Search Results (297)

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

Carcinomas originate from polarized epithelia, displaying luminal and basal orientations with distinct biological properties. Regardless of tissue of origin, many carcinomas show luminal or basal traits that are reflected in molecular profiles and are associated with different clinical behaviors and outcomes. Traditionally, cancers have been classified by histology and anatomical site, but accumulating evidence indicates that luminal/basal subtyping reflects shared biological programs that transcend organ boundaries. Breast cancer was the first model in which these subtypes were defined, revealing clear prognostic and therapeutic implications. Subsequent studies have identified similar subtypes in bladder, lung, prostate, pancreatic, and head and neck carcinomas, where basal phenotypes are consistently associated with aggressive disease and distinct vulnerabilities to treatment. In this review, we synthesize advances from the last decade (2010–2024) on the basal-like subtype across epithelial tumors. We summarize key studies applying luminal/basal subtyping in large cohorts of carcinomas and in single tissue tumor types. By integrating these findings, we aim to clarify the current understanding of luminal and basal subtypes in epithelial tumors and outline their potential to refine cancer classification, improve prognostic accuracy, and guide therapeutic decision-making. This perspective supports a biology-driven framework for cancer classification and treatment, moving beyond traditional histological boundaries. Full article

Breast cancer is a significant cause of cancer-related mortality among women around the globe, underscoring the need for early and accurate diagnosis. Typically, histopathological analysis of biopsy slides is utilized for tumor classification. However, it is labor-intensive, subjective, and often affected by inter-observer variability. Therefore, this study explores a deep learning-based, multi-class classification framework for distinguishing breast cancer subtypes using convolutional neural networks (CNNs). Unlike previous work using the popular BreaKHis dataset, where binary classification models were applied, in this work, we differentiate eight histopathological subtypes: four benign (adenosis, fibroadenoma, phyllodes tumor, and tubular adenoma) and four malignant (ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma). This work leverages transfer learning with ImageNet-pretrained ResNet architectures (ResNet-18, ResNet-34, and ResNet-50) and extensive data augmentation to enhance classification accuracy and robustness across magnifications. Among the ResNet models, ResNet-50 achieved the best performance, attaining a maximum accuracy of 92.42%, an AUC-ROC of 99.86%, and an average specificity of 98.61%. These findings validate the combined effectiveness of CNNs and transfer learning in capturing fine-grained histopathological features required for accurate breast cancer subtype classification. Full article

Phytochemicals exhibit a broad spectrum of pharmacological activities, including significant anticancer potential. However, their clinical translation is often hampered by poor aqueous solubility, low bioavailability, and chemical instability. Lipid-based nanocarriers, especially solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs), have proven to be effective strategies for addressing these challenges. These nanocarriers improve the solubility, stability, and bioavailability of phytochemical-based anticancer agents, while enabling controlled and tumor-specific drug release. Encapsulation of anticancer phytochemicals such as curcumin, quercetin, resveratrol, silymarin, and naringenin in SLNs and NLCs has demonstrated improved therapeutic efficacy, cellular uptake, and reduced systemic toxicity. Co-delivery strategies, combining multiple phytochemicals or phytochemical–synthetic drug pairs, further contribute to synergistic anticancer effects, dose reduction, and minimized side effects, particularly important in complex cancers such as glioblastoma, breast, and colon cancers. This review presents a comparative overview of SLNs and NLCs in terms of formulation methods, in vitro characterization, and classification of key phytochemicals based on chemical structure and botanical sources. The roles of these lipidic carriers in enhancing anticancer activity, challenges in formulation, and recent patent filings are discussed to highlight ongoing innovations. Additionally, hybrid lipid–polymer nanoparticles are introduced as next-generation carriers combining the benefits of both systems. Future research should aim to develop scalable, biomimetic, and stimuli-responsive nanostructures through advanced surface engineering. Collaborative interdisciplinary efforts and regulatory harmonization are essential to translate these lipid-based carriers into clinically viable platforms for anticancer phytochemical delivery. 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: 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

