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BACKGROUND: The tumor burden within the axillary lymph nodes (ALNs) constitutes a pivotal factor in breast cancer, serving as the primary determinant for treatment decisions and exhibiting a close correlation with prognosis. OBJECTIVE: This study aimed to investigate the potential of ultrasound-based radiomics and clinical characteristics in non-invasively distinguishing between low tumor burden (1-2 positive nodes) and high tumor burden (more than 2 positive nodes) in patients with node-positive breast cancer. METHODS: A total of 215 patients with node-positive breast cancer, who underwent preoperative ultrasound examinations, were enrolled in this study. Among these patients, 144 cases were allocated to the training set, 37 cases to the validation set, and 34 cases to the testing set. Postoperative histopathology was used to determine the status of ALN tumor burden. The region of interest for breast cancer was delineated on the ultrasound image. Nine models were developed to predict high ALN tumor burden, employing a combination of three feature screening methods and three machine learning classifiers. Ultimately, the optimal model was selected and tested on both the validation and testing sets. In addition, clinical characteristics were screened to develop a clinical model. Furthermore, Shapley additive explanations (SHAP) values were utilized to provide explanations for the machine learning model. RESULTS: During the validation and testing sets, the models demonstrated area under the curve (AUC) values ranging from 0.577 to 0.733 and 0.583 to 0.719, and accuracies ranging from 64.9% to 75.7% and 64.7% to 70.6%, respectively. Ultimately, the Boruta_XGB model, comprising five radiomics features, was selected as the final model. The AUC values of this model for distinguishing low from high tumor burden were 0.828, 0.715, and 0.719 in the training, validation, and testing sets, respectively, demonstrating its superiority over the clinical model. CONCLUSIONS: The developed radiomics models exhibited a significant level of predictive performance. The Boruta_XGB radiomics model outperformed other radiomics models in this study.
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Axila , Neoplasias da Mama , Linfonodos , Metástase Linfática , Carga Tumoral , Ultrassonografia , Humanos , Feminino , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Pessoa de Meia-Idade , Axila/diagnóstico por imagem , Linfonodos/diagnóstico por imagem , Linfonodos/patologia , Metástase Linfática/diagnóstico por imagem , Adulto , Ultrassonografia/métodos , Idoso , Aprendizado de Máquina , Valor Preditivo dos Testes , RadiômicaRESUMO
The hormone receptor (HR) status plays a significant role in breast cancer, serving as the primary guide for treatment decisions and closely correlating with prognosis. This study aims to investigate the predictive value of radiomics analysis in long-axis and short-axis ultrasound planes for distinguishing between HR-positive and HR-negative breast cancers. A cohort of 505 patients from two hospitals was stratified into discovery (Institute 1, 416 patients) and validation (Institute 2, 89 patients) cohorts. A comprehensive set of 788 ultrasound radiomics features was extracted from both long-axis and short-axis ultrasound planes, respectively. Utilizing least absolute shrinkage and selection operator (LASSO) regression analysis, distinct models were constructed for the long-axis and short-axis data. Subsequently, radiomics scores (Rad-scores) were computed for each patient. Additionally, a combined model was formulated by integrating data from long-axis and short-axis Rad-scores along with clinical factors. The diagnostic efficacy of all models was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The long-axis and short-axis models, consisting of 11 features and 15 features, respectively, were established, yielding AUCs of 0.743 and 0.751 in the discovery cohort, and 0.795 and 0.744 in the validation cohort. The calculated long-axis and short-axis Rad-scores exhibited significant differences between HR-positive and HR-negative groups across all cohorts (all p < 0.001). Univariate analysis identified ultrasound-reported tumor size as an independent predictor. The combined model, incorporating long-axis and short-axis Rad-scores along with tumor size, achieved superior AUCs of 0.788 and 0.822 in the discovery and validation cohorts, respectively. The combined model effectively distinguishes between HR-positive and HR-negative breast cancers based on ultrasound radiomics features and tumor size, which may offer a valuable tool to facilitate treatment decision making and prognostic assessment.
