Search Results (2,143)

The ribosome in eukaryotic cells is a macromolecular complex composed of four ribonucleic acids and over 80 proteins. This organelle facilitates protein synthesis in cells, and its activity is strongly upregulated in human cancers. Immune cells, a variety of cellular stressors and numerous structurally and mechanistically distinct anti-cancer agents have been shown to induce ribosomal RNA degradation in tumour cells in vitro and in vivo—a phenomenon we termed “RNA disruption”. RNA disruption can be quantified in cultured cell lines and patient samples using the RNA disruption assay (RDA). Unlike well-known high-throughput anti-cancer drug sensitivity assays, RDA can distinguish between dying and arrested tumour cells, making it an attractive assay for anti-cancer drug discovery and development. Low tumour RNA disruption during neoadjuvant chemotherapy (as measured using RDA) is strongly associated with residual disease and reduced disease-free survival, making it a potentially valuable chemo-resistance assessment tool. High RNA disruption may also indicate chemo-responsiveness. RDA holds the prospect of being a useful tool to escalate or de-escalate neoadjuvant chemotherapy in cancer patients. Moreover, the assay’s ability to predict treatment outcomes during neoadjuvant chemotherapy may permit its use in adaptive clinical trials and in drug approval by regulatory agencies. This review provides insight into the cellular processes involved in chemotherapy-induced RNA disruption. It also describes the results of clinical studies on tumour RNA disruption in cancer patients and suggests possible approaches that could be considered for the utilization of RDAs in the clinical management of breast cancer patients undergoing current neoadjuvant chemotherapy regimens. Full article

Background: Neoadjuvant chemotherapy (NAC) is the important and effective approach to treat locally advanced breast cancer (LABC). The prediction of response to NAC prior to start is an efficient approach to obtaining perspective about the effectiveness of treatment. The objective of this study is to design a machine learning pipeline to predict tumor response to NAC treatment for patients with LABC using the combination of clinical features and radiomics computed tomography (CT) features. Method: A total of 858 clinical and radiomics CT features were determined for 117 patients with LABC to predict the tumor response to NAC treatment. Since the number of features is greater than the number of samples, dimensionality reduction is an indispensable step. To this end, we proposed a novel hybrid feature selection to not only select top features but also optimize the classifier hyperparameters. This hybrid feature selection has two phases. In the first phase, we applied a filter-based strategy feature selection technique using matrix rank theorem to remove all dependent and redundant features. In the second phase, we applied a genetic algorithm which coupled with the SVM classifier. The genetic algorithm determined the optimum number of features and top features. Performance of the proposed technique was assessed by balanced accuracy, accuracy, area under curve (AUC), and F1-score. This is the binary classification task to predict response to NAC. We consider three models for this study including clinical features, radiomics CT features, and a combination of clinical and radiomics CT features. Results: A total of 117 patients with LABC with a mean age of 52 ± 11 were studied in this study. Of these, 82 patients with LABC were the responder group (response to NAC) and 35 were the non-response group to chemotherapy. The best performance was obtained by the combination of clinical and CT radiomics features with Accuracy = 0.88. Conclusion: The results indicate that the combination of clinical features and CT radiomic features is an effective approach to predict response to NAC treatment for patients with LABC. Full article

Background and Objectives: Breast cancer is a leading cause of cancer-related mortality, particularly in aggressive subtypes such as HER2-positive and triple-negative breast cancer (TNBC). Achieving a pathological complete response (pCR) after neoadjuvant therapy is strongly associated with improved survival outcomes in these subgroups, making the prediction of pCR a clinical priority. Sarcopenia, a progressive loss of skeletal muscle mass and strength, is increasingly recognized in cancer patients and has been linked to chemotherapy toxicity and poorer survival. However, its specific impact on pCR in HER2-positive and TNBC patients remains unclear. This study aimed to evaluate the association between radiologically defined sarcopenia, or a low skeletal muscle index (SMI), and pathological response in these subtypes, and to explore its potential as a predictive biomarker. Materials and Methods: This retrospective study included patients with HER2-positive or TNBC who received neoadjuvant therapy between January 2015 and October 2023. SMI was assessed using pre-treatment positron emission tomography images at the L3 vertebral level, with values < 38.5 cm²/m² considered as low. Univariate and multivariate logistic regression analyses were performed to identify factors associated with pCR. Results: A total of 85 patients were included, with low SMI present in 35 (41.2%). In univariate analysis, clinical stage and low SMI were associated with pCR. However, in the multivariate model, only low SMI remained an independent predictor. Patients without low SMI had higher odds of achieving pCR (odds ratio [OR] 4.13; 95% confidence interval [CI] 1.55–10.95; p = 0.004). Low SMI was also associated with higher rates of treatment-related toxicity (42.9% vs. 20.0%, p = 0.023). Conclusions: Pre-treatment low SMI is strongly associated with lower pCR rates in patients with HER2-positive and TNBC undergoing neoadjuvant therapy. These findings underscore the importance of early identification and management of radiologically defined sarcopenia to optimize treatment response and improve clinical outcomes. Full article

