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In clinical practice, we noticed that triple negative breast cancer (TNBC) patients had higher shear-wave elasticity (SWE) stiffness than non-TNBC patients and a higher α-SMA expression was found in TNBC tissues than the non-TNBC tissues. Moreover, SWE stiffness also shows a clear correlation to neoadjuvant response efficiency. To elaborate this phenomenon, TNBC cell membrane-modified polylactide acid-glycolic acid (PLGA) nanoparticle was fabricated to specifically deliver artesunate to regulate SWE stiffness through inhibiting CAFs functional status. As tested in MDA-MB-231 and E0771 orthotopic tumor models, CAFs functional status inhibited by 231M-ARS@PLGA nanoparticles (231M-AP NPs) had reduced the SWE stiffness as well as attenuated hypoxia of tumor as tumor soil loosening agent which amplified the antitumor effects of paclitaxel and PD1 inhibitor. Single-cell sequencing indicated that the two main CAFs (extracellular matrix and wound healing CAFs) that produces extracellular matrix could influence the tumor SWE stiffness as well as the antitumor effect of drugs. Further, biomimetic nanoparticles inhibited CAFs function could attenuate tumor hypoxia by increasing proportion of inflammatory blood vessels and oxygen transport capacity. Therefore, our finding is fundamental for understanding the role of CAFs on affecting SWE stiffness and drugs antitumor effects, which can be further implied in the potential clinical theranostic predicting in neoadjuvant therapy efficacy through non-invasive analyzing of SWE imaging.

To investigate the occult contralateral papillary thyroid carcinoma (PTC)-associated ultrasound (US) and clinical characteristics and establish a US-based model for the prediction of occult contralateral carcinoma in adolescents and young adults (AYAs) who were diagnosed with unilateral thyroid carcinoma preoperatively.From January 2015 to December 2020, patients who were diagnosed with unilateral thyroid carcinoma by preoperative US examination and underwent total thyroidectomy or thyroid lobectomy with more than 60 months of US follow-up at our hospital were retrospectively collected. Univariate and multivariate analyses were applied to identify the independent risk factors associated with occult contralateral PTC in AYAs, on which a prediction model was developed. The performance of the model was evaluated with accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve.Occult contralateral PTC was found in 91 of 365 (24.9%) PTC patients with a median age at diagnosis of 26 years (interquartile range, 24-29 years). The multivariate analysis indicated that the presence of contralateral benign nodule, intra-tumoral calcification, and intraglandular dissemination were significantly associated with occult contralateral PTC in AYAs. The prediction model, which incorporated all independent predictors, yielded an area under the receiver operating characteristic curve of .661 (95% CI: .602-.719). The accuracy, sensitivity and specificity were 67.9%, 54.9%, and 72.3%, respectively.The US-based prediction model proposed here exhibited a favorable performance for predicting occult contralateral PTC, which might be used to determine the appropriate extent of surgery for AYAs who had a preoperative diagnosis of unilateral thyroid carcinoma.

To establish a breast lesion risk stratification system using ultrasound images to predict breast malignancy and assess Breast Imaging Reporting and Data System (BI-RADS) categories simultaneously.This multicenter study prospectively collected a dataset of ultrasound images for 5012 patients at thirty-two hospitals from December 2018 to December 2020. A deep learning (DL) model was developed to conduct binary categorization (benign and malignant) and BI-RADS categories (2, 3, 4a, 4b, 4c, and 5) simultaneously. The training set of 4212 patients and the internal test set of 416 patients were from thirty hospitals. The remaining two hospitals with 384 patients were used as an external test set. Three experienced radiologists performed a reader study on 324 patients randomly selected from the test sets. We compared the performance of the DL model with that of three radiologists and the consensus of the three radiologists.In the external test set, the DL model achieved areas under the receiver operating characteristic curve (AUCs) of 0.980 and 0.945 for the binary categorization and six-way categorizations, respectively. In the reader study set, the DL BI-RADS categories achieved a similar AUC (0.901 vs. 0.933, p = 0.0632), sensitivity (90.98% vs. 95.90%, p = 0.1094), and accuracy (83.33% vs. 79.01%, p = 0.0541), but higher specificity (78.71% vs. 68.81%, p = 0.0012) than those of the consensus of the three radiologists.The DL model performed well in distinguishing benign from malignant breast lesions and yielded outcomes similar to experienced radiologists. This indicates the potential applicability of the DL model in clinical diagnosis.• The DL model can achieve binary categorization for benign and malignant breast lesions and six-way BI-RADS categorizations for categories 2, 3, 4a, 4b, 4c, and 5, simultaneously. • The DL model showed acceptable agreement with radiologists for the classification of breast lesions. • The DL model performed well in distinguishing benign from malignant breast lesions and had promise in helping reduce unnecessary biopsies of BI-RADS 4a lesions.

Convolutional neural networks (CNNs) have been successfully applied in the computer-aided ultrasound diagnosis for breast cancer. Up to now, several CNN-based methods have been proposed. However, most of them consider tumor localization and classification as two separate steps, rather than performing them simultaneously. Besides, they suffer from the limited diagnosis information in the B-mode ultrasound (BUS) images. In this study, we develop a novel network ResNet-GAP that incorporates both localization and classification into a unified procedure. To enhance the performance of ResNet-GAP, we leverage stiffness information in the elastography ultrasound (EUS) modality by collaborative learning in the training stage. Specifically, a dual-channel ResNet-GAP network is developed, one channel for BUS and the other for EUS. In each channel, multiple class activity maps (CAMs) are generated using a series of convolutional kernels of different sizes. The multi-scale consistency of the CAMs in both channels are further considered in network optimization. Experiments on 264 patients in this study show that the newly developed ResNet-GAP achieves an accuracy of 88.6%, a sensitivity of 95.3%, a specificity of 84.6%, and an AUC of 93.6% on the classification task, and a 1.0NLF of 87.9% on the localization task, which is better than some state-of-the-art approaches.