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Neoplasias da Mama , Radiômica , Adulto , Idoso , Feminino , Humanos , Pessoa de Meia-Idade , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/metabolismo , Neoplasias da Mama/patologia , Prognóstico , Receptor ErbB-2/metabolismo , Receptores de Estrogênio/metabolismo , Receptores de Progesterona/metabolismo , Curva ROC , Ultrassonografia Mamária/métodosRESUMO
Rationale and Objectives: We aimed to develop and validate prediction models for histological grade of invasive breast carcinoma (BC) based on ultrasound radiomics features and clinical characteristics. Materials and Methods: A number of 383 patients with invasive BC were retrospectively enrolled and divided into a training set (207 patients), internal validation set (90 patients), and external validation set (86 patients). Ultrasound radiomics features were extracted from all the eligible patients. The Boruta method was used to identify the most useful features. Seven classifiers were adopted to developed prediction models. The output of the classifier with best performance was labeled as the radiomics score (Rad-score) and the classifier was selected as the Rad-score model. A combined model combining clinical factors and Rad-score was developed. The performance of the models was evaluated using receiver operating characteristic curve. Results: Seven radiomics features were selected from 788 candidate features. The logistic regression model performing best among the 7 classifiers in the internal and external validation sets was considered as Rad-score model, with areas under the receiver operating characteristic curve (AUC) values of 0.731 and 0.738. The tumor size was screened out as the risk factor and the combined model was developed, with AUC values of 0.721 and 0.737 in the internal and external validation sets. Furthermore, the 10-fold cross-validation demonstrated that the 2 models above were reliable and stable. Conclusion: The Rad-score model and combined model were able to predict histological grade of invasive BC, which may enable tailored therapeutic strategies for patients with BC in routine clinical use.
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Neoplasias da Mama , Gradação de Tumores , Curva ROC , Humanos , Feminino , Neoplasias da Mama/patologia , Neoplasias da Mama/diagnóstico por imagem , Pessoa de Meia-Idade , Adulto , Idoso , Estudos Retrospectivos , Ultrassonografia/métodos , Invasividade Neoplásica , Ultrassonografia Mamária/métodos , RadiômicaRESUMO
ABSTRACT: Adult animals are unable to regenerate heart cells due to postnatal cardiomyocyte cycle arrest, leading to higher mortality rates in cardiomyopathy. However, reprogramming of energy metabolism in cardiomyocytes provides a new perspective on the contribution of glycolysis to repair, regeneration, and fibrosis after cardiac injury. Pyruvate kinase (PK) is a key enzyme in the glycolysis process. This review focuses on the glycolysis function of PKM2, although PKM1 and PKM2 both play significant roles in the process after cardiac injury. PKM2 exists in both low-activity dimer and high-activity tetramer forms. PKM2 dimers promote aerobic glycolysis but have low catalytic activity, leading to the accumulation of glycolytic intermediates. These intermediates enter the pentose phosphate pathway to promote cardiomyocyte proliferation and heart regeneration. Additionally, they activate adenosine triphosphate (ATP)-sensitive K + (K ATP ) channels, protecting the heart against ischemic damage. PKM2 tetramers function similar to PKM1 in glycolysis, promoting pyruvate oxidation and subsequently ATP generation to protect the heart from ischemic damage. They also activate KDM5 through the accumulation of αKG, thereby promoting cardiomyocyte proliferation and cardiac regeneration. Apart from glycolysis, PKM2 interacts with transcription factors like Jmjd4, RAC1, ß-catenin, and hypoxia-inducible factor (HIF)-1α, playing various roles in homeostasis maintenance, remodeling, survival regulation, and neovascularization promotion. However, PKM2 has also been implicated in promoting cardiac fibrosis through mechanisms like sirtuin (SIRT) 3 deletion, TG2 expression enhancement, and activation of transforming growth factor-ß1 (TGF-ß1)/Smad2/3 and Jak2/Stat3 signals. Overall, PKM2 shows promising potential as a therapeutic target for promoting cardiomyocyte proliferation and cardiac regeneration and addressing cardiac fibrosis after injury.
