Zhi Li Wang

Immunogenic cell death (ICD) represents a promising strategy to stimulate antitumor immunity. Doxorubicin (DOX) is one drug that can trigger ICD. However, DOX has limitations for treating triple-negative breast cancer (TNBC). Its ability to stimulate a strong immune response is weak. The drug also promotes an immunosuppressive tumor microenvironment. This suppression hinders the antitumor immune response. Since reactive oxygen species (ROS) generation is a pivotal ICD trigger, we therefore hypothesized that nintedanib, a triple tyrosine kinase inhibitor known to promote immune cell infiltration and activation within tumors, potentiates DOX-induced ICD by augmenting intratumoral ROS generation. To address this, we engineered a targeted nanoenhancer for TNBC by coencapsulating liquid nintedanib and perfluoropentane (PFP), within a peptide-functionalized (tLyp-1) liposomal shell, creating tLyp-1-Nintedanib-PFP Nanoparticles (tNP-NPs) for use in sonodynamic therapy. This tNP-NP platform is intended for combination therapy with DOX in a chemoimmunotherapeutic strategy. With the assistance of low-intensity focused ultrasound (LIFU), tNP-NPs exhibited potent targeting and penetrating capabilities toward 4T1 TNBC cells, enhanced drug cytotoxicity, ROS production, and ICD markers in vitro. Subsequently, for the in vivo study, to mitigate systemic toxicity, an injectable thermosensitive hydrogel coencapsulating tNP-NPs and DOX was applied for local peritumoral injection. In orthotopic 4T1 TNBC models, this combination elicited potent ICD, promoting dendritic cell (DC) cells maturation and cytotoxic T lymphocyte activation within tumors and draining lymph nodes. This approach achieved exceptional antitumor efficacy and systemic immunity while reversing the immunosuppressive tumor microenvironment, without observable organ toxicity, presenting a robust chemoimmunotherapeutic strategy for TNBC.

Objectives To develop an AI model using ultrasound features of carotid plaque for predicting the risk of acute ischemic stroke (AIS) and assess its efficacy in comparison with conventional regression prediction models. Materials and methods This study retrospectively included 923 patients who underwent US and CEUS examinations of carotid plaque at our institution. They were randomly divided into training + validation and test set in an 8:2 ratio. Additionally, 143 prospectively collected patients from three other centers were included as an external test set. Two expert radiologists described and documented the ultrasound images. Logistic regression analysis was used to analyze plaque ultrasound characteristics, leading to the establishment of statistical predictive models for AIS risk based on US alone and US combined with CEUS. AI models were developed using ResNet34 architecture trained on ultrasound images. ROC curves were generated, and AUC values were computed to compare the performance of the statistical models with the AI models. Results During a median follow-up of 5.3 years, 523 patients experienced AIS, while 543 had no history of stroke. The AUC was 0.719 for the model using US alone, 0.819 for the model combining US with CEUS, and 0.917 for the model incorporating AI, with all pairwise comparisons being statistically significant (p < 0.05). Furthermore, the AUC of the AI model in the external test set was 0.866, indicating good generalization ability and stability. Conclusion US and CEUS characteristics of carotid plaque were strongly associated with AIS. Deep learning enhances AIS prediction in carotid plaque assessment using ultrasound. Key Points Question Can AI enhance the prediction of AIS risk by analyzing the characteristics of carotid artery plaques using US and CEUS compared to conventional statistical models? Findings The AI model integrating US and CEUS plaque features significantly outperformed traditional methods in AIS risk prediction, with CEUS providing substantial value beyond US alone. Clinical relevance This AI-driven approach offers an automated and standardized method for stratifying stroke risk directly from ultrasound images of carotid plaques. This advancement facilitates early identification of high-risk individuals, eliminating the need for additional testing or labor-intensive manual analysis. Graphical Abstract

Triple‑negative breast cancer (TNBC) is an aggressive malignancy with limited treatment options, leading to poor clinical outcomes and the need for novel therapeutic approaches. Nintedanib, a United States Food and Drug Administration‑approved multi‑kinase inhibitor with anti‑fibrotic and anti‑angiogenic properties, has shown promise in cancer treatment. However, its precise molecular effects on TNBC have not yet been fully elucidated. Therefore, the present study aimed to investigate the therapeutic potential of nintedanib in TNBC using in vitro and in vivo models, specifically focusing on its regulatory effects on key oncogenic pathways. The present study utilized TNBC cell lines (MDA‑MB‑231 and 4T1) and BALB/c mice to evaluate the antitumor efficacy of nintedanib. Cell viability and clonogenic capacity were assessed using Cell Counting Kit‑8 and colony formation assays. Subsequently, apoptosis induction and cell cycle progression were determined by flow cytometry, and cell migration and invasion were analyzed through scratch and Transwell assays. To identify underlying mechanisms, potential molecular targets were identified via bioinformatics and network pharmacology, and were validated through western blotting, immunofluorescence and immunohistochemistry. Finally, an orthotopic TNBC mouse model was established and monitored in real time by multimodal ultrasound imaging. The results revealed that nintedanib significantly inhibited TNBC cell proliferation and suppressed stem cell‑like properties. Furthermore, it induced cell cycle arrest at the G₂/M phase and promoted apoptosis. Mechanistic analysis revealed that nintedanib activated tumor protein p73 (TP73), leading to the disruption of the p53‑peroxisome proliferator‑activated receptor α (PPARα)/PI3K‑Akt signaling axis. Additionally, it downregulated epithelial‑mesenchymal transition (EMT) markers, including Snail and zinc finger E‑box‑binding homeobox protein 1, thereby mitigating tumor invasiveness. In vivo, nintedanib treatment effectively reduced tumor growth, angiogenesis and stiffness, indicating its potential as a viable therapeutic agent for TNBC. In conclusion, nintedanib exerts potent anti‑TNBC effects by modulating TP73, disrupting oncogenic signaling via the p53‑PPARα/PI3K‑Akt axis, and attenuating EMT‑associated transcription factors. These findings highlight its potential as a promising targeted therapy for TNBC, warranting further clinical exploration.