The Kähler potentials of modular symmetry models receive unsuppressed contributions which may be controlled by a flavor symmetry, where the combination of the two symmetry types is referred to as eclectic flavor symmetry. After briefly reviewing the consistency conditions of eclectic flavor symmetry models, including those with generalised (g)CP, we perform a comprehensive bottom-up study of eclectic flavor symmetry models based on Ω(1) ≅ ∆(27) ⋊ T′, consisting of the flavor symmetry ∆(27) in a semi-direct product with the modular symmetry T′. The modular transformations of different ∆(27) multiplets are given by solving the consistency condition. The eight nontrivial singlets of ∆(27) are related by T′ modular symmetry, and they have to be present or absent simultaneously in any Ω(1) model. The most general forms of the superpotential and Kähler potential invariant under Ω(1) are discussed, and the corresponding fermion mass matrices are presented. Based on the eclectic flavor group Ω(1), two concrete lepton models which can successfully describe the experimental data of lepton masses and mixing parameters are constructed. For the two models without gCP, all six mixing parameters vary in small regions. A nearly maximal atmospheric mixing angle θ23 and Dirac CP phase δCP are obtained in the first model. After considering the compatible gCP symmetry and the assumption of $$ \mathfrak{R}\tau $$ R τ = 0 in the first model, the μ − τ reflection symmetry is preserved in the charged lepton diagonal basis. As a consequence, the atmospheric mixing angle and Dirac CP phase are predicted to be maximal, and two Majorana CP phases are predicted to be π.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and ultrasound (US), which are two common modalities for clinical breast tumor diagnosis besides Mammograms, can provide different and complementary information for the same tumor regions. Although many machine learning methods have been proposed for breast tumor classification based on either single modality, it remains unclear how to further boost the classification performance by utilizing paired multi-modality information with different dimensions. In this paper, we propose MRI-US multi-modality network (MUM-Net) to classify breast tumor into different subtypes based on 3D MR and 2D US images. The key insight of MUM-Net is that we explicitly distill modality-agnostic features for tumor classification. Specifically, we first adopt a discrimination-adaption module to decompose features into modality-agnostic and modality-specific ones with min-max training strategies. Then, we propose a feature fusion module to increase the compactness of the modality-agnostic features by utilizing an affinity matrix with nearest neighbour selection. We build a paired MRI-US breast tumor classification dataset containing 502 cases with three clinical indicators to validate the proposed method. In three tasks including lymph node metastasis, histological grade and Ki-67 level, MUM-Net achieves AUC scores of 0.8581, 0.8965 and 0.8577, outperforming other counterparts which are based on single task or single modality by a wide margin. In addition, we find that the extracted modality-agnostic features can help the network focus on the tumor regions in both modalities.

Objective This study aimed to incorporate clinicopathological, sonographic, and mammographic characteristics to construct and validate a nomogram model for predicting disease-free survival (DFS) in patients with triple-negative breast cancer (TNBC). Methods Patients diagnosed with TNBC at our institution between 2011 and 2015 were retrospectively evaluated. A nomogram model was generated based on clinicopathological, sonographic, and mammographic variables that were associated with 1-, 3-, and 5-year DFS determined by multivariate logistic regression analysis in the training set. The nomogram model was validated according to the concordance index (C-index) and calibration curves in the validation set. Results A total of 636 TNBC patients were enrolled and divided into training cohort (n = 446) and validation cohort (n = 190). Clinical factors including tumor size > 2 cm, axillary dissection, presence of LVI, and sonographic features such as angular/spiculated margins, posterior acoustic shadows, and presence of suspicious lymph nodes on preoperative US showed a tendency towards worse DFS. The multivariate analysis showed that no adjuvant chemotherapy (HR = 6.7, 95% CI: 2.6, 17.5, p < 0.0005), higher axillary tumor burden (HR = 2.7, 95% CI: 1.0, 7.1, p = 0.045), and ≥ 3 malignant features on ultrasound (HR = 2.4, CI: 1.1, 5.0, p = 0.021) were identified as independent prognostic factors associated with poorer DFS outcomes. In the nomogram, the C-index was 0.693 for the training cohort and 0.694 for the validation cohort. The calibration plots also exhibited excellent consistency between the nomogram-predicted and actual survival probabilities in both the training and validation cohorts. Conclusions Clinical variables and sonographic features were correlated with the prognosis of TNBCs. The nomogram model based on three variables including no adjuvant chemotherapy, higher axillary tumor load, and more malignant sonographic features showed good predictive performance for poor survival outcomes of TNBC. Key Points • The absence of adjuvant chemotherapy, heavy axillary tumor load, and malignant-like sonographic features can predict DFS in patients with TNBC. • Mammographic features of TNBC could not predict the survival outcomes of patients with TNBC. • The nomogram integrating clinicopathological and sonographic characteristics is a reliable predictive model for the prognostic outcome of TNBC.

PurposeThe detection of human epidermal growth factor receptor 2 (HER2) expression status is essential to determining the chemotherapy regimen for breast cancer patients and to improving their prognosis. We developed a deep learning radiomics (DLR) model combining time-frequency domain features of ultrasound (US) video of breast lesions with clinical parameters for predicting HER2 expression status.Patients and MethodsData for this research was obtained from 807 breast cancer patients who visited from February 2019 to July 2020. Ultimately, 445 patients were included in the study. Pre-operative breast ultrasound examination videos were collected and split into a training set and a test set. Building a training set of DLR models combining time-frequency domain features and clinical features of ultrasound video of breast lesions based on the training set data to predict HER2 expression status. Test the performance of the model using test set data. The final models integrated with different classifiers are compared, and the best performing model is finally selected.ResultsThe best diagnostic performance in predicting HER2 expression status is provided by an Extreme Gradient Boosting (XGBoost)-based time-frequency domain feature classifier combined with a logistic regression (LR)-based clinical parameter classifier of clinical parameters combined DLR, particularly with a high specificity of 0.917. The area under the receiver operating characteristic curve (AUC) for the test cohort was 0.810.ConclusionOur study provides a non-invasive imaging biomarker to predict HER2 expression status in breast cancer patients.

