Studies on deep learning approach in breast lesions detection and cancer diagnosis in mammograms

Breast cancer accounts for the largest proportion of newly diagnosed cancers in women recently. Early diagnosis of breast cancer can improve treatment outcomes and reduce mortality. Mammography is convenient and reliable, which is the most commonly used method for breast cancer screening. However, manual examinations are limited by the cost and experience of radiologists, which introduce a high false positive rate and false examination. Therefore, a high-performance computer-aided diagnosis (CAD) system is significant for lesions detection and cancer diagnosis. Traditional CADs for cancer diagnosis require a large number of features selected manually and remain a high false positive rate. The methods based on deep learning can automatically extract image features through the network, but their performance is limited by the problems of multicenter data biases, the complexity of lesion features, and the high cost of annotations. Therefore, it is necessary to propose a CAD system to improve the ability of lesion detection and cancer diagnosis, which is optimized for the above problems. This thesis aims to utilize deep learning methods to improve the CADs' performance and effectiveness of lesion detection and cancer diagnosis. Starting from the detection of multi-type lesions using deep learning methods based on full consideration of characteristics of mammography, this thesis explores the detection method of microcalcification based on multiscale feature fusion and the detection method of mass based on multi-view enhancing. Then, a classification method based on multi-instance learning is developed, which integrates the detection results from the above methods, to realize the precise lesions detection and cancer diagnosis in mammography. For the detection of microcalcification, a microcalcification detection network named MCDNet is proposed to overcome the problems of multicenter data biases, the low resolution of network inputs, and scale differences between microcalcifications. In MCDNet, Adaptive Image Adjustment mitigates the impact of multicenter biases and maximizes the input effective pixels. Then, the proposed pyramid network with shortcut connections ensures that the feature maps for detection contain more precise localization and classification information about multiscale objects. In the structure, trainable Weighted Feature Fusion is proposed to improve the detection performance of both scale objects by learning the contribution of feature maps in different stages. The experiments show that MCDNet outperforms other methods on robustness and precision. In case the average number of false positives per image is 1, the recall rates of benign and malignant microcalcification are 96.8% and 98.9%, respectively. MCDNet can effectively help radiologists detect microcalcifications in clinical applications. For the detection of breast masses, a weakly supervised multi-view enhancing mass detection network named MVMDNet is proposed to solve the lack of lesion-level labels. MVMDNet can be trained on the image-level labeled dataset and extract the extra localization information by exploring the geometric relation between multi-view mammograms. In Multi-view Enhancing, Spatial Correlation Attention is proposed to extract correspondent location information between different views while Sigmoid Weighted Fusion module fuse diagnostic and auxiliary features to improve the precision of localization. CAM-based Detection module is proposed to provide detections for mass through the classification labels. The results of experiments on both in-house dataset and public dataset, [email protected] and [email protected] (recall rate@average number of false positive per image), demonstrate MVMDNet achieves state-of-art performances among weakly supervised methods and has robust generalization ability to alleviate the multicenter biases. In the study of cancer diagnosis, a breast cancer classification network named CancerDNet based on Multi-instance Learning is proposed. CancerDNet successfully solves the problem that the features of lesions are complex in whole image classification utilizing the lesion detection results from the previous chapters. Whole Case Bag Learning is proposed to combined the features extracted from four-view, which works like a radiologist to realize the classification of each case. Low-capacity Instance Learning and High-capacity Instance Learning successfully integrate the detections of multi-type lesions into the CancerDNet, so that the model can fully consider lesions with complex features in the classification task. CancerDNet achieves the AUC of 0.907 and AUC of 0.925 on the in-house and the public datasets, respectively, which is better than current methods. The results show that CancerDNet achieves a high-performance cancer diagnosis. In the works of the above three parts, this thesis fully considers the characteristics of mammograms and proposes methods based on deep learning for lesions detection and cancer diagnosis. The results of experiments on in-house and public datasets show that the methods proposed in this thesis achieve the state-of-the-art in the microcalcifications detection, masses detection, and the case-level classification of cancer and have a strong ability of multicenter generalization. The results also prove that the methods proposed in this thesis can effectively assist radiologists in making the diagnosis while saving labor costs

Hypoxia-induced miR-497 decreases glioma cell sensitivity to TMZ by inhibiting apoptosis

