A Multicenter Cohort Study on Ultrasound-based Deep Learning Nomogram for Predicting Post-Neoadjuvant Chemotherapy Axillary Lymph Node Status in Breast Cancer Patients.

RATIONALE AND OBJECTIVES: The aim of this study was to evaluate the capability of an ultrasound (US)-based deep learning (DL) nomogram for predicting axillary lymph node (ALN) status after neoadjuvant chemotherapy (NAC) in breast cancer patients and its potential to assist radiologists in diagnosis. METHODS: Two medical centers retrospectively recruited 535 node-positive breast cancer patients who had undergone NAC. Center 1 included 288 patients in the training cohort and 123 patients in the internal validation cohort, while center 2 enrolled 124 patients for the external validation cohort. Five DL models (ResNet 34, ResNet 50, VGG19, GoogLeNet, and DenseNet 121) were trained on pre- and post-NAC US images, and the best model was chosen. A US-based DL nomogram was constructed using DL predictive probabilities and clinicopathological characteristics. Furthermore, the performances of radiologists were compared with and without the assistance of the nomogram. RESULT: ResNet 50 performed best among all DL models, achieving areas under the curve (AUCs) of 0.837 and 0.850 in the internal and external validation cohorts, respectively. The US-based DL nomogram demonstrated strong predictive ability for ALN status post-NAC, with AUCs of 0.890 and 0.870 in the internal and external validation cohorts, respectively, outperforming both the clinical model and the DL model (p all < 0.05, except p = 0.19 for DL model in external validation cohort). Moreover, the nomogram significantly improved radiologists' diagnostic ability. CONCLUSION: The US-based DL nomogram is promising for predicting ALN status post-NAC and could assist radiologists for better diagnostic performance.

Predicting Pathological Characteristics of HER2-Positive Breast Cancer from Ultrasound Images: a Deep Ensemble Approach.

The objective is to evaluate the feasibility of utilizing ultrasound images in identifying critical prognostic biomarkers for HER2-positive breast cancer (HER2 + BC). This study enrolled 512 female patients diagnosed with HER2-positive breast cancer through pathological validation at our institution from January 2016 to December 2021. Five distinct deep convolutional neural networks (DCNNs) and a deep ensemble (DE) approach were trained to classify axillary lymph node involvement (ALNM), lymphovascular invasion (LVI), and histological grade (HG). The efficacy of the models was evaluated based on accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), receiver operating characteristic (ROC) curves, areas under the ROC curve (AUCs), and heat maps. DeLong test was applied to compare differences in AUC among different models. The deep ensemble approach, as the most effective model, demonstrated AUCs and accuracy of 0.869 (95% CI: 0.802-0.936) and 69.7% in LVI, 0.973 (95% CI: 0.949-0.998) and 73.8% in HG, thus providing superior classification performance in the context of imbalanced data (p < 0.05 by the DeLong test). On ALNM, AUC and accuracy were 0.780 (95% CI: 0.688-0.873) and 77.5%, which were comparable to other single models. The pretreatment US-based DE model could hold promise as a clinical guidance for predicting pathological characteristics of patients with HER2-positive breast cancer, thereby providing benefit of facilitating timely adjustments in treatment strategies.

Targeting the HSP47-collagen axis inhibits brain metastasis by reversing M2 microglial polarization and restoring anti-tumor immunity.

Brain metastases (BrMs) are the leading cause of death in patients with solid cancers. BrMs exhibit a highly immunosuppressive milieu and poor response to immunotherapies; however, the underlying mechanism remains largely unclear. Here, we show that upregulation of HSP47 in tumor cells drives metastatic colonization and outgrowth in the brain by creating an immunosuppressive microenvironment. HSP47-mediated collagen deposition in the metastatic niche promotes microglial polarization to the M2 phenotype via the α2ß1 integrin/nuclear factor κB pathway, which upregulates the anti-inflammatory cytokines and represses CD8+ T cell anti-tumor responses. Depletion of microglia reverses HSP47-induced inactivation of CD8+ T cells and abolishes BrM. Col003, an inhibitor disrupting HSP47-collagen association restores an anti-tumor immunity and enhances the efficacy of anti-PD-L1 immunotherapy in BrM-bearing mice. Our study supports that HSP47 is a critical determinant of M2 microglial polarization and immunosuppression and that blocking the HSP47-collagen axis represents a promising therapeutic strategy against brain metastatic tumors.

