Deep learning-based predictive model for pathological complete response to neoadjuvant chemotherapy in breast cancer from biopsy pathological images: a multicenter study.

Introduction: Early predictive pathological complete response (pCR) is beneficial for optimizing neoadjuvant chemotherapy (NAC) strategies for breast cancer. The hematoxylin and eosin (HE)-stained slices of biopsy tissues contain a large amount of information on tumor epithelial cells and stromal. The fusion of pathological image features and clinicopathological features is expected to build a model to predict pCR of NAC in breast cancer. Methods: We retrospectively collected a total of 440 breast cancer patients from three hospitals who underwent NAC. HE-stained slices of biopsy tissues were scanned to form whole-slide images (WSIs), and pathological images of representative regions of interest (ROI) of each WSI were selected at different magnifications. Based on several different deep learning models, we propose a novel feature extraction method on pathological images with different magnifications. Further, fused with clinicopathological features, a multimodal breast cancer NAC pCR prediction model based on a support vector machine (SVM) classifier was developed and validated with two additional validation cohorts (VCs). Results: Through experimental validation of several different deep learning models, we found that the breast cancer pCR prediction model based on the SVM classifier, which uses the VGG16 model for feature extraction of pathological images at ×20 magnification, has the best prediction efficacy. The area under the curve (AUC) of deep learning pathological model (DPM) were 0.79, 0.73, and 0.71 for TC, VC1, and VC2, respectively, all of which exceeded 0.70. The AUCs of clinical model (CM), a clinical prediction model established by using clinicopathological features, were 0.79 for TC, 0.73 for VC1, and 0.71 for VC2, respectively. The multimodal deep learning clinicopathological model (DPCM) established by fusing pathological images and clinicopathological features improved the AUC of TC from 0.79 to 0.84. The AUC of VC2 improved from 0.71 to 0.78. Conclusion: Our study reveals that pathological images of HE-stained slices of pre-NAC biopsy tissues can be used to build a pCR prediction model. Combining pathological images and clinicopathological features can further enhance the predictive efficacy of the model.

Prospective assessment of breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics.

Introduction: AI-assisted ultrasound diagnosis is considered a fast and accurate new method that can reduce the subjective and experience-dependent nature of handheld ultrasound. In order to meet clinical diagnostic needs better, we first proposed a breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics (hereafter, Auto BI-RADS). In this study, we prospectively verify its performance. Methods: In this study, the model development was based on retrospective data including 480 ultrasound dynamic videos equivalent to 18122 static images of pathologically proven breast lesions from 420 patients. A total of 292 breast lesions ultrasound dynamic videos from the internal and external hospital were prospectively tested by Auto BI-RADS. The performance of Auto BI-RADS was compared with both experienced and junior radiologists using the DeLong method, Kappa test, and McNemar test. Results: The Auto BI-RADS achieved an accuracy, sensitivity, and specificity of 0.87, 0.93, and 0.81, respectively. The consistency of the BI-RADS category between Auto BI-RADS and the experienced group (Kappa:0.82) was higher than that of the juniors (Kappa:0.60). The consistency rates between Auto BI-RADS and the experienced group were higher than those between Auto BI-RADS and the junior group for shape (93% vs. 80%; P = .01), orientation (90% vs. 84%; P = .02), margin (84% vs. 71%; P = .01), echo pattern (69% vs. 56%; P = .001) and posterior features (76% vs. 71%; P = .0046), While the difference of calcification was not significantly different. Discussion: In this study, we aimed to prospectively verify a novel AI tool based on ultrasound dynamic videos and ACR BI-RADS characteristics. The prospective assessment suggested that the AI tool not only meets the clinical needs better but also reaches the diagnostic efficiency of experienced radiologists.

Enhanced high-temperature electrochemical properties of in situ carbon-coated truncated octahedral LiMn2O4 cathodes.

Truncated octahedral spinel LiMn2O4 was homogenously coated by amorphous carbon layer via chemical vapor deposition (CVD) using acetylene gas (C2H2) as carbon source to ease Mn dissolution to improve high-temperature performance, delivering a capacity retention of 92.9% after 1000 cycles at 5C at 50 °C.

Steric-hindrance effect and self-sacrificing template behavior induced PDA@SnO2-QDs/N-doped carbon hollow nanospheres: Enhanced structural stability and reaction kinetics for long-cyclic Li-ion half/full batteries.