Cancer-associated fibroblasts (CAFs) constitute a heterogeneous population of cells within the tumor microenvironment and are associated with cancer development and drug resistance. The absence of a universal classification for CAFs hinders their research and therapeutic targeting. To define CAF phenotypes, we developed patient-derived cell cultures of breast cancer (BC) and validated and characterized four distinct CAF subtypes (S1–S4) by Costa’s classification. Three out of five primary cell cultures of BC demonstrated different functional features rather than fixed cellular states due to the plasticity of the CAF phenotype. CAF crosstalk with cancer cells supported their survival in the presence of anticancer drugs. Based on the analysis of the cytotoxic effect of doxorubicin, cisplatin and tamoxifen, it was demonstrated that CAF-S4 and CAF-S1 cells were sensitive to the action of all drugs investigated, despite the fact that they possessed different mechanisms of action. CAF-S2 cells exhibited the highest level of resistance to the antitumour agents. Homotypic and heterotypic spheroids with CAFs could be used to model the fibrotic area of BC in vitro. The patient-derived cell cultures of CAFs formed spheroids. Hypoxia-activated CAF-S4 have been shown to stimulate the metastatic potential of triple-negative BC cells in a heterotypic spheroid model. Consequently, this study could be a starting point for the development of novel therapeutic strategies that target CAFs and their interactions with cancer cells. Full article

Background: Basal-like breast cancer (BLBC) is a highly aggressive molecular subtype characterized by the strong expression of a gene cluster found in the basal or outer epithelial layer of the adult mammary gland. Patients with BLBC typically face a poor prognosis, with a shorter disease-free period and overall survival. Methods: In this study, we explored the proteomic profiles of BLBC patients using publicly available data from two large cohorts of breast cancer patients. By integrating cluster analysis, predictive modeling, protein differential abundance expression, and network analysis, we identified and validated the presence of two distinct subgroups, characterized by 256 upregulated and 99 downregulated proteins. Results: We report the upregulation of spliceosome components, especially SNRPG and its partners (BUD13, CWC15, SNRNP70, ZMAT12), indicating altered splicing activity between TNBC subgroups. Collagen proteins (COL1A1, COL1A2, COL3A1, COL11A1) were associated with tumor progression and metastasis. Proteins in the CCT complex and microtubule-associated proteins (TUBA1C, TUBB) were linked to cytoskeletal structure and chemotherapy resistance. Aminoacyl-tRNA synthetases (DARS1, IARS1, KARS1) may also play a role in TNBC development. Conclusions: These findings suggest the existence of novel molecular signatures that could improve TNBC classification, prognosis, and potential therapeutic targeting. Full article

Carboxypeptidase A4 (CPA4) is an exopeptidase that cleaves peptide bonds at the C-terminal domain within peptides and proteins. It preferentially cleaves peptides with terminal aromatic or branched chain amino acid residues such as phenylalanine, tryptophan, or leucine. CPA4 was first discovered in prostate cancer cells, but it is now known to be expressed in various tissues throughout the body. Its physiologic expression is governed by latexin, a noncompetitive endogenous inhibitor of CPA4. Nevertheless, the overexpression of CPA4 has been associated with the progression and aggressiveness of many malignancies, including prostate, pancreatic, breast and lung cancer, to name a few. CPA4’s role in cancer has been attributed to its disruption of many cellular signaling pathways, e.g., PI3K-AKT-mTOR, STAT3-ERK, AKT-cMyc, GPCR, and estrogen signaling. The dysregulation of these pathways by CPA4 could be responsible for inducing epithelial--mesenchymal transition (EMT), tumor invasion and drug resistance. Although CPA4 has been found to regulate cancer aggressiveness and poor prognosis, no comprehensive review summarizing the role of CPA4 in cancer is available so far. In this review, we provide a brief description of peptidases, their classification, history of CPA4, mechanism of action of CPA4 as a peptidase, its expression in various tissues, including cancers, its role in various tumor types, the associated molecular pathways and cellular processes. We further discuss the limitations of current literature linking CPA4 to cancers and challenges that prevent using CPA4 as a biomarker for cancer aggressiveness and predicting drug response and highlight a number of future strategies that can help to overcome the limitations. Full article

