routine examination sequences. IVIM diffusion-weighted imaging (DWI) with 11 b values (0-800 s/mm2) was also acquired concurrently. Apparent diffusion coefficient (ADCtotal) and IVIM-derived parameters, such as the pure diffusion coefficient (D), the pseudodiffusion coefficient (D⁎), and the perfusion fraction (f), were calculated and compared between the two groups. A receiver operating characteristic curve analysis was performed to assess their diagnostic value. RESULTS: ADCtotal, D, and f were significantly lower in the malignant group than in the benign group, whereas D⁎ did not show a statistical difference. D had a larger area under the curve value (0.968) and higher sensitivity (92.30%) for differentiation. CONCLUSION: IVIM is a useful method to differentiate malignant and benign FLLs. The D value showed higher efficacy to detect hepatic solid lesions.
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