Background/Objectives: Breast cancer is the most commonly diagnosed cancer worldwide, with high rates of distant metastasis. While circulating tumor cells (CTCs) are the disseminatory units of metastasis and are indicative of a poor prognosis, CTC heterogeneity within individual patients, among breast cancer subtypes, and between primary and metastatic tumors within a patient obscures the relationship between CTCs and disease progression. EpCAM, its homolog Trop2, and a pan-Cytokeratin marker were evaluated to determine their contributions to CTC presence and clustering over the study period. We conducted a systematic longitudinal analysis of 51 breast cancer patients during the course of their treatment to deepen our understanding of CTC contributions to breast cancer progression. Methods: 272 total blood samples from 51 metastatic breast cancer (mBC) patients were included in the study. Patients received diverse treatment schedules based on discretion of the practicing oncologist. Patients were monitored from July 2020 to March 2023, with blood samples collected at scheduled care appointments. Nucleated cells were isolated, imaged, and analyzed using Rarecyte^® technology, and statistical analysis was performed in R using the lmerTest and lme4 packages, as well as in Graphpad Prism version 10.4.1. Results: Both classical CTCs (DAPI+, EpCAM+, CK+, CD45– cells) and Trop2+ CTCs were detected in the blood of breast cancer patients. A high degree of correlation was found between CTC biomarkers, and CTC expression of EpCAM, Trop2, and the presence of CD45+ cells, all predicted cluster size, while Pan-CK did not. Furthermore, while analyses of biomarkers by receptor status revealed no significant differences among HR+, HER2+, and TNBC patients, longitudinal analysis found evidence for discrete trajectories of EpCAM, Trop2, and clustering between HR+ and HER2+ cancers after diagnosis of metastasis. Conclusions: Correlation and longitudinal analysis revealed that EpCAM+, Trop2+, and CD45+ cells were predictive of CTC cluster presence and size, and highlighted distinct trajectories of biomarker change over time between HR+ and HER2+ cancers following metastatic diagnosis. Full article

Aim: This study aimed to investigate the effectiveness and safety of whole-body hyperthermia (WBH) in cancer patients, identifying predictive factors for successful treatment (reaching target temperature ≥ 38.5 °C) and assessing adverse effects. Methods: We conducted a retrospective analysis of 397 cancer patients receiving a total of 855 WBH treatment sessions at a single institution between January 2018 and December 2018. Results: A total of 855 WBH treatments were performed on 397 patients (76.6% female; median age 58 years). The most common cancer types included breast cancer (52.4%), followed by prostate cancer (13.1%) and gynecological cancers (10.6%), with 54.7% of patients having metastatic disease. Target temperature was reached in 90.1% (770 of 855) of sessions, with a median treatment time of 202 min and maximum temperature of 40.4 °C. Common side effects included headache (54.9%), skin reactions (11.7%), and cardiac effects (9.4%), with no serious adverse events. Serum creatinine (p = 0.01, OR 0.30, 95% CI: 0.11–0.78) and secale cornutum/galena co-medication during WBH (p < 0.001, OR 0.26 [0.12, 0.54]) emerged as independent predictors of achieving target temperature in multivariate analysis. Both elevated creatinine levels and the use of secale cornutum/galena were associated with an approximately 70% lower probability of achieving the target temperature. Conclusions: WBH demonstrates safety in cancer patients with high success rates in reaching target temperatures. Both elevated creatinine levels and the use of secale cornutum/galena were associated with a lower chance of reaching the target temperature and thus impacting and predicting WBH success. Full article