Tin-based anode materials with high theoretical specific capacity are subject to huge volume expansion and poor reaction reversibility, leading to degradation of battery performance. Herein, the steric-hindrance effect and self-sacrificing template behavior of polydopamine were firstly developed to induce the formation of hollow nanospheres assembled by ultrafine SnO2 quantum dots (SnO2-QDs) and nitrogen-doped carbon (NC), containing residual polydopamine (PDA) cores. The PDA@SnO2-QDs/NC hollow nanospheres could effectively accommodate the volume expansion and maintain structural stability. More importantly, the PDA core could capture oxygen free radicals produced by the charge/discharge process and be involved in the evolution of the SEI layer, achieving enhanced electrochemical reaction kinetics. The optimized PDA@SnO2-QDs/NC anode shows a specific capacity of 898 mAh g-1 after 300 cycles at 0.3 A g-1, and scarcely capacity attenuation after 1500 cycles at 1 A g-1. The long-cyclic life is up to 3000 cycles at 3 A g-1. Even after 200 cycles, the anode in the PDA@SnO2-QDs/NC||LFP full battery gives a reversible capacity of 489 mAh g-1 at 0.3 A g-1, with a capacity retention of 77 %. This work casts new light on tin-based anode materials and interface optimization.

Copper Substitution in P2-Type Sodium Layered Oxide To Mitigate Phase Transition and Enhance Cyclability of Sodium-Ion Batteries.

Development of high-performance cathode materials is one of the key challenges in the practical application of sodium-ion batteries. Among all the cathode materials, layered sodium transition-metal oxides are particularly attractive. However, undesired phase transitions are often reported and have detrimental effects on the structure stability and electrochemical performance. Cu substitution of zinc in the P2-type Na0.6Mn0.7Ni0.15Zn0.15-xCuxO2 (x = 0, 0.075, and 0.15) composites was investigated in this study for mitigating the biphase transition and enhancing the electrochemical performance of sodium-ion batteries. The coupling effect of Zn and Cu enables an excellent capacity retention of 96.4% of the initial discharge capacity after 150 cycles at 0.1 C in the Na/Na0.6Mn0.7Ni0.15Zn0.075Cu0.075O2 cell. The biphase transition that occurred in the high voltage range has been significantly suppressed after the incorporation of Cu in Na0.6Mn0.7Ni0.15Zn0.15O2, which was confirmed by in situ X-ray diffraction studies. Moreover, the substitution of the inert element Zn with electrochemically active Cu leads to the suppression of anionic redox and the occurrence of Cu2+/3+ redox reaction, and the electrolyte decomposition is impeded after the introduction of electrochemically active Cu in the Na0.6Mn0.7Ni0.15Zn0.15-xCuxO2 composite cathode. The enhanced electrochemical performance in the Na0.6Mn0.7Ni0.15Zn0.075Cu0.075O2 electrode can be ascribed to the coexistence of Zn and Cu and alleviated volumetric change as well as suppressed electrode/electrolyte side reaction after Cu substitution.

Improved ECG-Derived Respiration Using Empirical Wavelet Transform and Kernel Principal Component Analysis.

Many methods have been developed to derive respiration signals from electrocardiograms (ECGs). However, traditional methods have two main issues: (1) focusing on certain specific morphological characteristics and (2) not considering the nonlinear relationship between ECGs and respiration. In this paper, an improved ECG-derived respiration (EDR) based on empirical wavelet transform (EWT) and kernel principal component analysis (KPCA) is proposed. To tackle the first problem, EWT is introduced to decompose the ECG signal to extract the low-frequency part. To tackle the second issue, KPCA and preimaging are introduced to capture the nonlinear relationship between ECGs and respiration. The parameter selection of the radial basis function kernel in KPCA is also improved, ensuring accuracy and a reduction in computational cost. The correlation coefficient and amplitude square coherence coefficient are used as metrics to carry out quantitative and qualitative comparisons with three traditional EDR algorithms. The results show that the proposed method performs better than the traditional EDR algorithms in obtaining single-lead-EDR signals.

Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion.