Background/Objectives: In recent years, there has been a significant increase in the number of women with breast cancer. Breast cancer prediction is defined as a medical data analysis and image processing problem. Experts may need artificial intelligence technologies to distinguish between benign and malignant tumors in order to make decisions. When the studies in the literature are examined, it can be seen that applications of deep learning algorithms in the field of medicine have achieved very successful results. Methods: In this study, 11 different deep learning algorithms (Vanilla, ResNet50, ResNet152, VGG16, DenseNet152, MobileNetv2, EfficientB1, NasNet, DenseNet201, ensemble, and Tuned Model) were used. Images of pathological specimens from breast biopsies consisting of two classes, benign and malignant, were used for classification analysis. To limit the computational time and speed up the analysis process, 10,000 images, 6172 IDC-negative and 3828 IDC-positive, were selected. Of the images, 80% were used for training, 10% were used for validation, and 10% were used for testing the trained model. Results: The results demonstrate that DenseNet201 achieved the highest classification accuracy of 89.4%, with a precision of 88.2%, a recall of 84.1%, an F1 score of 86.1%, and an AUC score of 95.8%. Conclusions: In conclusion, this study highlights the potential of deep learning algorithms in breast cancer classification. Future research should focus on integrating multi-modal imaging data, refining ensemble learning methodologies, and expanding dataset diversity to further improve the classification accuracy and real-world clinical applicability. Full article

Background and Objectives: The accuracy of breast cancer diagnosis depends on the concordance between imaging features and pathological findings. While BI-RADS (Breast Imaging Reporting and Data System) provides standardized risk stratification, its correlation with histologic grade and immunohistochemical markers remains underexplored. This study assessed the diagnostic performance of BI-RADS 3, 4, and 5 classifications and their association with tumor grade and markers such as ER, PR, HER2, and Ki-67. Materials and Methods: In this prospective study, 67 women aged 33–82 years (mean 56.4) underwent both mammography and ultrasound. All lesions were biopsied using ultrasound-guided 14G core needles. Imaging characteristics (e.g., margins, echogenicity, calcifications), histopathological subtype, and immunohistochemical data were collected. Statistical methods included logistic regression, Chi-square tests, and Spearman’s correlation to assess associations between BI-RADS, histology, and immunohistochemical markers. Results: BI-RADS 5 lesions showed a 91% malignancy rate. Evaluated features included spiculated margins, pleomorphic microcalcifications, and hypoechoic masses with posterior shadowing, and were correlated with histological and immunohistochemical results. Invasive tumors typically appeared as irregular, hypoechoic masses with posterior shadowing, while mucinous carcinomas mimicked benign features. Higher BI-RADS scores correlated significantly with increased Ki-67 index (ρ = 0.76, p < 0.001). Logistic regression yielded an AUC of 0.877, with 93.8% sensitivity and 80.0% specificity. Conclusions: BI-RADS scoring effectively predicts malignancy and correlates with tumor proliferative markers. Integrating imaging, histopathology, and molecular profiling enhances diagnostic precision and supports risk-adapted clinical management in breast oncology. Full article

The intricate and complex tumor cell morphology in breast pathology images is a key factor for tumor classification. This paper proposes a lightweight breast tumor classification model with multi-frequency feature fusion (LMFM) to tackle the problem of inadequate feature extraction and poor classification performance. The LMFM utilizes wavelet transform (WT) for multi-frequency feature fusion, integrating high-frequency (HF) tumor details with high-level semantic features to enhance feature representation. The network’s ability to extract irregular tumor characteristics is further reinforced by dynamic adaptive deformable convolution (DADC). The introduction of the token-based Region Focus Module (TRFM) reduces interference from irrelevant background information. At the same time, the incorporation of a linear attention (LA) mechanism lowers the model’s computational complexity and further enhances its global feature extraction capability. The experimental results demonstrate that the proposed model achieves classification accuracies of 98.23% and 97.81% on the BreaKHis and BACH datasets, with only 9.66 M parameters. Full article