Acute lung injury (ALI) is a life-threatening disease with high incidence and mortality rates. Urolithin A (UA) is a pomegranate intestinal flora metabolite with anti-inflammatory, antioxidant, and anti-aging properties. Ferroptosis is a critical factor in lipopolysaccharide (LPS)-induced acute lung injury (ALI). However, the link between UA and ferroptosis is unknown. The purpose of this research was to look into the role of UA in regulating LPS-induced ferroptosis in ALI. The current study used LPS to injure two models, one BEAS-2B cell injury model and one ALI mouse model. UA effectively alleviated LPS-induced ALI compared to the LPS group by lowering in vivo lung wet/dry weight ratio, reactive oxygen species, and malondialdehyde production, as well as superoxide dismutase, catalase, and glutathione depletion. Furthermore, by increasing GPX4 and SLC7A11 expression and decreasing Fe2+ levels, lung histopathological damage, inflammatory cytokine secretion, and ferroptosis levels can be significantly reduced. The Keap1-Nrf2/HO-1 pathway was upregulated by UA, which inhibited LPS-induced ALI and ferroptosis. ML385 inhibited UA’s protective effect against LPS-induced ALI. These findings suggested that UA could be a novel potential therapeutic target for ALI.

Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing a deep neural network to synthesize virtual EUS (V-EUS) from conventional B-mode images. A total of 4580 breast tumor cases were collected from 15 medical centers, including a main cohort with 2501 cases for model establishment, an external dataset with 1730 cases and a portable dataset with 349 cases for testing. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5% after V-EUS is equipped.

The sonographic appearance of benign and malignant breast nodules overlaps to some extent, and we aimed to assess the performance of the Gail model as an adjunctive tool to ultrasound (US) Breast Imaging Reporting and Data System (BI-RADS) for predicting the malignancy of nodules.From 2018 to 2019, 2607 patients were prospectively enrolled by 35 health care facilities. An individual breast cancer risk was assessed by the Gail model. Based on B-mode US, color Doppler, and elastography, all nodules were evaluated according to the fifth edition of BI-RADS, and these nodules were all confirmed later by pathology.We demonstrated that the Gail model, age, tumor size, tumor shape, growth orientation, margin, contour, acoustic shadowing, microcalcification, presence of duct ectasia, presence of architectural distortion, color Doppler flow, BI-RADS, and elastography score were significantly related to breast cancer (all p0.001). The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the curve (AUC) for combining the Gail model with the BI-RADS category were 95.6%, 91.3%, 85.0%, 97.6%, 92.8%, and 0.98, respectively. Combining the Gail model with the BI-RADS showed better diagnostic efficiency than the BI-RADS and Gail model alone (AUC 0.98 vs 0.80, p0.001; AUC 0.98 vs 0.55, p0.001) and demonstrated a higher specificity than the BI-RADS (91.3% vs 59.4%, p0.001).The Gail model could be used to differentiate malignant and benign breast lesions. Combined with the BI-RADS category, the Gail model was adjunctive to US for predicting breast lesions for malignancy. For the diagnosis of malignancy, more attention should be paid to high-risk patients with breast lesions.

Additional file 1: Figure S1. Alignment of ColKP3-type plasmid.

The Kähler potentials of modular symmetry models receive unsuppressed contributions which may be controlled by a flavor symmetry, where the combination of the two symmetry types is referred to as eclectic flavor symmetry. After briefly reviewing the consistency conditions of eclectic flavor symmetry models, including with generalised (g)CP, we perform a comprehensive bottom-up study of eclectic flavor symmetry models based on $Ω(1)\cong Δ(27)\rtimes T^\prime$, consisting of the flavor symmetry $Δ(27)$ in a semi-direct product with the modular symmetry $T^\prime$. The modular transformations of different $Δ(27)$ multiplets are given by solving the consistency condition. The eight nontrivial singlets of $Δ(27)$ are related by $T'$ modular symmetry, and they have to be present or absent simultaneously in any $Ω(1)$ model. The most general forms of the superpotential and Kähler potential invariant under $Ω(1)$ are discussed, and the corresponding fermion mass matrices are presented. Based on the eclectic flavor group $Ω(1)$, two concrete lepton models which can successfully describe the experimental data of lepton masses and mixing parameters are constructed. For the two models without gCP, all six mixing parameters vary in small regions. A nearly maximal atmospheric mixing angle $θ_{23}$ and Dirac CP phase $δ_{CP}$ are obtained in the first model. After considering the compatible gCP symmetry and the assumption of $\Re τ=0$ in the first model, the $μ-τ$ reflection symmetry is preserved in the charged lepton diagonal basis. As a consequence, the atmospheric mixing angle and Dirac CP phase are predicted to be maximal, and two Majorana CP phases are predicted to be $π$., 47 pages, 2 figures

Additional file 4: Table S3. Antimicrobial susceptibility profiles of OXA-48-like-producing strains and transconjugants.

Additional file 2: Table S1. Sample Collection information in different regions of Zhejiang Province.

Sonographic features are associated with pathological and immunohistochemical characteristics of triple-negative breast cancer (TNBC). To predict the biological property of TNBC, the performance using quantitative high-throughput sonographic feature analysis was compared with that using qualitative feature assessment. We retrospectively reviewed ultrasound images, clinical, pathological, and immunohistochemical (IHC) data of 252 female TNBC patients. All patients were subgrouped according to the histological grade, Ki67 expression level, and human epidermal growth factor receptor 2 (HER2) score. Qualitative sonographic feature assessment included shape, margin, posterior acoustic pattern, and calcification referring to the Breast Imaging Reporting and Data System (BI-RADS). Quantitative sonographic features were acquired based on the computer-aided radiomics analysis. Breast cancer masses were manually segmented from the surrounding breast tissues. For each ultrasound image, 1688 radiomics features of 7 feature classes were extracted. The principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were used to determine the high-throughput radiomics features that were highly correlated to biological properties. The performance using both quantitative and qualitative sonographic features to predict biological properties of TNBC was represented by the area under the receiver operating characteristic curve (AUC). In the qualitative assessment, regular tumor shape, no angular or spiculated margin, posterior acoustic enhancement, and no calcification were used as the independent sonographic features for TNBC. Using the combination of these four features to predict the histological grade, Ki67, HER2, axillary lymph node metastasis (ALNM), and lymphovascular invasion (LVI), the AUC was 0.673, 0.680, 0.651, 0.587, and 0.566, respectively. The number of high-throughput features that closely correlated with biological properties was 34 for histological grade (AUC 0.942), 27 for Ki67 (AUC 0.732), 25 for HER2 (AUC 0.730), 34 for ALNM (AUC 0.804), and 34 for LVI (AUC 0.795). High-throughput quantitative sonographic features are superior to traditional qualitative ultrasound features in predicting the biological behavior of TNBC. • Sonographic appearances of TNBCs showed a great variety in accordance with its biological and clinical characteristics. • Both qualitative and quantitative sonographic features of TNBCs are associated with tumor biological characteristics. • The quantitative high-throughput feature analysis is superior to two-dimensional sonographic feature assessment in predicting tumor biological property.