Double-layer hollow mesoporous silica nanoparticles for ultrasound-guided photodynamic treatment.

Cervical carcinoma persists as a major global public health burden. While conventional therapeutic modalities inevitably cause ablation of adjacent non-tumorous tissues, photodynamic therapy (PDT) offers a targeted cytotoxic strategy through a photosensitizing agent (PS). However, the hydrophobicity and lack of selective accumulation of promising PS compounds such as zinc(II) phthalocyanine (ZnPc) impedes their clinical translation as standalone agents. The present study sought to incorporate ZnPc within double-layer hollow mesoporous silica nanoparticles (DHMSN) as nanocarriers to enhance aqueous dispersibility and tumor specificity. Owing to their compartmentalized design, the hollow mesoporous silica nanoparticles (HMSN) demonstrated enhanced ultrasonic imaging contrast. Combined with the vaporization of the perfluorocarbon perfluoropentane (PFP), the HMSN-encapsulated ZnPc enabled real-time ultrasound monitoring of PDT treatment.In vivo, the innate thermal energy induced vaporization of the DHMSN-carried PFP to significantly amplify ultrasound signals from the tumor site. Results demonstrated biocompatibility, efficient PFP microbubble generation, and robust photocatalytic activity. Collectively, this investigation establishes ultrasound-guided PDT utilizing multi-layer HMSN as a targeted therapeutic strategy for cervical malignancies with mitigated toxicity.

Prediction of pathological complete response of breast cancer patients who received neoadjuvant chemotherapy with a nomogram based on clinicopathologic variables, ultrasound, and MRI.

OBJECTIVE: To establish a nomogram for predicting the pathologic complete response (pCR) in breast cancer (BC) patients after NAC by applying magnetic resonance imaging (MRI) and ultrasound (US). METHODS: A total of 607 LABC women who underwent NAC before surgery between January 2016 and June 2022 were retrospectively enrolled, and then were randomly divided into the training (n = 425) and test set (n = 182) with the ratio of 7:3. MRI and US variables were collected before and after NAC, as well as the clinicopathologic features. Univariate and multivariate logistic regression analyses were applied to confirm the potentially associated predictors of pCR. Finally, a nomogram was developed in the training set with its performance evaluated by the area under the receiver operating characteristics curve (ROC) and validated in the test set. RESULTS: Of the 607 patients, 108 (25.4%) achieved pCR. Hormone receptor negativity (odds ratio [OR], 0.3; P < .001), human epidermal growth factor receptor 2 positivity (OR, 2.7; P = .001), small tumour size at post-NAC US (OR, 1.0; P = .031), tumour size reduction ≥50% at MRI (OR, 9.8; P < .001), absence of enhancement in the tumour bed at post-NAC MRI (OR, 8.1; P = .003), and the increase of ADC value after NAC (OR, 0.3; P = .035) were all significantly associated with pCR. Incorporating the above variables, the nomogram showed a satisfactory performance with an AUC of 0.884. CONCLUSION: A nomogram including clinicopathologic variables and MRI and US characteristics shows preferable performance in predicting pCR. ADVANCES IN KNOWLEDGE: A nomogram incorporating MRI and US with clinicopathologic variables was developed to provide a brief and concise approach in predicting pCR to assist clinicians in making treatment decisions early.

Effectiveness of focused extracorporeal shock wave versus manual therapy in postpartum patients with sacroiliac joint dysfunction: a prospective clinical trial.