(1) Background: The presence of metastatic disease significantly increases the risk of venous thromboembolism (VTE) in breast cancer, particularly during chemotherapy. Although not categorized as a highly thrombogenic malignancy, the elevated global prevalence of this cancer places a substantial number of patients at risk of thrombosis, which cannot yet be accurately predicted by validated risk assessment models (RAMs), highlighting the need for a dedicated model. (2) Aim: This study aims to develop a RAM for VTE in newly diagnosed metastatic breast cancer patients enrolled in a prospective, observational, and multicenter study. (3) Methods: A cohort of 189 patients beginning antitumor therapy were enrolled and prospectively monitored for VTE and mortality. Blood samples collected at enrollment were tested for D-dimer, fibrinogen, FVIII, prothrombin fragment 1 + 2 (F1 + 2), and thrombin generation (TG). Competing risk analyses were performed to identify significant predictors. (4) Results: Within one year, the cumulative incidences of VTE and mortality were 7.0% and 12%, respectively. Univariable analysis identified high Ki-67, D-dimer, FVIII, fibrinogen, and TG levels, along with low hemoglobin levels, as independent predictors of VTE. Only Ki-67, fibrinogen, FVIII, and hemoglobin were retained as significant predictors in multivariable analysis. These variables were further examined by multiple linear regression, which revealed Ki-67 and fibrinogen as the most significant parameters. A continuous RAM was then developed based on Ki-67 and fibrinogen (c-statistics 0.78), categorizing patients into low-risk and high-risk groups for VTE (2% vs. 13%; SHR 3.6, p = 0.018). This stratification could not be achieved using currently validated models for VTE risk. (5) Conclusions: We developed an accurate RAM for VTE that enables the identification of metastatic breast cancer patients at high risk for VTE, which supports clinicians in personalized thromboprophylaxis strategies if externally validated. Full article

Breast cancer remains a critical global health challenge, with over 2.1 million new cases annually. This review systematically evaluates recent advancements (2022–2024) in machine and deep learning approaches for breast cancer detection and risk management. Our analysis demonstrates that deep learning models achieve 90–99% accuracy across imaging modalities, with convolutional neural networks showing particular promise in mammography (99.96% accuracy) and ultrasound (100% accuracy) applications. Tabular data models using XGBoost achieve comparable performance (99.12% accuracy) for risk prediction. The study confirms that lifestyle modifications (dietary changes, BMI management, and alcohol reduction) significantly mitigate breast cancer risk. Key findings include the following: (1) hybrid models combining imaging and clinical data enhance early detection, (2) thermal imaging achieves high diagnostic accuracy (97–100% in optimized models) while offering a cost-effective, less hazardous screening option, (3) challenges persist in data variability and model interpretability. These results highlight the need for integrated diagnostic systems combining technological innovations with preventive strategies. The review underscores AI’s transformative potential in breast cancer diagnosis while emphasizing the continued importance of risk factor management. Future research should prioritize multi-modal data integration and clinically interpretable models. Full article

Deep learning has achieved widespread adoption for medical image diagnosis, with extensive research dedicated to mammographic image analysis for breast cancer screening. This study investigates the hypothesis that incorporating region-of-interest (ROI) mask information for individual mammographic images during deep learning can improve the accuracy of benign/malignant diagnoses. Swin Transformer and ConvNeXtV2 deep learning models were used to evaluate their performance on the public VinDr and CDD-CESM datasets. Our approach involved stratifying mammographic images based on the presence or absence of ROI masks, performing independent training and prediction for each subgroup, and subsequently merging the results. Baseline prediction metrics (sensitivity, specificity, F-score, and accuracy) without ROI-stratified separation were the following: VinDr/Swin Transformer (0.00, 1.00, 0.00, 0.85), VinDr/ConvNeXtV2 (0.00, 1.00, 0.00, 0.85), CDD-CESM/Swin Transformer (0.29, 0.68, 0.41, 0.48), and CDD-CESM/ConvNeXtV2 (0.65, 0.65, 0.65, 0.65). Subsequent analysis with ROI-stratified separation demonstrated marked improvements in these metrics: VinDr/Swin Transformer (0.93, 0.87, 0.90, 0.87), VinDr/ConvNeXtV2 (0.90, 0.86, 0.88, 0.87), CDD-CESM/Swin Transformer (0.65, 0.65, 0.65, 0.65), and CDD-CESM/ConvNeXtV2 (0.74, 0.61, 0.67, 0.68). These findings provide compelling evidence that validate our hypothesis and affirm the utility of considering ROI mask information for enhanced diagnostic accuracy in mammography. Full article