To develop and validate a nomogram incorporating clinical and ultrasound (US) characteristics for predicting the pathological nodal negativity of unilateral clinically N1a (cN1a) papillary thyroid carcinoma (PTC) among adolescents and young adults.From December 2016 to August 2021, 278 patients aged ≤ 30 years from two medical centers were enrolled and randomly assigned to the training and validation cohorts at a ratio of 2:1. After performing univariate and multivariate analyses, a nomogram combining all independent predictive factors was constructed and applied to the validation cohort. The performance of the nomogram was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis .Multivariate logistic regression analysis showed that unilateral cN1a PTC in young patients with Hashimoto's thyroiditis, T1 stage, no intra-tumoral microcalcification, and tumors located in the upper third of the thyroid gland was more likely to be free of central lymph node metastases. The nomogram revealed good calibration and discrimination in both cohorts, with areas under the receiver operating characteristic curve of 0.764 (95% confidence interval [CI]: 0.684-0.843) and 0.728 (95% CI: 0.602-0.853) in the training and validation cohorts, respectively. The clinical application of the nomogram was further confirmed using decision curve analysis.This US-based nomogram may assist the assessment of central cervical lymph nodes in young patients with unilateral cN1a PTC, enabling improved risk stratification and optimal treatment management in clinical practice.

Despite of the great progress that China in healthcare has made since the 2009 healthcare reform, China is still facing high and increasing burden from non-communicable diseases (NCDs), and there still are many major challenges in China’s primary healthcare (PHC) system, including the lack of effective incentives to attract and retain PHC workers, limited infrastructure, low quality of public healthcare services, and substantial regional disparities. Further policy enhancements are needed to address the remaining and emerging challenges in China’s PHC system, and a comprehensive review of the existing national policies on PHC strengthening and NCD control is warranted to inform future policy-making. The present study aims to bridge the above knowledge gap by conducting a scoping review of policies released by China’s central government that targeted NCD control at the PHC level since China’s 2009 Healthcare Reform. There are three objectives for the study: (1) to characterize the related policies by China’s central government and to identify general patterns in the related policy making process; (2) to explore key areas of China’s health reform from the PHC systems’ perspective and to identify to what extend the current policies addressed the combined framework of WHO six building blocks and five strategies for integrated care; and (3) to identify underlying actions and strategies of China’s health reform in PHC strengthening and NCD control and to determine gaps in existing policies.

Precise nodule segmentation in thyroid ultrasound images is important for clinical quantitative analysis and diagnosis. Fully supervised deep learning method can effectively extract representative features from nodules and background. Despite the great success, deep learning–based segmentation methods still face a critical hindrance: the difficulty in acquiring sufficient training data due to high annotation costs. To this end, we propose a weakly supervised framework called uncertainty to fine generative adversarial network (U2F-GAN) for nodule segmentation in thyroid ultrasound images that exploits only a handful of rough bounding box annotations to successfully generate reliable labels from these weak supervisions. Based on feature-matching GAN, the proposed method alternates between generating masks and learning a segmentation network in an adversarial manner. Super-pixel processing mechanism is adopted to reflect low-level image structure features for learning and inferring semantic segmentation, which largely improve the efficiency of training process. In addition, we introduce a similarity comparison module and a distributed loss function with constraints to effectively remove noise in localization annotations and enhance the generalization capability of the network, thus strengthen the overall segmentation performance. Compared to existing weakly supervised approaches, our proposed U2F-GAN yields a significant performance boost. The segmentation results are also comparable to fully supervised methods, but the annotation burden is much lower. Also, the training speed of the network model is much faster than other methods with weak supervisions, which enables the network to be updated in time, thus is beneficial to high-throughput medical image setting.

The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT and FA remains a great challenge for radiologists in clinical work. Artificial intelligence (AI)-assisted diagnosis has shown promise in distinguishing PT from FA. However, a very small sample size was adopted in previous studies. In this work, we retrospectively enrolled 656 breast tumors (372 FAs and 284 PTs) with 1945 ultrasound images in total. Two experienced ultrasound physicians independently evaluated the ultrasound images. Meanwhile, three deep-learning models (i.e., ResNet, VGG, and GoogLeNet) were applied to classify FAs and PTs. The robustness of the models was evaluated by fivefold cross validation. The performance of each model was assessed by using the receiver operating characteristic (ROC) curve. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. Among the three models, the ResNet model yielded the highest AUC value, of 0.91, with an accuracy value of 95.3%, a sensitivity value of 96.2%, and a specificity value of 94.7% in the testing data set. In contrast, the two physicians yielded an average AUC value of 0.69, an accuracy value of 70.7%, a sensitivity value of 54.4%, and a specificity value of 53.2%. Our findings indicate that the diagnostic performance of deep learning is better than that of physicians in the distinction of PTs from FAs. This further suggests that AI is a valuable tool for aiding clinical diagnosis, thereby advancing precision therapy.