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Fibrose , Glicólise , Miócitos Cardíacos , Piruvato Quinase , Transdução de Sinais , Animais , Humanos , Piruvato Quinase/metabolismo , Piruvato Quinase/genética , Miócitos Cardíacos/enzimologia , Miócitos Cardíacos/patologia , Miócitos Cardíacos/metabolismo , Metabolismo Energético , Proliferação de Células , Regeneração , Cardiomiopatias/enzimologia , Cardiomiopatias/patologia , Cardiomiopatias/metabolismo , Cardiomiopatias/genética , Cardiomiopatias/fisiopatologiaRESUMO
Aptamers, as a kind of small-molecule nucleic acid, have attracted much attention since their discovery. Compared with biological reagents such as antibodies, aptamers have the advantages of small molecular weight, low immunogenicity, low cost, and easy modification. At present, aptamers are mainly used in disease biomarker discovery, disease diagnosis, treatment, and targeted drug delivery vectors. In the process of screening and optimizing aptamers, it is found that there are still many problems need to be solved such as the design of the library, optimization of screening conditions, the truncation of screened aptamer, and the stability and toxicity of the aptamer. In recent years, the incidence of liver-related diseases is increasing year by year and the treatment measures are relatively lacking, which has attracted the people's attention in the application of aptamers in liver diseases. This article mainly summarizes the research status of aptamers in disease diagnosis and treatment, especially focusing on the application of aptamers in liver diseases, showing the crucial significance of aptamers in the diagnosis and treatment of liver diseases, and the use of Discovery Studio software to find the binding target and sequence of aptamers, and explore their possible interaction sites.
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Aptâmeros de Nucleotídeos , Hepatopatias , Técnica de Seleção de Aptâmeros , Humanos , Hepatopatias/terapia , Hepatopatias/genética , Hepatopatias/diagnóstico , Técnica de Seleção de Aptâmeros/métodos , Animais , BiomarcadoresRESUMO
OBJECTIVE: To explore the underlying mechanism of inhibition by Jinkui Shenqi Pills (JKSQP) on glucocorticoid-enhanced axial length elongation in experimental lens-induced myopia (LIM) guinea pigs. METHODS: Sixty 2-week old male guinea pigs were randomly divided into 4 groups with 15 guinea pigs in each group, according to the random numbers generated by SPSS software: control, LIM, saline and JKSQP groups. The control group includes animals with no treatment, while the guinea pigs in the other 3 groups received lens-induced myopization on the right eyes throughout the experiment (for 8 weeks). The saline and JKSQP groups were given daily intraperitoneal injections of 10 mg/kg hydrocortisone for 2 consecutive weeks at the same time, and then orally administered either saline or JKSQP [13.5 g/(kgâ¢d) for 6 consecutive weeks. Body weight, anal temperature and animal appearance were observed and recorded to evaluate the GC-associated symptoms. The ocular parameters, including refraction and axial length, were measured by streak retinoscopy and A-scan ultrasonography, respectively. The levels of plasma hormones associated with the hypothalamic-pituitary-adrenal axis (HPAA), including free triiodothyronine, free thyroxine, estradiol and testosterone, were measured by radioimmunoassay, and cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate were measured by enzyme-linked immunosorbent assay. In addition, the mRNA and protein expressions of retinal amphiregulin (AREG) was measured by quantitative real-time polymerase chain reaction and Western blotting, respectively. RESULTS: JKSQP effectively increased body weight and anal temperature, improved animal appearance and suppressed axial length elongation in glucocorticoid-enhanced myopic guinea pigs with normalization of 4 HPAA-associated plasma hormones (all P<0.05). The plasma level of cAMP was significantly increased, whereas the plasma level of cGMP and the mRNA and protein expressions of retinal AREG were decreased after treatment with JKSQP (all P<0.05). CONCLUSION: JKSQP exhibited a significant inhibitory effect on axial length elongation with decreased expression of AREG in the retina, and normalized 4 HPAA-associated plasma hormones and the expression of cAMP and cGMP in GC-enhanced myopic guinea pigs.
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Glucocorticoides , Miopia , Cobaias , Masculino , Animais , Sistema Hipotálamo-Hipofisário , Sistema Hipófise-Suprarrenal , Miopia/tratamento farmacológico , Miopia/metabolismo , Peso Corporal , RNA Mensageiro , Modelos Animais de DoençasRESUMO
E26 transformation specific or E twenty-six (ETS) protein family consists of 28 transcription factors, five of which, named ETS1/2, PU.1, ERG and EHF, are known to involve in the development of liver fibrosis, and are expected to become diagnostic markers or therapeutic targets of liver fibrosis. In recent years, some small molecule inhibitors of ETS protein family have been discovered, which might open up a new path for the liver fibrosis therapy targeting ETS. This article reviews the research progress of ETS family members in the development liver fibrosis as well as their prospect of clinical application.