BACKGROUND AND OBJECTIVE: Breast cancer is a fatal threat to the health of women. Ultrasonography is a common method for the detection of breast cancer. Computer-aided diagnosis of breast ultrasound images can help doctors in diagnosing benign and malignant lesions. In this paper, by combining image decomposition and fusion techniques with adaptive spatial feature fusion technology, a reliable classification method for breast ultrasound images of tumors is proposed. METHODS: First, fuzzy enhancement and bilateral filtering algorithms are used to process the original breast ultrasound image. Then, various decomposition images representing the clinical characteristics of breast tumors are obtained using the original and mask images. By considering the diversity of the benign and malignant characteristic information represented by each decomposition image, the decomposition images are fused through the RGB channel, and three types of fusion images are generated. Then, from a series of candidate deep learning models, transfer learning is used to select the best model as the base model to extract deep learning features. Finally, while training the classification network, adaptive spatial feature fusion technology is used to train the weight network to complete deep learning feature fusion and classification. RESULTS: In this study, 1328 breast ultrasound images were collected for training and testing. The experimental results show that the values of accuracy, precision, specificity, sensitivity/recall, F1 score, and area under the curve of the proposed method were 0.9548, 0.9811, 0.9833, 0.9392, 0.9571, and 0.9883, respectively. CONCLUSION: Our research can automate breast cancer detection and has strong clinical utility. When compared to previous methods, our proposed method is expected to be more effective while assisting doctors in diagnosing breast ultrasound images.

Multi-Features-Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine.

Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors.

Automatic Segmentation of Left Ventricle in Echocardiography Based on YOLOv3 Model to Achieve Constraint and Positioning.

Cardiovascular disease (CVD) is the most common type of disease and has a high fatality rate in humans. Early diagnosis is critical for the prognosis of CVD. Before using myocardial tissue strain, strain rate, and other indicators to evaluate and analyze cardiac function, accurate segmentation of the left ventricle (LV) endocardium is vital for ensuring the accuracy of subsequent diagnosis. For accurate segmentation of the LV endocardium, this paper proposes the extraction of the LV region features based on the YOLOv3 model to locate the positions of the apex and bottom of the LV, as well as that of the LV region; thereafter, the subimages of the LV can be obtained, and based on the Markov random field (MRF) model, preliminary identification and binarization of the myocardium of the LV subimages can be realized. Finally, under the constraints of the three aforementioned positions of the LV, precise segmentation and extraction of the LV endocardium can be achieved using nonlinear least-squares curve fitting and edge approximation. The experiments show that the proposed segmentation evaluation indices of the method, including computation speed (fps), Dice, mean absolute distance (MAD), and Hausdorff distance (HD), can reach 2.1-2.25 fps, 93.57 ± 1.97%, 2.57 ± 0.89 mm, and 6.68 ± 1.78 mm, respectively. This indicates that the suggested method has better segmentation accuracy and robustness than existing techniques.

Tumor classification in automated breast ultrasound (ABUS) based on a modified extracting feature network.

People can get consistent Automated Breast Ultrasound (ABUS) images due to the imaging mechanism of scanning. Therefore, it has unique advantages in breast tumor classification using artificial intelligence technology. This paper proposes a method for classifying benign and malignant breast tumors using ABUS sequence based on deep learning. First, Images of Interest (IOI) will be extracted and Region of Interest (ROI) will be cropped in ABUS sequence by two preprocessing deep learning models, Extracting-IOI model and Cropping-ROI model. Then, we propose a Shallowly Dilated Convolutional Branch Network (SDCB-Net). We combine this network with the VGG16 transfer learning network to construct a brand-new Shared Extracting Feature Network (SEF-Net) to extract ROI sequence features. Finally, the correlation features of ABUS images are extracted and integrated by using GRU Classified Network (GRUC-Net) to achieve the accurate breast tumors classification. The final results show that the accuracy of the test set for classifying benign and malignant ABUS sequence is 92.86 %. This method not only has high accuracy but also greatly improves the speed and efficiency of breast tumor classification. It has high clinical application significance that more women can discover breast tumors timely.

Automatic segmentation for ultrasound image of carotid intimal-media based on improved superpixel generation algorithm and fractal theory.