Background: Spinal metastasis is the third most common site for metastatic localization, following the lung and liver. Manual detection through imaging modalities such as CT, MRI, PET, and bone scintigraphy can be costly and inefficient. Preliminary artificial intelligence (AI) techniques and computer-aided detection (CAD) systems have attempted to improve lesion detection, segmentation, and treatment response in oncological imaging. The objective of this review is to evaluate the current applications of AI across multimodal imaging techniques in the diagnosis of spinal metastasis. Methods: Databases like PubMed, Scopus, Web of Science Advance, Cochrane, and Embase (Ovid) were searched using specific keywords like ‘spine metastases’, ‘artificial intelligence’, ‘machine learning’, ‘deep learning’, and ‘diagnosis’. The screening of studies adhered to the PRISMA guidelines. Relevant variables were extracted from each of the included articles such as the primary tumor type, cohort size, and prediction model performance metrics: area under the receiver operating curve (AUC), accuracy, sensitivity, specificity, internal validation and external validation. A random-effects meta-analysis model was used to account for variability between the studies. Quality assessment was performed using the PROBAST tool. Results: This review included 39 studies published between 2007 and 2024, encompassing a total of 6267 patients. The three most common primary tumors were lung cancer (56.4%), breast cancer (51.3%), and prostate cancer (41.0%). Four studies reported AUC values for model training, 16 for internal validation, and five for external validation. The weighted average AUCs were 0.971 (training), 0.947 (internal validation), and 0.819 (external validation). The risk of bias was the highest in the analysis domain, with 22 studies (56%) rated high risk, primarily due to inadequate external validation and overfitting. Conclusions: AI-based approaches show promise for enhancing the detection, segmentation, and characterization of spinal metastatic lesions across multiple imaging modalities. Future research should focus on developing more generalizable models through larger and more diverse training datasets, integrating clinical and imaging data, and conducting prospective validation studies to demonstrate meaningful clinical impact. Full article

(1) Background: Response to neoadjuvant chemotherapy (NAC) is a key prognostic indicator in breast cancer. However, current response evaluation methods rely on histopathological assessment after surgery, delaying opportunities for early treatment adaptation. This study aimed to develop a machine learning model by integrating radiomic features extracted from pre-treatment, contrast-enhanced computed tomography (CT) images with baseline clinical variables to predict NAC response before therapy initiation. (2) Methods: The study investigated two categories of response: (i) pathologic complete response (pCR) versus non-pCR, and (ii) clinical response versus non-response, where clinical response was defined as a reduction in tumor size of at least 30%, encompassing both complete and partial responses. Radiomic features (n = 214) were extracted from intratumoral and peritumoral regions of pre-treatment CT images. Clinical variables (n = 7) were also incorporated to enhance predictive capability. A predictive model was developed using XGBoost algorithm, and performance was evaluated across ten independent data partitions using metrics including accuracy, precision, sensitivity, specificity, F1-score, and AUC. (3) Results: A total of 177 patients were enrolled in the study. The combined clinical-radiomic model set exhibited superior predictive performance compared to models based solely on either radiomic or clinical features. For pCR classification, integrating clinical and radiomic features produced the strongest model, achieving 82.8% accuracy with an AUC of 0.846. The clinical model alone reached 71.4% accuracy and an AUC of 0.797, while the radiomic model achieved 67.5% accuracy and an AUC of 0.615. For clinical response classification, the combined model again outperformed the individual models, achieving 71.7% accuracy with an AUC of 0.725, compared with 65.0% accuracy and an AUC of 0.666 for the clinical model, and 65.6% accuracy with an AUC of 0.615 for the radiomic model. (4) Conclusions: These results demonstrate that integrating CT radiomic features with clinical information enhances the prediction of NAC response, supporting the potential for earlier and more personalized therapeutic decision-making in breast cancer management. Full article