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Proteínas Proto-Oncogênicas , Fatores de Transcrição , Humanos , Fatores de Transcrição/metabolismo , Proteínas Proto-Oncogênicas c-ets , Proteínas Proto-Oncogênicas/metabolismo , Proteína Proto-Oncogênica c-ets-1/metabolismo , Cirrose HepáticaRESUMO
BACKGROUND: Aptamers, consisting of single-stranded DNA or RNA, have secondary and tertiary structures which could bind specifically to target molecules. They are characterized by strong specificity, high affinity, low molecular weight, and low immunogenicity; therefore, the current research focuses on their potential as a targeted drug carrier, a diagnostic probe for diseases, or as a direct therapeutic drug. OBJECTIVE: In this review, how to improve the success rate of adaptor screening and the optimization after screening is described. RESULTS: For aptamer screening, an efficient selection strategy is needed. In this article, by analyzing key aspects of SELEX such as initial library design, screening procedures, truncation and modification after screening, a comprehensive analysis of each step that might meet obstacles in SELEX is provided. CONCLUSION: Aptamers, which possess the specificity and affinity with the target, can serve as targeted drug carriers or biosensors for diagnosing a disease. If the problems in the screening process in cell-SELEX technology, truncation, and modification after screening are solved, it will have a broader range of applications.
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Aptâmeros de Nucleotídeos , Técnica de Seleção de Aptâmeros , Aptâmeros de Nucleotídeos/química , Aptâmeros de Nucleotídeos/farmacologia , Aptâmeros de Nucleotídeos/normas , Técnica de Seleção de Aptâmeros/métodos , Técnica de Seleção de Aptâmeros/normasRESUMO
(1) Objective: To evaluate the performance of ultrasound-based radiomics in the preoperative prediction of human epidermal growth factor receptor 2-positive (HER2+) and HER2- breast carcinoma. (2) Methods: Ultrasound images from 309 patients (86 HER2+ cases and 223 HER2- cases) were retrospectively analyzed, of which 216 patients belonged to the training set and 93 patients assigned to the time-independent validation set. The region of interest of the tumors was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analyses using the intra-class correlation coefficient (ICC), Mann-Whitney U test, and the least absolute shrinkage and selection operator (LASSO) algorithm. The radiomics score (Rad-score) for each patient was calculated through a linear combination of the nonzero coefficient features. The support vector machine (SVM), K nearest neighbors (KNN), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction models based on the Rad-score. A clinical model based on significant clinical features was also established. In addition, the logistic regression method was used to integrate Rad-score and clinical features to generate the nomogram model. The leave-one-out cross validation (LOOCV) method was used to validate the reliability and stability of the model. (3) Results: Among the seven classifier models, the LR achieved the best performance in the validation set, with an area under the receiver operating characteristic curve (AUC) of 0.786, and was obtained as the Rad-score model, while the RF performed the worst. Tumor size showed a statistical difference between the HER2+ and HER2- groups (p = 0.028). The nomogram model had a slightly higher AUC than the Rad-score model (AUC, 0.788 vs. 0.786), but no statistical difference (Delong test, p = 0.919). The LOOCV method yielded a high median AUC of 0.790 in the validation set. (4) Conclusion: The Rad-score model performs best among the seven classifiers. The nomogram model based on Rad-score and tumor size has slightly better predictive performance than the Rad-score model, and it has the potential to be utilized as a routine modality for preoperatively determining HER2 status in BC patients non-invasively.
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Introduction: The molecular subtype plays a significant role in breast carcinoma (BC), which is the main indicator to guide treatment and is closely associated with prognosis. The aim of this study was to investigate the feasibility and efficacy of an ultrasound-based radiomics nomogram in preoperatively discriminating the luminal from non-luminal type in patients with BC. Methods: A total of 264 BC patients who underwent routine ultrasound examination were enrolled in this study, of which 184 patients belonged to the training set and 80 patients to the test set. Breast tumors were delineated manually on the ultrasound images and then radiomics features were extracted. In the training set, the T test and least absolute shrinkage and selection operator (LASSO) were used for selecting features, and the radiomics score (Rad-score) for each patient was calculated. Based on the clinical risk features, Rad-score, and combined clinical risk features and Rad-score, three models were established, respectively. The performances of the models were validated with receiver operator characteristic (ROC) curve and decision curve analysis. Results: In all, 788 radiomics features per case were obtained from the ultrasound images. Through radiomics feature selection, 11 features were selected to constitute the Rad-score. The area under the ROC curve (AUC) of the Rad-score for predicting the luminal type was 0.828 in the training set and 0.786 in the test set. The nomogram comprising the Rad-score and US-reported tumor size showed AUCs of the training and test sets were 0.832 and 0.767, respectively, which were significantly higher than the AUCs of the clinical model in the training and test sets (0.691 and 0.526, respectively). However, there was no significant difference in predictive performance between the Rad-score and nomogram. Conclusion: Both the Rad-score and nomogram can be applied as useful, noninvasive tools for preoperatively discriminating the luminal from non-luminal type in patients with BC. Furthermore, this study might provide a novel technique to evaluate molecular subtypes of BC.