BACKGROUND: Elucidation the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer is important for informing therapeutic decisions. This study aimed at evaluating the potential value of contrast-enhanced ultrasound (CEUS) parameters in predicting breast cancer responses to NAC. METHODS: We performed CEUS examinations before and after two cycles of NAC. Quantitative CEUS parameters [maximum intensity (IMAX), rise time (RT), time to peak (TTP), and mean transit time (mTT)], tumor diameter, and their changes were measured and compared to histopathological responses, according to the Miller-Payne Grading (MPG) system (score 1, 2, or 3: minor response; score 4 or 5: good response). Prediction models for good response were developed by multiple logistic regression analysis and internally validated through bootstrap analysis. The receiver operating characteristic (ROC) curve was used to evaluate the performance of prediction models. RESULTS: A total of 143 patients were enrolled in this study among whom 98 (68.5%) achieved a good response and while 45 (31.5%) exhibited a minor response. Several imaging variables including diameter, IMAX, changes in diameter (Δdiameter), IMAX (ΔIMAX) and TTP (ΔTTP) were found to be significantly associated with good therapeutic responses (P

Within the context of China’s Urban Employees’ Basic Pension Insurance (UEBPI), this paper constructs an actuarial model to analyze the financial imbalance risk of contribution rate reduction and to investigate the possibility of further reducing the contribution rate. It is found that the UEBPI fund would show financial imbalance risk in 2024 if the contribution rate is 16%, and no control strategy is introduced. In the case of single strategy (the collection system reform, delay of retirement age, or the introduction of external finance), the financial sustainability of the UEBPI fund could be improved to some extent, whereas the financial imbalance risk remains huge. In the case of a package of control strategies being implemented, the UEBPI fund could be able to continue its operation until 2060, and the contribution rate can be further reduced by 0–4 percentage. Therefore, the implementation of a package of control strategies presents a prerequisite for controlling the financial imbalance risk and further reducing the contribution rate.

Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. We therefore present a cost-efficient solution by designing a deep neural network to synthesize augmented reality EUS (AR-EUS) from conventional B-mode images. By using 4580 cases from 15 medical centers, we evaluate the performance of AR-EUS on breast cancer diagnosis. The quantitative metric and blind evaluation results show no significant difference between AR-EUS and real EUS in image authenticity and in clinical diagnosis. The performance of pocket-sized ultrasound in breast tumor diagnosis is also significantly improved after AR-EUS is equipped. These results highlight the potential of AR-EUS in clinical application.

Inspired by the distinct metabolic pathway of tumor cells, here a new anti-cancer therapy (i.e., Cu-TCPP(Fe) metal-organic frameworks (MOFs) with sonosensitizer for sonodynamic therapy (SDT) and tumor microenvironment (TME)-specific release of copper/iron for chemical catalysis) is introduced and verified. Upon exposure to ultrasound, the porphyrin structure in the MOFs yields reactive oxygen species (ROS), and the copper/iron induces the Fenton reaction in the TME existing H2O2 and acid. Thus, highly toxic ROS are generated through these TME-specific reactions to trigger the apoptosis and death of tumor cells. The current work paves a new avenue to design SDT with catalytic nanomedicine for the selective and effective treatment of tumors.

Objective The data regarding the mutation landscape in Chinese patients with thyroid cancer are limited. The diagnostic performance of thyroid nodules by fine-needle aspiration (FNA) cytology needs optimization, especially in indeterminate nodules. Methods A total of 1039 FNA and surgical resection samples tested using the targeted multigene next-generation sequencing (NGS) panel were retrospectively collected. The features of gene alterations in different thyroid tumors were analyzed, and the diagnostic efficacy was evaluated. Results Among 1039 samples, there were 822 FNA and 217 surgical FFPE samples. Among 207 malignant thyroid resections, a total of 181 out of 193 papillary thyroid carcinomas (PTCs) were NGS-positive (93.8%), with a high prevalence of BRAF mutations (81.9%, 158/193) and a low prevalence of RAS (1.0%, 2/193) and TERT promoter mutations (3.6%, 7/193). Gene fusions, involving the RET and NTRK3 genes, were present in 20 PTCs (10.4%) and mutually exclusive with other driver mutations. Two of three follicular thyroid carcinomas harbored multiple mutations. RET gene point mutations were common in medullary thyroid carcinoma (8/11, 72.7%). The combination of cytology and DNA–RNA-based NGS analysis demonstrated superior diagnostic value (98.0%) in FNA samples. For indeterminate thyroid nodules, the diagnostic sensitivity and specificity of NGS testing were 79.2 (38/48) and 80.0% (8/10), respectively. Two mutation-positive benign cases harbored NRAS and TSHR mutations, respectively. Conclusions Our study revealed the distinct molecular profile of thyroid tumors in the Chinese population. The combination of NGS testing and FNA cytology could facilitate the accurate diagnosis of thyroid nodules, especially for indeterminate nodules.

Background An accurate preoperative assessment of cervical lymph node metastasis (LNM) is important for choosing an optimal therapeutic strategy for papillary thyroid carcinoma (PTC) patients. This study aimed to develop and validate two ultrasound (US) nomograms for the individual prediction of central and lateral compartment LNM in patients with PTC. Methods A total of 720 PTC patients from 3 institutions were enrolled in this study. They were categorized into a primary cohort, an internal validation, and two external validation cohorts. Radiomics features were extracted from conventional US images. LASSO regression was used to select optimized features to construct the radiomics signature. Two nomograms integrating independent clinical variables and radiomics signature were established with multivariate logistic regression. The performance of the nomograms was assessed with regard to discrimination, calibration, and clinical usefulness. Results The radiomics scores were significantly higher in patients with central/lateral LNM. A radiomics nomogram indicated good discrimination for central compartment LNM, with an area under the curve (AUC) of 0.875 in the training set, the corresponding value in the validation sets were 0.856, 0.870 and 0.870, respectively. Another nomogram for predicting lateral LNM also demonstrated good performance with an AUC of 0.938 and 0.905 in the training and internal validation cohorts, respectively. The AUC for the two external validation cohorts were 0.881 and 0.903, respectively. The clinical utility of the nomograms was confirmed by the decision curve analysis. Conclusion The nomograms proposed here have favorable performance for preoperatively predicting cervical LNM, hold promise for optimizing the personalized treatment, and might greatly facilitate the decision-making in clinical practice.

Segmentation of the breast ultrasound (BUS) image is an important step for subsequent assessment and diagnosis of breast lesions. Recently, Deep-learning-based methods have achieved satisfactory performance in many computer vision tasks, especially in medical image segmentation. Nevertheless, those methods always require a large number of pixel-wise labeled data that is expensive in medical practices. In this study, we propose a new segmentation method by dense prediction and local fusion of superpixels for breast anatomy with scarce labeled data. First, the proposed method generates superpixels from the BUS image enhanced by histogram equalization, a bilateral filter, and a pyramid mean shift filter. Second, using a convolutional neural network (CNN) and distance metric learning-based classifier, the superpixels are projected onto the embedding space and then classified by calculating the distance between superpixels’ embeddings and the centers of categories. By using superpixels, we can generate a large number of training samples from each BUS image. Therefore, the problem of the scarcity of labeled data can be better solved. To avoid the misclassification of the superpixels, $K$ -nearest neighbor (KNN) is used to reclassify the superpixels within every local region based on the spatial relationships among them. Fivefold cross-validation was taken and the experimental results show that our method outperforms several often used deep-learning methods under the condition of the absence of a large number of labeled data (48 BUS images for training and 12 BUS images for testing).