Background/Objectives: We analyzed clinical and radiological characteristics and prognostic factors specific to young patients with breast cancer (YBC) aged <30 years. Methods: This retrospective study included 132 women aged <30 years who underwent breast surgery between 2008 and 2013. The clinical and radiological findings of the patients were examined and compared according to recurrence or death status at follow-up. Disease-free survival (DFS) and overall survival (OS) rates were also assessed. Results: Most patients (mean age, 27.1 years) presented with palpable lesions (85.6%). Hormone receptor-positive/human epidermal growth factor receptor-negative cancer was the most common molecular subtype (59.8%), followed by triple-negative breast cancer (28.0%), with high Ki-67 expression (62.1%). Mammography and ultrasound detected abnormalities in 90.1% and 97.3% of patients, respectively, whereas magnetic resonance imaging detected abnormalities in all patients. During the follow-up period (8–10 years), 28.5% of the patients experienced recurrence and 11.5% died. The calculated DFS and OS at 5 years were 80.8% and 69.8% and 91.3% and 87.8% at 10 years, respectively. Statistically significant factors associated with DFS/OS included the BRCA1 gene mutation, with preoperative neoadjuvant chemotherapy, no hormone therapy, larger tumor size, negative hormone receptor status, high Ki-67 expression, and some radiological findings, including asymmetry with calcifications on mammography, no sonographic echogenic rind of mass, and mild vascularity on Doppler study. Conclusions: Our study highlights the aggressive nature of breast cancer in YBC aged <30 years, with relatively high rates of recurrence and mortality. Significant factors affecting prognosis may guide personalized treatment approaches and predict the prognosis. Full article

The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling continuous monitoring and real-time data collection through interconnected medical devices such as wearable sensors and smart health monitors. These devices generate sensitive physiological data, including cardiac signals, glucose levels, and vital signs, that are integrated into electronic health records (EHRs). Machine Learning (ML) and Deep Learning (DL) techniques have shown significant potential for predictive diagnostics and decision support based on such data. However, traditional centralized ML approaches raise significant privacy concerns due to the transmission and aggregation of sensitive health information. Additionally, compliance with data protection regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR), restricts centralized data sharing and analytics. To address these challenges, this study introduces FED-EHR, a privacy-preserving Federated Learning (FL) framework that enables collaborative model training on distributed EHR datasets without transferring raw data from its source. The framework is implemented using Logistic Regression (LR) and Multi-Layer Perceptron (MLP) models and was evaluated using two publicly available clinical datasets: the UCI Breast Cancer Wisconsin (Diagnostic) dataset and the Pima Indians Diabetes dataset. The experimental results demonstrate that FED-EHR achieves a classification performance comparable to centralized learning, with ROC-AUC scores of 0.83 for the Diabetes dataset and 0.98 for the Breast Cancer dataset using MLP while preserving data privacy by ensuring data locality. These findings highlight the practical feasibility and effectiveness of applying the proposed FL approach in real-world IoMT scenarios, offering a secure, scalable, and regulation-compliant solution for intelligent healthcare analytics. Full article

Aralia chinensis L. has shown potential in breast cancer treatment, yet its pharmacodynamically active components and mechanisms remain undefined. To systematically identify the bioactive constituents absorbed into the bloodstream and elucidate their multi-target mechanisms against breast cancer, we employed ultra-high-performance liquid chromatography in conjunction with Q Exactive Orbitrap mass spectrometry (UHPLC-Q Exactive Orbitrap-MS) alongside serum pharmacochemistry to analyze the chemical constituents of total saponins derived from A. chinensis (TSAC) and to identify the blood-absorbed prototypes in a rat model. Network pharmacology predicted targets and pathways of serum prototypes, validated by molecular docking and in vitro experiments. We identified 38 triterpenoid saponins, 3 steroidal saponins, and 8 triterpenoids in TSAC, with 22 prototype compounds detected in serum. An integrative analysis encompassing 486 compound targets and 1747 genes associated with breast cancer elucidated critical pathways, notably the PI3K-Akt signaling pathway and resistance mechanisms to EGFR tyrosine kinase inhibitors. Molecular docking confirmed strong binding of araloside A and elatoside L to SRC, PIK3R1, PIK3CA, STAT3, and EGFR. In MCF-7 cells, TSAC suppressed proliferation and migration while downregulating Src, PI3K, and EGFR expression at the gene and protein levels. This study successfully identified TSAC’s serum-absorbed bioactive components and demonstrated their anti-breast cancer effects via multi-target mechanisms involving the Src/PI3K/EGFR axis, providing a crucial pharmacological foundation for developing A. chinensis-derived breast cancer therapies. Full article