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Objective: The aim of this study was to develop and validate an ultrasound-based radiomics nomogram model by integrating the clinical risk factors and radiomics score (Rad-Score) to predict the Ki-67 status in patients with breast carcinoma. Methods: Ultrasound images of 284 patients (196 high Ki-67 expression and 88 low Ki-67 expression) were retrospectively analyzed, of which 198 patients belonged to the training set and 86 patients to the test set. The region of interest of tumor was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analysis by using the independent sample t test and least absolute shrinkage and selection operator (LASSO) algorithm. The support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction model based on the selected features. The classifier with the highest AUC value was selected to convert the output of the results into the Rad-Score and was regarded as Rad-Score model. In addition, the logistic regression method was used to integrate Rad-Score and clinical risk factors to generate the nomogram model. The leave group out cross-validation (LGOCV) method was performed 200 times to verify the reliability and stability of the nomogram model. Results: Six classifier models were established based on the 15 non-zero coefficient features. Among them, the LR classifier achieved the best performance in the test set, with the area under the receiver operating characteristic curve (AUC) value of 0.786, and was obtained as the Rad-Score model, while the XGB performed the worst (AUC, 0.615). In multivariate analysis, independent risk factor for high Ki-67 status was age (odds ratio [OR] = 0.97, p = 0.04). The nomogram model based on the age and Rad-Score had a slightly higher AUC than that of Rad-Score model (AUC, 0.808 vs. 0.798) in the test set, but no statistical difference (p = 0.144, DeLong test). The LGOCV yielded a median AUC of 0.793 in the test set. Conclusions: This study proposed a convenient, clinically useful ultrasound radiomics nomogram model that can be used for the preoperative individualized prediction of the Ki-67 status in patients with BC.
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Purpose: The clinical practice of elastosonography for the detection of salivary gland tumors is still a controversial issue. The objective of this meta-analysis was to evaluate the effect of elastosonography for the diagnosis of salivary gland tumors and to compare the diagnostic value of elastosonography and conventional ultrasound in the diagnosis of salivary gland tumors. Methods: A comprehensive literature search through PubMed, EMBASE, and Cochrane Library was carried out from inception to November 2021. Two researchers independently extracted the data from the enrolled papers using a standard data extraction form. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated to evaluate the diagnostic performance of elastosonography. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was utilized to evaluate the quality of each included study. Meta-DiSc version 1.4, Review Manager 5.3, and StataSE 15 were used. Results: Sixteen studies with a total of 1105 patients with 1146 lesions were included in this meta-analysis. The pooled sensitivity, specificity, PLR, NLR, and DOR of elastosonography for the differentiation between benign and malignant salivary gland tumors were 0.73 (95%CI, 0.66-0.78), 0.64 (95%CI, 0.61-0.67), 2.83 (95%CI, 1.97-4.07), 0.45 (95%CI, 0.32-0.62), and 9.86 (95%CI, 4.49-21.62), respectively, with an AUC of 0.82. Four studies provided data regarding the conventional ultrasound for the differentiation between benign and malignant salivary gland tumors. The pooled sensitivity, specificity, and DOR were 0.62 (95%CI, 0.50-0.73), 0.93 (95%CI, 0.90-0.96), and 25.07 (95%CI, 4.28-146.65), respectively. The meta-regression and subgroup analyses found that assessment methods were associated with significant heterogeneity, and quantitative or semiquantitative elastosonography performed better than the qualitative one. Conclusions: Elastosonography showed a limited value for diagnosing malignant salivary gland tumors; it could be considered as a supplementary diagnostic technology to conventional ultrasound, and quantitative or semiquantitative elastosonography was superior to the qualitative one.