Purpose To determine the overall performance of contrast-enhanced ultrasound (CEUS) in differentiating between benign and malignant breast lesions and in predicting the pathologic response to neoadjuvant chemotherapy (NAC) in patients with breast cancer (BC). Materials and Methods Articles published up to April 2019 were systematically searched in Medline, Web of Science, and China National Knowledge Infrastructure. The sensitivities and specificities across studies, the calculations of positive and negative likelihood ratios (LR+ and LR−), diagnostic odds ratio (OR), and constructed summary receiver operating characteristic curves were determined. Methodologic quality was assessed using the QUADAS (Quality Assessment of Diagnostic Accuracy Studies) tool. Subgroup analyses and metaregression were performed on prespecified study-level characteristics. Results Fifty-one studies involving 4875 patients with 5246 breast lesions and 10 studies involving 462 patients with BC receiving NAC were included. Methodologic quality was relatively high, and no publication bias was detected. The overall sensitivity, specificity, diagnostic OR, LR+, and LR− for CEUS were 0.88 (95% confidence interval [CI], 0.86-0.89), 0.82 (95% CI, 0.80-0.83), 30.55 (95% CI, 21.40-43.62), 4.29 (95% CI, 3.51-5.25), and 0.16 (95% CI, 0.13-0.21), respectively, showing statistical heterogeneity. Multivariable metaregression analysis showed contrast mode to be the most significant source of heterogeneity. The overall sensitivity, specificity, LR+, LR, and diagnostic OR of CEUS imaging in predicting the overall pathologic response to NAC in patients with BC were 0.89 (95% CI, 0.83-0.93), 0.83 (95% CI, 0.78-0.88), 4.49 (95% CI, 3.04-6.62), 0.16 (95% CI, 0.10-0.24,), and 32.21 (95% CI, 16.74-62.01), respectively, showing mild heterogeneity. Conclusion Our data confirmed the excellent performance of breast CEUS in differentiating between benign and malignant breast lesions as well as pathologic response prediction in patients with BC receiving NAC.

Teacher preparation programs (tpps) are expected to help preservice teachers (psts) to be aware of identify and revise their possibly biased perceptions on potential students and schools they may serve. Field experience, a requirement of every accredited tpp, has long been viewed to have an awareness-raising effect to reconstruct experiences into meaningful insights as well as to impact psts’ perceptions. This study employs photovoice to explore five psts’ perception on rural students and education during their 5-day field experiences from visiting five rural and ethnic minority schools in Yunnan, a southwest province of China. The findings from this study indicate that psts in the field far different from their personal education background shift their perspective concern from instruction to student, which could be the sign that psts improve their reflection to become more inclusive, student-centered other than egocentric, teacher-centered. The study also found that photovoice mediates the participants to document and report their lived experience. It gives the opportunities to psts to ascribe meanings to the photos representing their knowledge and perspective of the phenomenon. Implication to tpps is finally discussed.

OBJECTIVES To investigate the correlation between ultrasound (US) appearances of invasive breast cancers and tumor proliferation and invasiveness measured according to the histologic grade, Ki-67 expression, axillary lymph node metastasis (ALNM), and lymphovascular invasion (LVI). METHODS This study evaluated 676 patients who underwent primary surgical treatment of invasive breast cancers. The preoperative US reports and postoperative pathologic and immunohistochemical results of the patients were retrospectively reviewed. Ultrasound characteristics were evaluated according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) lexicon. Logistic regression analyses were used to identify independent predictive US features that were correlated with tumor proliferation and invasiveness of breast cancers. Odds ratios (ORs) were calculated. RESULTS Posterior acoustic enhancement and calcifications on US images were independent predictive factors of a higher histologic grade and a higher Ki-67 level (OR, 1.69-6.54; P

BackgroundStudies on deep learning (DL)-based models in breast ultrasound (US) remain at the early stage due to a lack of large datasets for training and independent test sets for verification. We aimed to develop a DL model for differentiating benign from malignant breast lesions on US using a large multicenter dataset and explore the model’s ability to assist the radiologists.MethodsA total of 14,043 US images from 5012 women were prospectively collected from 32 hospitals. To develop the DL model, the patients from 30 hospitals were randomly divided into a training cohort (n = 4149) and an internal test cohort (n = 466). The remaining 2 hospitals (n = 397) were used as the external test cohorts (ETC). We compared the model with the prospective Breast Imaging Reporting and Data System assessment and five radiologists. We also explored the model’s ability to assist the radiologists using two different methods.ResultsThe model demonstrated excellent diagnostic performance with the ETC, with a high area under the receiver operating characteristic curve (AUC, 0.913), sensitivity (88.84%), specificity (83.77%), and accuracy (86.40%). In the comparison set, the AUC was similar to that of the expert (p = 0.5629) and one experienced radiologist (p = 0.2112) and significantly higher than that of three inexperienced radiologists (p ConclusionsThe DL model yielded satisfactory predictions in distinguishing benign from malignant breast lesions. The model showed the potential value in improving the diagnosis of breast lesions by radiologists.