Background/Objectives: This study aimed to evaluate the diagnostic performance of simplified intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) parameters in distinguishing malignant from benign breast lesions, and to explore their association with clinicopathological features. Methods: This retrospective study included 108 women who underwent breast MRI with multi-b-value DWI (0, 20, 200, 500, 800 s/mm²). Of those 108 women, 73 had pathologically confirmed malignant lesions. IVIM maps (ADC_map, D, D*, and perfusion fraction f) were generated using IB-Diffusion™ software version 21.12. Lesions were manually segmented by radiologists, and clinicopathological data including receptor status, Ki-67 index, cancer type, histologic grade, and molecular subtype were extracted from medical records. Nonparametric tests and ROC analysis were used to assess group differences and diagnostic performance. Additionally, a binary logistic regression model combining D, D*, and f was developed to evaluate their joint diagnostic utility, with ROC analysis applied to the model’s predicted probabilities. Results: Malignant lesions demonstrated significantly lower diffusion parameters compared to benign lesions, including ADC_map (p = 0.004), D (p = 0.009), and D* (p = 0.016), indicating restricted diffusion in cancerous tissue. In contrast, the perfusion fraction (f) did not show a significant difference (p = 0.202). ROC analysis revealed moderate diagnostic accuracy for ADC_map (AUC = 0.671), D (AUC = 0.657), and D* (AUC = 0.644), while f showed poor discrimination (AUC = 0.576, p = 0.186). A combined logistic regression model using D, D*, and f significantly improved diagnostic performance, achieving an AUC of 0.725 (p < 0.001), with 67.1% sensitivity and 74.3% specificity. ADC_map achieved the highest sensitivity (100%) but had low specificity (11.4%). Among clinicopathological features, only histologic grade was significantly associated with IVIM metrics, with higher-grade tumors showing lower ADC_map and D* values (p = 0.042 and p = 0.046, respectively). No significant associations were found between IVIM parameters and ER, PR, HER2 status, Ki-67 index, cancer type, or molecular subtype. Conclusions: Simplified IVIM DWI offers moderate accuracy in distinguishing malignant from benign breast lesions, with diffusion-related parameters (ADC_map, D, D*) showing the strongest diagnostic value. Incorporating D, D*, and f into a combined model enhanced diagnostic performance compared to individual IVIM metrics, supporting the potential of multivariate IVIM analysis in breast lesion characterization. Tumor grade was the only clinicopathological feature consistently associated with diffusion metrics, suggesting that IVIM may reflect underlying tumor differentiation but has limited utility for molecular subtype classification. Full article

Bryostatin 1, a natural macrolide isolated from Bugula neritina, is a potent modulator of protein kinase C (PKC) isoforms with promising anticancer properties. In numerous in vitro studies, bryostatin 1 has been shown to inhibit tumor cell proliferation and induce differentiation and apoptotic cell death in a wide range of cell lines, including leukemia, lymphoma, glioma, and solid tumors such as ovarian and breast cancer. Its antitumor activity, both as monotherapy and in combination with conventional chemotherapy, has been confirmed in in vivo models, where synergistic effects have been observed, including sensitization of tumor cells to cytostatic agents. Despite promising preclinical findings, phase I and II clinical trials have not yielded the expected results, suggesting limited efficacy of the macrolide as a single agent with a relatively favorable safety profile. Current research directions focus on optimizing dosing regimens, combining bryostatin 1 with other anticancer drugs and identifying predictive biomarkers of response. This article reviews the current state of knowledge on the anticancer effects of bryostatin 1, analyzing available data from in vitro, in vivo, and clinical trials and discussing potential directions for further translational research. Full article