AbstractUnderstanding the resistance of glioma cells to chemotherapy has been an enormous challenge. In particular, mechanisms by which tumor cells acquire resistance to chemotherapy under hypoxic conditions are not fully understood. In this study, we have found that miR-497 is overexpressed in glioma and that hypoxia can induce the expression of miR-497 at the transcriptional level by binding with the hypoxia response element in the promoter. Ectopic overexpression of miR-497 promotes chemotherapy resistance in glioma cells by targeting PDCD4, a tumor suppressor that is involved in apoptosis. In contrast, the inhibition of miR-497 enhances apoptosis and increases the sensitivity of glioma cells to TMZ. These results suggest that miR-497 is a potential molecular target for glioma therapy

Atorvastatin Improves Inflammatory Response in Atherosclerosis by Upregulating the Expression of GARP

Regulatory T cells play an important role in the progression of atherosclerosis. GARP is a newly biological membrane molecule existed on activated Tregs, which is related to the release of TGF-β. The antiatherosclerosis effects of statins partly depend on their multiple immune modulatory potencies. In this paper, we present that atorvastatin could upregulate the expression of GARP and TGF-β in CD4+ T cells and increase the numbers of CD4+LAP+ and CD4+Foxp3+ regulatory T cells in ApoE−/− mice. Also, we indicate that atorvastatin promotes the aggregation of GARP+ and Foxp3+ cells and secretory of the TGF-β1 in atherosclerotic plaques. Furthermore, we prove that atorvastatin could delay the procession of atherosclerosis and improve the stability of atherosclerotic plaques. Interestingly, we report that inhibition of GARP distinctly inhibits the anti-inflammatory effects of atorvastatin. We conclude that atorvastatin improves the inflammatory response in atherosclerosis partly by upregulating the expression of GARP on regulatory T cells

Heme Oxygenase-1 Restores Impaired GARP+CD4+CD25+ Regulatory T Cells from Patients with Acute Coronary Syndrome by Upregulating LAP and GARP Expression on Activated T Lymphocytes

Background: Accumulating evidence shows that the pathological autoreactive immune response is responsible for plaque rupture and the subsequent onset of acute coronary syndrome (ACS). Naturally occurring CD4+CD25+regulatory T cells (nTregs) are indispensable in suppressing the pathological autoreactive immune response and maintaining immune homeostasis. However, the number and the suppressive function of glycoprotein-A repetitions predominant (GARP) + CD4+ CD25+ activated nTregs were impaired in patients with ACS. Recent evidence suggests that heme oxygenase-1 (HO-1) can regulate the adaptive immune response by promoting the expression of Foxp3. We therefore hypothesized that HO-1 may enhance the function of GARP+ CD4+ CD25+Tregs in patients with ACS and thus regulate immune imbalance. Methods: T lymphocytes were isolated from healthy volunteers (control, n=30) and patients with stable angina (SA, n=40) or ACS (n=51). Half of these cells were treated with an HO-1 inducer (hemin) for 48 h, and the other half were incubated with complete RPMI-1640 medium. The frequencies of T-helper 1 (Th1), Th2, Th17 and latency-associated peptide (LAP) +CD4+ T cells and the expression of Foxp3 and GARP by CD4+CD25+T cells were then assessed by measuring flow cytometry after stimulation in vitro. The suppressive function of activated Tregs was measured by thymidine uptake. The levels of transforming growth factor-1 (TGF-β1) in the plasma were measured using enzyme-linked immunosorbent assay (ELISA). The expression levels of the genes encoding these proteins were analyzed by real-time polymerase chain reaction. Results: Patients with ACS exhibited an impaired number and suppressive function of GARP+ CD4+ CD25+Tregs and a mixed Th1/Th17-dominant T cell response when compared with the SA and control groups. The expression of LAP in T cells was also lower in patients with ACS compared to patients with SA and the control individuals. Treatment with an HO-1 inducer enhanced the biological activity of GARP+ CD4+ CD25+Tregs and resulted in increased expression of LAP and GARP by activated T cells. Conclusions: The reduced number and impaired suppressive function of GARP+ CD4+ CD25+Tregs result in excess effector T cell proliferation, leading to plaque instability and the onset of ACS. HO-1 can effectively restore impaired GARP+ CD4+ CD25+Tregs from patients with ACS by promoting LAP and GARP expression on activated T cells