Background/Objectives: Breast cancer (BC) is a multifactorial disease, with genetic alterations in cell proliferation and migration pathways being significant risk factors. This study examines the association between three variants in the BIRC5 gene (rs8073069, rs17878467, and rs9904341) and breast cancer (BC) susceptibility. Methods: Peripheral blood DNA samples were collected from 423 women (221 BC patients and 202 healthy controls). Genotyping was performed by polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) methodology. Associations were calculated using odds ratios (OR), with p-values adjusted by the Bonferroni test (significance at p ≤ 0.016). In silico analyses were conducted to predict the functional impact of the analyzed variants. Results: Patients carrying the C/C genotype for the rs8073069 variant showed increased susceptibility to BC with early TNM (tumor-node-metastasis classification) stage and Luminal A subtype (OR > 2.00; p ≤ 0.004). For the rs17878467 variant, patients with the C/T or T/T genotype exhibited a higher susceptibility to developing breast cancer (BC), particularly at early TNM stages or with a histological lobular type (OR > 2.00; p ≤ 0.012). Regarding the rs9904341 variant, patients with the G/C or C/C genotype had a higher susceptibility to breast cancer. Notably, G/C genotype carriers with Luminal A and B subtypes, and C/C genotype carriers who had TNM stages II and III, and Luminal A, Luminal B, and HER2 subtypes demonstrated increased risk (OR > 2.00; p ≤ 0.009). The C-T-C haplotype (rs8073069–rs17878467–rs9904341) was significantly associated with BC (OR = 4.20; 95% CI = 2.38–7.41; p ≤ 0.001). In silico analysis using CADD indicated a low probability of deleterious effects. Conclusions: The results suggest that the rs8073069, rs17878467, and rs9904341 variants in BIRC5 have a significant influence on breast cancer susceptibility. Full article

Background and Objectives: The clinical value of HER2-low breast cancer (BC), defined by immunohistochemistry (IHC) scores of 1+ or 2+/ISH-negative without HER2 amplification, remains unclear in the neoadjuvant setting. This study aimed to determine whether HER2-low and HER2-zero tumors differ in pathological complete response (pCR) rates and disease-free survival (DFS) among early-stage breast cancer patients undergoing neoadjuvant chemotherapy (NAC). Materials and Methods: We retrospectively analyzed 134 early BC patients treated with NAC between 2017 and 2023. Patients were categorized as HER2-zero (IHC 0) or HER2-low (IHC 1+ or 2+/ISH–). The primary endpoint was total pCR (tpCR); secondary endpoints included breast (bpCR), nodal (npCR), and radiologic complete response (rCR), alongside DFS analysis stratified by hormone receptor (HR) status. Results: Of the cohort, 91 patients (67.9%) were HER2-zero and 43 (32.1%) were HER2-low. There was no statistically significant difference in tpCR (26.4% vs. 27.9%, p = 0.852), bpCR (28.6% vs. 30.2%, p = 0.843), npCR (37.4% vs. 32.6%, p = 0.588), and rCR (23.1% vs. 30.2%, p = 0.374) between HER2-zero and HER2-low groups. DFS did not significantly differ between HER2-zero and HER2-low groups overall (p = 0.714), nor within HR-positive (p = 0.540) or TNBC (p = 0.523) subgroups. Conclusions: HER2-low tumors demonstrated similar pathological responses and survival outcomes compared to HER2-zero tumors. While a HER2-low status does not appear to define a distinct biological subtype in early BC, it remains a relevant classification for emerging HER2-targeted therapies, needing further investigation in prospective studies. Full article

Breast cancer remains a leading cause of death among women worldwide, underscoring the urgent need for practical diagnostic tools. This paper presents an advanced machine learning algorithm designed to improve classification accuracy in breast cancer diagnosis. The system integrates a deep multi-layer perceptron (Deep MLP) for feature extraction, a feature-fused autoencoder for efficient dimensional reduction, and a weight-tuned decision-tree classifier optimized via cross-validation and square weight adjustment. The proposed method was rigorously tested using the Wisconsin breast cancer dataset, employing k-fold cross-validation to ensure robustness and generalizability. Key performance indicators, including accuracy, precision, recall, F1-score, and area under the curve (AUC), were used to evaluate the model’s ability to distinguish between malignant and benign tumors. Our results suggest that this combination model outperforms traditional classification methods, with high accuracy and robust performance across data partitions. The main contribution of this research is the development of a new framework for deep learning. Auto-encoder and decision tree results show that this system has strong potential to improve breast cancer diagnosis, offering physicians a reliable and effective tool. Full article