To construct a multimodal ultrasound (US) radiomics model for predicting axillary lymph node metastasis (ALNM) in breast cancer and evaluated its application value in predicting ALNM and patient prognosis. From March 2014 to December 2022, data from 682 breast cancer patients from four hospitals were collected, including preoperative grayscale US, color Doppler flow imaging (CDFI), contrast-enhanced ultrasound (CEUS) imaging data, and clinical information. Data from the First Medical Center of PLA General Hospital were used as the training and internal validation sets, while data from Peking University First Hospital, the Cancer Hospital of the Chinese Academy of Medical Sciences, and the Fourth Medical Center of PLA General Hospital were used as the external validation set. LASSO regression was employed to select radiomic features (RFs), while eight machine learning algorithms were utilized to construct radiomic models based on US, CDFI, and CEUS. The prediction efficiency of ALNM was assessed to identify the optimal model. In the meantime, Radscore was computed and integrated with immunoinflammatory markers to forecast Disease-Free Survival (DFS) in breast cancer patients. Follow-up methods included telephone outreach and in-person hospital visits. The analysis employed Cox regression to pinpoint prognostic factors, while clinical-imaging models were developed accordingly. The performance of the model was evaluated using the C-index, Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA). In the training cohort (n = 400), 40% of patients had ALNM, with a mean age of 55 ± 10 years. The US + CDFI + CEUS-based radiomics model achieved Area Under the Curves (AUCs) of 0.88, 0.81, and 0.77 for predicting N0 versus N+ (≥ 1) in the training, internal, and external validation sets, respectively, outperforming the US-only model (P < 0.05). For distinguishing N+ (1-2) from N+ (≥ 3), the model achieved AUCs of 0.89, 0.74, and 0.75. Combining radiomics scores with clinical immunoinflammatory markers (platelet count and neutrophil-to-lymphocyte ratio) yielded a clinical-radiomics model predicting disease-free survival (DFS), with C-indices of 0.80, 0.73, and 0.79 across the three cohorts. In the external validation cohort, the clinical-radiomics model achieved higher AUCs for predicting 2-, 3-, and 5-year DFS compared to the clinical model alone (2-year: 0.79 vs. 0.66; 3-year: 0.83 vs. 0.70; 5-year: 0.78 vs. 0.64; all P < 0.05). Calibration and decision curve analyses demonstrated good model agreement and clinical utility. The multimodal ultrasound radiomics model based on US, CDFI, and CEUS could effectively predict ALNM in breast cancer. Furthermore, the combined application of radiomics and immune inflammation markers might predict the DFS of breast cancer patients to some extent.

To develop a predictive model to identify atypical ductal hyperplasia (ADH) that was underestimated by US-guided core needle biopsy (CNB) and to evaluate the risk factors for underestimation for ADH with intraductal papilloma diagnosed by CNB. In this retrospective study, 300 CNB-diagnosed ADH lesions in 291 consecutive women between January 2014 and July 2023 were included and divided into training set (n = 181), internal validation set (n = 54), and external validation set (n = 65). The review included clinical, pathological, and US features, as well as final outcomes. Multivariate logistic regression was employed to establish predictive model and to evaluate risk factors. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis, and utility (patient stratification into low and high-risk groups). Model was validated both internally and externally by calculating its performance on validation sets. The upgrade rate to malignancy was 51.0%. Predictors included in the model were age, the pathological pattern of ADH with intraductal papilloma or ADH alone, Ki-67 positivity, and imaging-pathological discordance. The AUC was 0.915 (95% CI: 0.858, 0.955) in the training set, 0.906 (95% CI: 0.785, 0.972) in the internal validation set, and 0.934 (95% CI: 0.836, 0.983) in the external validation set. Using a cutoff value of 0.11, 38.3% of nonmalignant lesions in the training set were stratified into low-risk group with an upgrade rate of 4.1%. Similar results were obtained in the validation sets. For ADH with intraductal papilloma, age and imaging-pathological discordance were the independent risk factors for malignancy upgrading. The model established to predict ADH upgrading can help in individualized risk management. If predictors of non-upgraded ADH lesions can be confirmed with larger studies, more than one-third of non-malignant lesions are expected to be candidates for non-excision. This is a retrospective study.