OBJECTIVE: To investigate the effectiveness of focused extracorporeal shock wave therapy (FESWT) in treating postpartum sacroiliac joint (SIJ) dysfunction. METHODS: A total of 90 patients with SIJ dysfunction were included and randomly assigned to FESWT, manual therapy (MT), or combination therapy (CT) groups. Pain intensity and Oswestry Disability Index (ODI) score were measured upon admission, after 1 and 2 weeks of treatments. The treatment efficacy and adverse events of each group were also assessed. RESULTS: There were no significant differences among three groups regarding clinical data, pain intensity, and ODI score on admission (all P > 0.05). After 1 week of treatment, FESWT exhibited similar pain intensity and lower ODI score (P < 0.001) compared to MT. After 2 weeks of treatment, the pain and ODI in FESWT were similar with MT. The pain in CT was lower than MT after 1 week, but lower than FESWT after 2 weeks. Furthermore, we identified interaction effects between treatment method and duration in relation to pain intensity (Fgroup*time = 5.352, P = 0.001) and ODI score (Fgroup*time = 5.902, P < 0.001). FESWT group exhibited the highest improvement rate of 66.7%, while CT group achieved the highest cure rate of 73.3%. No adverse events were observed in any of the patients during 2 months follow-up period. CONCLUSIONS: Compared to MT, FESWT mainly reduced the ODI score rather than pain after 1 week of treatment. After 2 weeks, the effect of FESWT in relieving the pain was inferior to the MT.

Screening and Identifying Reference Genes for Erythrocyte Production from Cord Blood CD34+ Cells Exposed to Hypoxia.

Cord blood (CB) CD34+ cells have the potential to be used to achieve artificial hematopoiesis because of their ability to expand and differentiate in multiple directions. However, the mechanism and molecular changes underlying such differentiation are still unclear. The differentiation of CB CD34+ cells is generally driven by subtle changes in gene expression. A crucial method for examining gene expression is quantitative real-time polymerase chain reaction, but the accuracy of the results is dependent on the use of reliable reference genes. Here, the transcription levels of 10 novel candidate reference genes (EIF4G2, DYNC1H1, LUC7L3, CD46, POLR1D, WSB1, GAPVD1, HGS, LGALS8, and RBM5) and 8 traditional reference genes (GAPDH, YWHAZ, ACTB, B2MG, TBP, HMBS, PPIA, HPRT1) in CB CD34+ cells under different oxygen concentrations were screened and evaluated by using the geNorm and NormFinder algorithms. Comprehensive analysis conducted by RefFinder online tool showed that TBP (a traditional reference gene) and EIF4G2 (a novel reference gene) had the most stable expression, whereas GAPDH and HMBS were the least suitable reference genes under these conditions. These results may serve as a basis for selecting reference genes with stable expression for more accurate normalization under different oxygen concentration stimulation during CB CD34+ cells differentiation.

Ultrasound radiomics-based nomogram to predict lymphovascular invasion in invasive breast cancer: a multicenter, retrospective study.

OBJECTIVES: To develop and validate an ultrasound (US) radiomics-based nomogram for the preoperative prediction of the lymphovascular invasion (LVI) status in patients with invasive breast cancer (IBC). MATERIALS AND METHODS: In this multicentre, retrospective study, 456 consecutive women were enrolled from three institutions. Institutions 1 and 2 were used to train (n = 320) and test (n = 136), and 130 patients from institution 3 were used for external validation. Radiomics features that reflected tumour information were derived from grey-scale US images. The least absolute shrinkage and selection operator and the maximum relevance minimum redundancy (mRMR) algorithm were used for feature selection and radiomics signature (RS) building. US radiomics-based nomogram was constructed by using multivariable logistic regression analysis. Predictive performance was assessed with the receiving operating characteristic curve, discrimination, and calibration. RESULTS: The nomogram based on clinico-ultrasonic features (menopausal status, US-reported lymph node status, posterior echo features) and RS yielded an optimal AUC of 0.88 (95% confidence interval [CI], 0.84-0.91), 0.89 (95% CI, 0.84-0.94) and 0.95 (95% CI, 0.92-0.99) in the training, internal and external validation cohort. The nomogram outperformed the clinico-ultrasonic and RS model (p < 0.05). The nomogram performed favourable discrimination (C-index, 0.88; 95% CI: 0.84-0.91) and was confirmed in the validation (0.88 for internal, 0.95 for external) cohorts. The calibration and decision curve demonstrated the nomogram showed good calibration and was clinically useful. CONCLUSIONS: The radiomics nomogram incorporated in the RS and US and the clinical findings exhibited favourable preoperative individualised prediction of LVI. CLINICAL RELEVANCE STATEMENT: The US radiomics-based nomogram incorporating menopausal status, posterior echo features, US reported-ALN status, and radiomics signature has the potential to predict lymphovascular invasion in patients with invasive breast cancer. KEY POINTS: • The clinico-ultrsonic model of menopausal status, posterior echo features, and US-reported ALN status achieved a better predictive efficacy for LVI than either of them alone. • The radiomics nomogram showed optimal prediction in predicting LVI from patients with IBC (ROC, 0.88 and 0.89 in the training and validation sets). • A nomogram demonstrated favourable performance (area under the receiver operating characteristic curve, 0.95) and well calibration (C-index, 0.95) in an independent validation cohort (n = 130).