The aim of the work described here was to develop an ultrasound (US) image-based deep learning model to reduce the rate of malignancy among breast lesions diagnosed as category 4A of the Breast Imaging-Reporting and Data System (BI-RADS) during the pre-operative US examination. A total of 479 breast lesions diagnosed as BI-RADS 4A in pre-operative US examination were enrolled. There were 362 benign lesions and 117 malignant lesions confirmed by postoperative pathology with a malignancy rate of 24.4%. US images were collected from the database server. They were then randomly divided into training and testing cohorts at a ratio of 4:1. To correctly classify malignant and benign tumors diagnosed as BI-RADS 4A in US, four deep learning models, including MobileNet, DenseNet121, Xception and Inception V3, were developed. The performance of deep learning models was compared using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Meanwhile, the robustness of the models was evaluated by five-fold cross-validation. Among the four models, the MobileNet model turned to be the optimal model with the best performance in classifying benign and malignant lesions among BI-RADS 4A breast lesions. The AUROC, accuracy, sensitivity, specificity, PPV and NPV of the optimal model in the testing cohort were 0.897, 0.913, 0.926, 0.899, 0.958 and 0.784, respectively. About 14.4% of patients were expected to be upgraded to BI-RADS 4B in US with the assistance of the MobileNet model. The deep learning model MobileNet can help to reduce the rate of malignancy among BI-RADS 4A breast lesions in pre-operative US examinations, which is valuable to clinicians in tailoring treatment for suspicious breast lesions identified on US.

To describe grayscale ultrasound (US) features of metastatic ovarian tumors (MOTs) based on origin of the primary tumor in a large sample size study.This retrospective cross-sectional single-center study included 112 patients with 190 histopathologically confirmed MOTs. Among the patients, 102 collectively had 144 masses, which were detected via US. The clinical data and static US images of MOTs were collected.The MOTs were mostly bilateral (78.9%) but had a lower rate of bilaterality when detected by US (55.6%). Breast cancer metastasis had the highest nondetection rate (69.6%), because its focal metastasis could only be recognized using histology or immunohistochemistry. The stomach was the most common origin of metastasis (45.3% and 50.7% detected via pathology and US, respectively). The US images were classified into three subtypes: multilocular solid (Type A), purely solid (Type B), and solid with several round or oval cysts (Type C). The MOTs that originated from the colon mostly belonged to Type A (65.1%) and closely mimicked primary epithelial ovarian tumor morphologically. The MOTs that originated from the stomach predominantly belonged to Types B (31.5%) and C (57.5%). Signet-ring cell carcinoma (SRCC) corresponded to Types B and C regardless of origin.The developed novel typing method provides more vivid images for classifying MOTs compared with existing typing methods. Given that no specific sonographic parameters have been established to distinguish MOTs from primary invasive ovarian tumors, these images may be helpful in diagnosing these masses.

ObjectiveTo explore the feasibility of shear wave elastography (SWE) parameters for assessing the biological behavior of breast cancer.Materials and MethodsIn this prospective study, 224 breast cancer lesions in 216 female patients were examined by B-mode ultrasound and shear wave elastography in sequence. The maximum size (Smax) of the lesion was measured by B-mode ultrasound, and then shear wave elastography was performed on this section to obtain relevant parameters, including maximum elasticity (Emax), mean elasticity (Emean), standard deviation of elasticity (SD), and the area ratio of shear wave elastography to B-mode ultrasound (AR). The relationship between SWE parameters and pathological type, histopathological classification, histological grade, lymphovascular invasion status (LVI), axillary lymph node status (ALN), and immunohistochemistry of breast cancer lesions was performed according to postoperative pathology.ResultsIn the univariate analysis, the pathological type and histopathological classification of breast cancer were not significantly associated with SWE parameters; with an increase in the histological grade of invasive ductal carcinoma (IDC), SD (p = 0.016) and Smax (p = 0.000) values increased. In the ALN-positive group, Smax (p = 0.004) was significantly greater than in the ALN-negative group; Smax (p = 0.003), Emax (p = 0.034), and SD (p = 0.045) were significantly higher in the LVI-positive group than in the LVI-negative group; SD (p = 0.043, p = 0.047) and Smax (p = 0.000, p = 0.000) were significantly lower in the ER+ and PR+ groups than in the ER- and PR- groups, respectively; AR (p = 0.032) was significantly higher in the ER+ groups than in the ER- groups, and Smax (p = 0.002) of the HER2+ group showed higher values than that of the HER2- group; Smax (p = 0.000), SD (p = 0.006), and Emax (p = 0.004) of the Ki-67 high-expression group showed significantly higher values than those of the Ki-67 low-expression group. In the multivariate analysis, Ki-67 was an independent factor of Smax (p = 0.005), Emax (p = 0.004), and SD (p = 0.006); ER was an independent influencing factor of Smax (p = 0.000) and AR (p = 0.032). LVI independently influences Smax (p = 0.006).ConclusionsThe SWE parameters Emax, SD, and AR can be used to evaluate the biological behavior of breast cancer.

BACKGROUND: The high false-positive rates of US Breast Imaging Reporting and Data System (BI-RADS) category 3–4a breast lesions leads to excessive biopsies of many benign lesions, and our aim was to investigate the diagnostic performance achieved by adding a maximum elasticity (Emax) of shear-wave elastography (SWE) to ultrasound (US) to evaluate US BI-RADS category 3–4a breast lesions using conservative and aggressive approaches. We explored the capacity of using this method to avoid unnecessary biopsies without increasing the probability of missing breast cancers. METHODS: A total of 123 breast lesions of 120 patients classified as BI-RADS category 3 or 4a were enrolled from January 2019 to December 2019. The US features were evaluated according to the US BI-RADS lexicon. The maximum diameter measured on the US was defined as the size of the lesion. The Emax was assessed by SWE, and the average Emax of breast lesions on two images were calculated and recorded as the final maximum Young’s modulus. The diagnostic performance of the combined B-mode US and SWE approach for BI-RADS category 3–4a breast lesions was tested using a conservative approach and an aggressive approach. In the conservative approach, the lesions were downgraded with Emax of 30 kPa or less and upgraded with Emax of 160 kPa or more. In the aggressive approach, the lesions were downgraded with Emax of 80 kPa or less and upgraded with Emax of 160 kPa or more. Pathologic results were defined as the reference standard. RESULTS: Among all 123 breast lesions, there were 60 lesions classified as BI-RADS category 3 and 63 lesions classified as BI-RADS category 4a. Compared to the B-mode US, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC) of the combined B-mode US and SWE with a conservative approach changed from 88.9% to 94.4%, 55.2% to 60.0%, 25.4% to 28.8%, 96.7% to 98.4%, 60.2% to 65.0%, and 0.721 to 0.772, respectively. The specificity, PPV, and accuracy of combined B-mode US and SWE with an aggressive approach increased from 55.2% to 72.4%, 25.4% to 29.3%, and 60.2% to 71.5%, respectively, but this was accompanied with decreases in the sensitivity from 88.9% to 66.7%, the NPV from 96.7% to 92.7%, and the AUC from 0.721 to 0.695. CONCLUSIONS: The addition of SWE improves the diagnostic performance of breast US. Adding the diagnostic criteria of SWE to the BI-RADS assessment of B-mode US, downgrading the lesions with Emax 30 kPa or less, and upgrading the lesions with Emax 160 kPa or more helped discriminate low suspicion lesions from benign lesions in order to decrease false-positive findings and avoid missing cancer diagnosis.