Background: This study investigated the relationship between Shear Wave Elastography (SWE), TGF-β 1/MAPK signaling molecules, and epithelial-to-mesenchymal transition (EMT) in breast lesions, exploring the feasibility of SWE in early EMT identification for breast cancer. Methods: 117 breast lesions in 107 patients from July to November 2023 were consecutively enrolled. SWE was performed preoperatively, and elastic parameters were documented. Immunohistochemistry (IHC) assessed the expression levels of TGF-β 1, p38 MAPK, p-p38 MAPK, ERK1/2, p-ERK1/2, ERK5, p-ERK5, JNK, p-JNK, E-cadherin, β-catenin, N-cadherin, and Vimentin. Correlations between SWE parameters and biomarkers were analysed, and their diagnostic efficacy for axillary lymph node metastasis (LNM) was evaluated. Results: Among 117 breast lesions, 53 were classified as benign and 64 as malignant (25 exhibiting axillary LNM). Optimal SWE thresholds for distinguishing benign from malignant lesions were Emax = 106.7 kPa, Emean = 62.9 kPa, Emin = 22.5 kPa, Eratio = 3.4, and Esd = 21.2 kPa. For LNM prediction, cut-offs were Emax = 170.1 kPa, Emean = 118.5 kPa, and Eratio = 10.5. TGF-β 1 and E-cadherin showed significant predictive value for LNM (AUCs: 0.774 and 0.704, respectively). E-cadherin negatively correlated with SWE parameters, while TGF-β 1 and MAPK molecules (p38 MAPK, p-p38 MAPK) showed positive correlations. Lesions with “stiff rim sign” had significantly lower E-cadherin expression but elevated levels of TGF-β 1 (P< 0.001). Additionally, Vimentin, p38 MAPK and p-p38 MAPK levels were higher in the occurrence of the “stiff rim sign” (P all < 0.05). Conclusion: TGF-β 1, p38 MAPK, and E-cadherin demonstrated strong diagnostic capabilities and correlated with SWE parameters. SWE offers a promising non-invasive approach for assessing prognosis by identifying EMT characteristics at an earlier stage in breast cancer.

Background: Current predictive models for disease-free survival (DFS) in invasive breast cancer predominantly utilize clinical and pathological factors, with minimal incorporation of ultrasound (US) and contrast-enhanced ultrasound (CEUS) characteristics. This study aimed to establish a multimodal map integrating US, clinical features, and US data to enhance the prediction of DFS in invasive breast cancer. Methods: The study utilized three retrospective datasets obtained from three academic medical centers, covering the period from March 2014 to December 2022. Clinical data, gray scale US, and CEUS were assessed in 942 adult patients undergoing breast cancer resection. The training and internal test sets were supplied by The First Medical Center of the PLA General Hospital, while the external test sets were sourced from The Fourth Medical Center of the PLA General Hospital and the Specialist Medical Center of the Strategic Support Forces. The patients were followed up by phone or clinic visits. DFS was evaluated as a prognostic outcome. Cox regression analysis identified prognostic factors, leading to the construction of three nomograms. The model performance was evaluated using the C-index, time-dependent receiver operating characteristic (ROC) curve, calibration, decision curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification index (NRI). Results: A total of 942 patients were enrolled, with a mean age of 51.91 years [interquartile range (IQR), 44.25–58.69 years]. The patients were included with the median DFS of 36 months. Cox regression analysis identified menopausal status, body mass index (BMI), color Doppler flow imaging (CDFI), tumor size on CEUS, adjuvant/neoadjuvant chemotherapy, progesterone receptor (PR) status, and tumor-node-metastasis (TNM) staging as significant risk factors for invasive breast cancer. The nomogram combining US, CEUS, and clinical data demonstrated excellent predictive performance, achieving a C-index of 0.811 in the training set, 0.816 in the internal validation set, and 0.819 in the external validation set. Calibration curves confirmed that the predicted survival probabilities aligned closely with observed outcomes. Comparative analysis of ROC curves, IDI, NRI, and DCA confirmed that the integrated nomogram outperformed models based solely on US and clinical data or clinical data alone in predicting 24- and 36-month DFS. Conclusions: The integration of CEUS and clinical factors for non-invasive DFS prediction improves personalized risk stratification, minimizing unnecessary interventions for low-risk patients and ensuring adequate monitoring for high-risk individuals. Additional prospective validation is required to establish its clinical applicability and incorporation into standard oncology practice.

Original Article Application of an artificial intelligence-assisted diagnostic system for breast ultrasound: a prospective study Zhi-Ying Jin, Jun-Kang Li, Rui-Lan Niu, Nai-Qin Fu, Ying Jiang, Shi-Yu Li, Zhi-Li Wang