FBXO7 Confers Mesenchymal Properties and Chemoresistance in Glioblastoma by Controlling Rbfox2-Mediated Alternative Splicing.

Mesenchymal glioblastoma (GBM) is highly resistant to radio-and chemotherapy and correlates with worse survival outcomes in GBM patients; however, the underlying mechanism determining the mesenchymal phenotype remains largely unclear. Herein, it is revealed that FBXO7, a substrate-recognition component of the SCF complex implicated in the pathogenesis of Parkinson's disease, confers mesenchymal properties and chemoresistance in GBM by controlling Rbfox2-mediated alternative splicing. Specifically, FBXO7 ubiquitinates Rbfox2 Lys249 through K63-linked ubiquitin chains upon arginine dimethylation at Arg341 and Arg441 by PRMT5, leading to Rbfox2 stabilization. FBXO7 controls Rbfox2-mediated splicing of mesenchymal genes, including FoxM1, Mta1, and Postn. FBXO7-induced exon Va inclusion of FoxM1 promotes FoxM1 phosphorylation by MEK1 and nuclear translocation, thereby upregulates CD44, CD9, and ID1 levels, resulting in GBM stem cell self-renewal and mesenchymal transformation. Moreover, FBXO7 is stabilized by temozolomide, and FBXO7 depletion sensitizes tumor xenografts in mice to chemotherapy. The findings demonstrate that the FBXO7-Rbfox2 axis-mediated splicing contributes to mesenchymal transformation and tumorigenesis, and targeting FBXO7 represents a potential strategy for GBM treatment.

Mometasone Furoate Inhibits the Progression of Head and Neck Squamous Cell Carcinoma via Regulating Protein Tyrosine Phosphatase Non-Receptor Type 11.

Mometasone furoate (MF) is a kind of glucocorticoid with extensive pharmacological actions, including inhibiting tumor progression; however, the role of MF in head and neck squamous cell carcinoma (HNSCC) is still unclear. This study aimed to evaluate the inhibitory effect of MF against HNSCC and investigate its underlying mechanisms. Cell viability, colony formation, cell cycle and cell apoptosis were analyzed to explore the effect of MF on HNSCC cells. A xenograft study model was used to investigate the effect of MF on HNSCC in vivo. The core targets of MF for HNSCC were identified using network pharmacology analysis, TCGA database analysis and real-time PCR. Molecular docking was performed to determine the binding energy. Protein tyrosine phosphatase non-receptor type 11 (PTPN11)-overexpressing cells were constructed, and then, the cell viability and the expression levels of proliferation- and apoptosis-related proteins were detected after treatment with MF to explore the role of PTPN11 in the inhibitory effect of MF against HNSCC. After cells were treated with MF, cell viability and the number of colonies were decreased, the cell cycle was arrested and cell apoptosis was increased. The xenograft study results showed that MF could inhibit cell proliferation via promoting cell apoptosis in vivo. PTPN11 was shown to be the core target of MF against HNSCC via network pharmacology analysis, TCGA database analysis and real-time PCR. The molecular docking results revealed that PTPN11 exhibited the strongest ability to bind to MF. Finally, MF could attenuate the effects of increased cell viability and decreased cell apoptosis caused by PTPN11 overexpression, suggesting that MF can inhibit the progression of HNSCC by regulating PTPN11. MF targeted PTPN11, promoting cell cycle arrest and cell apoptosis, and consequently exerting effective anti-tumor activity.