Objective To assess the performance of conventional ultrasound (US) combined with strain elastography (SE) in the Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions on mammography. Methods Women with breast lesions identified as having mammography BI-RADS 4 lesions and underwent US examination were included in China. US features and US BI-RADS assessment were recorded in real-time and prospectively reported. The pathological result was referred to as the gold standard. The performance of US in the mammographic BI-RADS category 4 lesions was evaluated. Diagnostic performances of US BI-RADS, SE and combined both were compared. Results 751 women with 751 breast lesions classified as mammographic BI-RADS category 4 were included. For mammographic findings, 530 (70.6%) were true-positive and 221 (29.4%) were false-positive. Conventional US achieved higher positive predictive value (PPV) than mammography (78.5% vs 70.6%, p=0.001). The specificity increased from 34.4% to 47.1% (p Conclusions Conventional US combined with SE is a helpful tool for the non-invasive examination of breast lesions classified as BI-RADS category 4 on mammography. It helped increase the PPV and had the potential to avoid unnecessary biopsies of BI-RADS category 4 lesions detected on mammography. Micro Abstract We assessed the performance of conventional ultrasound (US) combined with strain elastography (SE) in the Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions on mammography. 751 women with 751 breast lesions classified as mammographic BI-RADS category 4 were included. Conventional US combined with SE helped increase the PPV and had the potential to avoid unnecessary biopsies of BI-RADS category 4 lesions detected on mammography.

Background and Objectives: The breast undergoes extensive physiologic changes during pregnancy/lactation. We aimed to review the ultrasound (US) imaging characteristics of breast lesions during pregnancy/lactation and to demonstrate ultrasonography as an excellent imaging modality in this patient population. Materials and Methods: We performed a retrospective study involving 195 patients with 206 pathologically confirmed breast lesions during pregnancy/lactation over the period of January 2010 to December 2018; 51 were diagnosed with breast cancer, including 50 invasive ductal carcinomas and 1 low malignant potential phyllodes tumor, whereas 144 were diagnosed with 155 benign lesions, including 103 mastitis/abscesses, 45 fibroadenomas, 2 intraductal papillomas, 2 sclerosing lesions, and 3 benign phyllodes tumors. The patients' age, lesion diameter, and US characteristics were analyzed. Results: The breast lesions of patients during pregnancy/lactation were compared with those during nonpregnancy/lactation. Patients with breast cancer or fibroadenomas during pregnancy/lactation were younger than those during nonpregnancy/lactation. The average lesion diameter was significantly higher among pregnant/lactating patients compared with controls of childbearing age for fibroadenomas, but not for malignant lesions. The fibroadenomas and mastitis/abscesses during pregnancy/lactation usually have higher BI-RADS categories than those during nonpregnancy/lactation. Conclusions: The imaging features of breast cancer during pregnancy/lactation did not differ much from those of nonpregnancy/lactation; however, some benign lesions had suspicious sonographic features, and US-guided core biopsies were necessary for differentiating benign from malignant lesions.

Uniaxial compression testing was combined with the digital speckle correlation method (DSCM) to explore the deformation field evolution characteristics and the Poisson’s ratio and energy release responses of a geopolymer cemented coal gangue-fly ash backfill (GCGFB) sample under uniaxial compression. The results showed that in the initial stage of loading, the deformation of the GCGFB sample was small and evenly distributed, and no localized deformation zone formed in the sample. As the loading progressed, a localized deformed zone gradually appeared, and the formation rate gradually accelerated. The maximum shear strain value at the start of localized deformation was 0.06. In the post-peak loading process, the localized deformed zone gradually expanded, and the degree of unevenness of the localized deformation was much greater than that before the peak; the maximum shear strain value at this time was 0.22. During the localization of the deformation to the peak intensity stage, the growth rate of the Poisson’s ratio of the GCGFB sample was markedly increased, and the growth rate after the peak was notably slowed and exhibited unstable fluctuations. When the stress reached the peak, the Poisson’s ratio was 0.24. The peak strain of the GCGFB sample was large, and the failure characteristics of the GCGFB sample showed multiple shear deformation zones, which is indicative of a typical cylindrical splitting failure. The deformation energy density of the GCGFB sample exhibited a slow decline after the peak, and the rate of decline gradually decreased. The deformation energy density drop time of the GCGFB sample was 96 s, which is much longer than that of high-strength rock.

As a new paste backfill material, geopolymer cemented coal gangue-fly ash backfill is prepared from fly ash geopolymer and spontaneous combustion gangue and can be beneficial to the cleaner production of coal mines and coal-fired power plants. A new mixing technique, in which a small amount of fly ash was mixed with cement, slag and water and allowed to react for a period of time before an aggregate and residual fly ash were added, was proposed. The influences of the solid mass concentration, the fine gangue ratio and the fly ash content on the workability and strength of the paste backfill material were analyzed, and ratio optimization was carried out using the response surface method, multi-objective multi-verse optimization and the desirability function approach. The results show that the optimal mixture is obtained when the mass concentration is 79.65%, the fine gangue rate is 57.19%, and the fly ash content is 15.67% of the total mass. The new mixing technique makes the fly ash more active to stimulate the early formation of calcium silicate gel and calcium silicate hydrate gel that improve the aggregate formation strength. In the later stage, amorphous hydrated calcium aluminate gel-coated ettringite and calcium carbonate formed a flocculation structure between the fly ash particles that gradually formed a condensation-crystallization structure. The research results can make extensive use of fly ash and gangue to promote cleaner production.