OBJECTIVE: Carotid atherosclerosis (CAS) is the main reason leading to cardiovascular conditions such as coronary heart disease and cerebrovascular diseases. In the carotid ultrasound images, the carotid intima-media structure can be observed in an annular narrow strip, which its inner contour corresponds to the carotid intima, and the outer contour corresponds to the carotid extima. With the development of carotid atherosclerosis, the carotid intima-media will gradually thicken. Therefore, doctors can observe the carotid intima-media so as to obtain the pathological changes of the internal structure of the patient's carotid arteries. However, due to the presence of artifacts and noises the quality of the ultrasound images are degraded, making it difficult to obtain accurate carotid intima-media structures. This article presents a novel self-adaptive method to enable obtaining the carotid intima-media through carotid intima/extima segmentation. METHOD: After preprocessing the ultrasound images by homomorphic filtering and median filtering, we propose an improved superpixel generation algorithm that employs the fusion of gray-level and luminosity-based information to decompose the image into numerous superpixels and later presents the carotid intima. Meanwhile, based on the features of the carotid artery, the initial position of the carotid extima is located by the normalized cut algorithm and later the fractal theory is employed to segment the carotid extima. RESULTS: The proposed method for segmenting carotid intima obtained mean values of the DICE true positive ratio (TPR), false positive ratio (FPR), precision scores of 97.797%, 99.126%, 0.540%, 97.202%, respectively. Further from the segmentation method of the carotid extima the performance measures such as mean DICE, TPR, accuracy, F-score obtained are 95.00%, 92.265%, 97.689%, 94.997%, respectively. CONCLUSION: Comparing with traditional methods, the proposed method performed better. The experimental results indicated that the proposed method obtained the carotid intima-media both automatically and accurately thus effectively assist doctors in the diagnosis of CAS.

Effect of pyrolysis condition on the adsorption mechanism of heavy metals on tobacco stem biochar in competitive mode.

To clarify the adsorption mechanism of multi-ions on biochars in competitive environment is very important for the decontamination of co-existed heavy metals. Herein, tobacco stem was pyrolyzed in different temperatures with selected residences to obtain biochars with various surface chemistry. Then the adsorption of co-existed typical heavy-metal ions like lead, cadmium, and copper was studied, followed with systematic analysis of surface properties of the post-adsorption biochars. After carefully examining the adsorption performance and surface property alteration of the demineralized biochars, the adsorption mechanism of multi-ions in competitive environment was discovered. Lead showed the most competitive nature with co-existence of cadmium and copper, but the adsorption capacity reduced significantly with the removal of minerals. Combined with the observation of large amount of lead containing crystals on the post-adsorption biochars, the main adsorption mechanism of lead should be precipitation. The adsorb capability of copper barely changed for biochars with and without minerals, which suggests the best affinity of copper on surface functional groups even with large content of competitors. Biochar that pyrolyzed in 700 °C for 6 h that contained more aromatic structures showed the highest sorbing capability of cadmium, which suggested the dominant position of cation-π interaction in cadmium removal.

The effects of graphene quantum dots on the maturation of mouse oocytes and development of offspring.

Recently, graphene nanomaterials have attracted tremendous attention and have been utilized in various fields because of their excellent mechanical, thermal, chemical, optical properties, and good biocompatibility, especially in biomedical aspects. However, there is a concern that the unique characteristics of nanomaterials may have undesirable effects. Therefore, in this study, we sought to systematically investigate the effects of graphene quantum dots (GQDs) on the maturation of mouse oocytes and development of the offspring via in vitro and in vivo studies. In vitro, we found that the first polar body extrusion rate in the high dosage exposure groups (1.0-1.5 mg/ml) 2 decreased significantly and the failure of spindle migration and actin cap formation after GQDs exposure was observed. The underlying mechanisms might be associated with reactive oxygen species accumulation and DNA damage. Moreover, transmission electron microscope studies showed that GQDs may have been internalized into oocytes, tending to accumulate in the nucleus and severely affecting mitochondrial morphology, which included swollen and vacuolated mitochondria accompanied by cristae alteration with a lower amount of dense mitochondrial matrix. In vivo, when pregnant mice were exposed to GQDs at 8.5 days of gestation (GD, 8.5), we found that high dosage of GQD exposure (30 mg/kg) significantly affected mean fetal length; however, all the second generation of female mice grew up normal, attained sexual maturity, and gave birth to a healthy offspring after mating with a healthy male mouse. The results presented in this study are important for the future investigation of GQDs for the biomedical applications.