Lymph node metastases in breast cancer: Mechanisms and molecular imaging.

Breast cancer is the most common malignant disease of women in the world. Breast cancer often metastasizes to axillary lymph nodes. Accurate assessment of the status of axillary lymph nodes is crucial to the staging and treatment of breast cancer. None of the methods used clinically for preoperative noninvasive examination of axillary lymph nodes can accurately identify cancer cells from a molecular level. In recent years, with the in-depth study of lymph node metastases, the mechanisms and molecular imaging of lymph node metastases in breast cancer have been reported. In this review, we highlight the new progress in the study of the main mechanisms of lymph node metastases in breast cancer. In addition, we analyze the advantages and disadvantages of traditional preoperative axillary lymph node imaging methods for breast cancer, and list molecular imaging methods that can accurately identify breast cancer cells in lymph nodes.

Comprehensive bioinformatics analysis and experimental validation: An anoikis-related gene prognostic model for targeted drug development in head and neck squamous cell carcinoma.

We analyzed RNA-sequencing (RNA-seq) and clinical data from head and neck squamous cell carcinoma (HNSCC) patients in The Cancer Genome Atlas (TCGA) Genomic Data Commons (GDC) portal to investigate the prognostic value of anoikis-related genes (ARGs) in HNSCC and develop new targeted drugs. Differentially expressed ARGs were screened using bioinformatics methods; subsequently, a prognostic model including three ARGs (CDKN2A, BIRC5, and PLAU) was constructed. Our results showed that the model-based risk score was a good prognostic indicator, and the potential of the three ARGs in HNSCC prognosis was validated by the TISCH database, the model's accuracy was validated in two independent cohorts of the Gene Expression Omnibus database. Immune correlation analysis and half-maximal inhibitory concentration were also performed to reveal the different landscapes of TIME between risk groups and to predict immuno- and chemo-therapeutic responses. Potential small-molecule drugs for HNSCC were subsequently predicted using the L1000FWD database. Finally, in vitro experiments were used to verify the database findings. The relative ARG mRNA expression levels in HNSCC and surrounding normal tissues remained consistent with the model results. BIRC5 knockdown inhibited anoikis resistance in WSU-HN6 and CAL-27 cells. Molecular docking, real-time PCR, cell counting kit-8 (CCK-8), plate clone, and flow cytometry analyses showed that small-molecule drugs predicted by the database may target the ARGs in the prognostic model, inhibit HNSCC cells survival rate, and promote anoikis in vitro. Therefore, we constructed a new ARG model for HNSCC patients that can predict prognosis and immune activity and identify a potential small-molecule drug for HNSCC, paving the way for clinically targeting anoikis in HNSCC.

SEC61G assists EGFR-amplified glioblastoma to evade immune elimination.

Amplification of chromosome 7p11 (7p11) is the most common alteration in primary glioblastoma (GBM), resulting in gains of epidermal growth factor receptor (EGFR) copy number in 50 to 60% of GBM tumors. However, treatment strategies targeting EGFR have thus far failed in clinical trials, and the underlying mechanism remains largely unclear. We here demonstrate that EGFR amplification at the 7p11 locus frequently encompasses its neighboring genes and identifies SEC61G as a critical regulator facilitating GBM immune evasion and tumor growth. We found that SEC61G is always coamplified with EGFR and is highly expressed in GBM. As an essential subunit of the SEC61 translocon complex, SEC61G promotes translocation of newly translated immune checkpoint ligands (ICLs, including PD-L1, PVR, and PD-L2) into the endoplasmic reticulum and promotes their glycosylation, stabilization, and membrane presentation. Depletion of SEC61G promotes the infiltration and cytolytic activity of CD8+ T cells and thus inhibits GBM occurrence. Further, SEC61G inhibition augments the therapeutic efficiency of EGFR tyrosine kinase inhibitors in mice. Our study demonstrates a critical role of SEC61G in GBM immune evasion, which provides a compelling rationale for combination therapy of EGFR-amplified GBMs.

An integrated radiomics nomogram based on conventional ultrasound improves discriminability between fibroadenoma and pure mucinous carcinoma in breast.

Objective: To evaluate the ability of integrated radiomics nomogram based on ultrasound images to distinguish between breast fibroadenoma (FA) and pure mucinous carcinoma (P-MC). Methods: One hundred seventy patients with FA or P-MC (120 in the training set and 50 in the test set) with definite pathological confirmation were retrospectively enrolled. Four hundred sixty-four radiomics features were extracted from conventional ultrasound (CUS) images, and radiomics score (Radscore) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different models were developed by a support vector machine (SVM), and the diagnostic performance of the different models was assessed and validated. A comparison of the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) was performed to evaluate the incremental value of the different models. Results: Finally, 11 radiomics features were selected, and then Radscore was developed based on them, which was higher in P-MC in both cohorts. In the test group, the clinic + CUS + radiomics (Clin + CUS + Radscore) model achieved a significantly higher area under the curve (AUC) value (AUC = 0.86, 95% CI, 0.733-0.942) when compared with the clinic + radiomics (Clin + Radscore) (AUC = 0.76, 95% CI, 0.618-0.869, P > 0.05), clinic + CUS (Clin + CUS) (AUC = 0.76, 95% CI, 0.618-0.869, P< 0.05), Clin (AUC = 0.74, 95% CI, 0.600-0.854, P< 0.05), and Radscore (AUC = 0.64, 95% CI, 0.492-0.771, P< 0.05) models, respectively. The calibration curve and DCA also suggested excellent clinical value of the combined nomogram. Conclusion: The combined Clin + CUS + Radscore model may help improve the differentiation of FA from P-MC.

Regulation of erythroid differentiation in K562 cells by the EPAS1-IRS2 axis under hypoxic conditions.

Red blood cells (RBCs) produced in vitro have the potential to alleviate the worldwide demand for blood transfusion. Hematopoietic cell differentiation and proliferation are triggered by numerous cellular physiological processes, including low oxygen concentration (<5%). In addition, hypoxia inducible factor 2α (HIF-2α) and insulin receptor substrate 2 (IRS2) were found to be involved in the progression of erythroid differentiation. However, the function of the HIF-2α-IRS2 axis in the progression of erythropoiesis is not yet fully understood. Therefore, we used an in vitro model of erythropoiesis generated from K562 cells transduced with shEPAS1 at 5% O2 in the presence or absence of the IRS2 inhibitor NT157. We observed that erythroid differentiation was accelerated in K562 cells by hypoxia. Conversely, knockdown of EPAS1 expression reduced IRS2 expression and erythroid differentiation. Intriguingly, inhibition of IRS2 could impair the progression of hypoxia-induced erythropoiesis without affecting EPAS1 expression. These findings indicated that the EPAS1-IRS2 axis may be a crucial pathway that regulates erythropoiesis and that drugs targeting this pathway may become promising agents for promoting erythroid differentiation.

Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.

OBJECTIVES: To investigate the predictive performance of the deep learning radiomics (DLR) model integrating pretreatment ultrasound imaging features and clinical characteristics for evaluating therapeutic response after neoadjuvant chemotherapy (NAC) in patients with breast cancer. METHODS: A total of 603 patients who underwent NAC were retrospectively included between January 2018 and June 2021 from three different institutions. Four different deep convolutional neural networks (DCNNs) were trained by pretreatment ultrasound images using annotated training dataset (n = 420) and validated in a testing cohort (n = 183). Comparing the predictive performance of these models, the best one was selected for image-only model structure. Furthermore, the integrated DLR model was constructed based on the image-only model combined with independent clinical-pathologic variables. Areas under the curve (AUCs) of these models and two radiologists were compared by using the DeLong method. RESULTS: As the optimal basic model, Resnet50 achieved an AUC and accuracy of 0.879 and 82.5% in the validation set. The integrated DLR model, yielding the highest classification performance in predicting response to NAC (AUC 0.962 and 0.939 in the training and validation cohort), outperformed the image-only model and the clinical model and also performed better than two radiologists' prediction (all p < 0.05). In addition, predictive efficacy of the radiologists was improved under the assistance of the DLR model significantly. CONCLUSION: The pretreatment US-based DLR model could hold promise as a clinical guidance for predicting NAC response of patients with breast cancer, thereby providing benefit of timely treatment strategy adjustment to potential poor NAC responders. KEY POINTS: • Multicenter retrospective study showed that deep learning radiomics (DLR) model based on pretreatment ultrasound image and clinical parameter achieved satisfactory prediction of tumor response to neoadjuvant chemotherapy (NAC) in breast cancer. • The integrated DLR model could become an effective tool to guide clinicians in identifying potential poor pathological responders before chemotherapy. • The predictive efficacy of the radiologists was improved under the assistance of the DLR model.

Combining primary tumor features derived from conventional and contrast-enhanced ultrasound facilitates the prediction of positive axillary lymph nodes in Breast Imaging Reporting and Data System category 4 malignant breast lesions.

PURPOSE: To determine whether the primary tumor features derived from conventional ultrasound (US) and contrast-enhanced US (CEUS) facilitate the prediction of positive axillary lymph nodes (ALNs) in breast cancer diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4. METHODS: A total of 240 women with breast cancer who underwent preoperative conventional US, strain elastography, and CEUS between September 2016 and December 2019 were included. The multiple parameters of the primary tumor were obtained, and univariate and multivariate analyses were performed to predict positive ALNs. Then three prediction models (conventional US features, CEUS features, and the combined features) were developed, and the diagnostic performance was evaluated with receiver operating characteristic curves. RESULTS: On conventional US, the traits of large size and the non-circumscribed margin of the primary tumor were marked as two independent predictors. On CEUS, the features of vessel perforation or distortion and the enhanced range of the primary tumor were marked as two independent predictors for positive ALNs. Three prediction models were then developed: model A (conventional US features), model B (CEUS features), and model C (model A plus B). Model C yielded the highest area under the curve (AUC) of 0.82 [95% confidence interval (CI), 0.75-0.88] compared with model A (AUC 0.74; 95% CI, 0.68-0.81; P = 0.008) and model B (AUC 0.72; 95% CI, 0.65-0.80; P < 0.001) as per the DeLong test. CONCLUSION: CEUS, as a non-invasive examination technique, can be used to predict ALN metastasis. Combining conventional US and CEUS may produce favorable predictive accuracy for positive ALNs in BI-RADS category 4 breast cancer.

Construction and validation of a personalized nomogram of ultrasound for pretreatment prediction of breast cancer patients sensitive to neoadjuvant chemotherapy.

OBJECTIVE: To construct a combined radiomics model based on pre-treatment ultrasound for predicting of advanced breast cancers sensitive to neoadjuvant chemotherapy (NAC). METHODS: A total of 288 eligible breast cancer patients who underwent NAC before surgery were enrolled in the retrospective study cohort. Radiomics features reflecting the phenotype of the pre-NAC tumors were extracted. With features selected using the least absolute shrinkage and selection operator (LASSO) regression, radiomics signature (Rad-score) was established based on the pre-NAC ultrasound. Then, radiomics nomogram of ultrasound (RU) was established on the basis of the best radiomic signature incorporating independent clinical features. The performance of RU was evaluated in terms of calibration curve, area under the curve (AUC), and decision curve analysis (DCA). RESULTS: Nine features were selected to construct the radiomics signature in the training cohort. Combined with independent clinical characteristics, the performance of RU for identifying Grade 4-5 patients was significantly superior than the clinical model and Rad-score alone (p < 0.05, as per the Delong test), which achieved an AUC of 0.863 (95% CI, 0.814-0.963) in the training group and 0.854 (95% CI, 0.776-0.931) in the validation group. DCA showed that this model satisfactory clinical utility, suggesting its robustness as a response predictor. CONCLUSION: This study demonstrated that RU has a potential role in predicting drug-sensitive breast cancers. ADVANCES IN KNOWLEDGE: Aiming at early detection of Grade 4-5 breast cancer patients, the radiomics nomogram based on ultrasound has been approved as a promising indicator with high clinical utility. It is the first application of ultrasound-based radiomics nomogram to distinguish drug-sensitive breast cancers.

Potential prognostic value of a eight ferroptosis-related lncRNAs model and the correlative immune activity in oral squamous cell carcinoma.

BACKGROUND: To investigate the prognostic value of ferroptosis-related long noncoding RNAs (lncRNAs) in oral squamous cell carcinoma (OSCC) and to construct a prognostic risk and immune activity model. METHODS: We obtained clinical and RNA-seq information on OSCC patient data in The Cancer Genome Atlas (TCGA) Genome Data Sharing (GDC) portal. Through a combination of a differential analysis, Pearson correlation analysis and Cox regression analysis, ferroptosis-related lncRNAs were identified, and a prognostic model was established based on these ferroptosis-related lncRNAs. The accuracy of the model was evaluated via analyses based on survival curves, receiver operating characteristic (ROC) curves, and clinical decision curve analysis (DCA). Univariate Cox and multivariate Cox regression analyses were performed to evaluate independent prognostic factors. Then, the infiltration and functional enrichment of immune cells in high- and low-risk groups were compared. Finally, certain small-molecule drugs that potentially target OSCC were predicted via use of the L1000FWD database. RESULTS: The prognostic model included 8 ferroptosis-related lncRNAs (FIRRE, LINC01305, AC099850.3, AL512274.1, AC090246.1, MIAT, AC079921.2 and LINC00524). The area under the ROC curve (AUC) was 0.726. The DCA revealed that the risk score based on the prognostic model was a better prognostic indicator than other clinical indicators. The multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for OSCC. There were differences in immune cell infiltration, immune functions, m6A-related gene expression levels, and signal pathway enrichment between the high- and low-risk groups. Subsequently, several small-molecule drugs were predicted for use against differentially expressed ferroptosis-related genes in OSCC. CONCLUSIONS: We constructed a new prognostic model of OSCC based on ferroptosis-related lncRNAs. The model is valuable for prognostic prediction and immune evaluation, laying a foundation for the study of ferroptosis-related lncRNAs in OSCC.

Optimization of cancer immunotherapy through pyroptosis: A pyroptosis-related signature predicts survival benefit and potential synergy for immunotherapy in glioma.

Background: Pyroptosis is a critical type of programmed cell death that is strongly associated with the regulation of tumor and immune cell functions. However, the role of pyroptosis in tumor progression and remodeling of the tumor microenvironment in gliomas has not been extensively studied. Thus, in this study, we aimed to establish a comprehensive pyroptosis-related signature and uncover its potential clinical application in gliomas. Methods: The TCGA glioma cohort was obtained and divided into training and internal validation cohorts, while the CGGA glioma cohort was used as an external validation cohort. Unsupervised consensus clustering was performed to identify pyroptosis-related expression patterns. A Cox regression analysis was performed to establish a pyroptosis-related risk signature. Real-time quantitative PCR was performed to analyze the expression of signature genes in glioma tissues. Immune infiltration was analyzed and validated by immunohistochemical staining. The expression patterns of signature genes in different cell types were analyzed using single-cell RNA sequencing data. Finally, therapeutic responses to chemotherapy, immunotherapy, and potential small-molecule inhibitors were investigated. Results: Patients with glioma were stratified into clusters 1 and 2 based on the expression patterns of pyroptosis-related genes. Cluster 2 showed a longer overall (P<0.001) and progression-free survival time (P<0.001) than Cluster 1. CD8+ T cell enrichment was observed in Cluster 1. A pyroptosis-related risk signature (PRRS) was then established. The high PRRS group showed a significantly poorer prognosis than the low PRRS group in the training cohort (P<0.001), with validation in the internal and external validation cohorts. Immunohistochemical staining demonstrated that CD8+ T cells were enriched in high PRRS glioma tissues. PRRS genes also showed cell-specific expression in tumor and immune cells. Moreover, the high PRRS risk group showed higher temozolomide sensitivity and increased response to anti-PD1 treatment in a glioblastoma immunotherapy cohort. Finally, Bcl-2 inhibitors were screened as candidates for adjunct immunotherapy of gliomas. Conclusion: The pyroptosis-related signature established in this study can be used to reliably predict clinical outcomes and immunotherapy responses in glioma patients. The correlation between the pyroptosis signature and the tumor immune microenvironment may be used to further guide the sensitization of glioma patients to immunotherapy.