Search Results (2,128)

Skin metastases represent a rare finding in dermatological practice, but their presence signifies an advanced disease and usually portends a poor prognosis. They commonly arise as multiple painless nodules in patients with a cancer history. Differential diagnoses are challenging, and zosteriform metastases should not be mistaken for herpes zoster. Dermoscopy typically reveals a white, structureless pattern. A skin biopsy with routine hematoxylin–eosin staining is essential for an accurate diagnosis, while immunohistochemistry is particularly useful in cases of anaplastic tumors. Breast cancer is the most common cause of skin metastasis in women, and lung cancer is the most common in men. The life expectancy after diagnosis is generally low. Cutaneous metastasectomy, electrochemotherapy, and radiotherapy are generally regarded as beneficial for palliative purposes. Intralesional cryosurgery was found to be beneficial in a few case series. Systemic immunotherapy can induce the regression of cutaneous metastases in selected patients. Full article

Alterations in the mitochondrial genome (mtDNA) have been shown to be key in cancer development and could be useful as biomarkers for diagnosis, prognosis, and treatment. To identify mtDNA variants associated with breast cancer, we analyzed the whole mtDNA sequence from paired tissues (tumor–peripheral blood) of women with this malignancy and from peripheral blood samples of healthy women. The mtDNA mutational landscape, heteroplasmy levels of the variants, and mitochondrial ancestry were established. Comparative analysis between cases and controls revealed significant differences in the number and location of variants, as well as in the heteroplasmy levels. Cases showed higher mutation number in MT-ND5, tRNAs, and rRNAs genes; increased proportion of missense variants; and elevated mtDNA content, than controls. Notably, a high blood mtDNA mutational burden (OR = 3.83, CI: 1.89–7.95, p = 5.3 × 10⁻⁵) and five mtDNA variants showed association with the risk of breast cancer. Furthermore, a low tumor mutational burden (HR = 7.82, CI: 1.0–63.6, p = 0.05) and the haplogroup L (HR = 12.16, CI: 2.0–72.8, p = 0.0062) were associated with decreased overall and disease-free survival, respectively. Our study adds evidence of the potential usefulness of mtDNA variants as risk and prognosis biomarkers for breast cancer. Full article

Background/Objectives: A newly developed nine-protein serum signature has been utilized to enhance the accuracy of an existing three-protein signature used as a blood-based diagnostic tool. This study used the new nine-protein serum signature to evaluate the clinical sensitivity and specificity of a medical device designed to test the clinical performance of an artificial intelligence algorithm. Methods: A blood-based test using multiple reaction monitoring via mass spectrometry was performed to quantify nine proteins (APOC1, CHL1, FN1, VWF, PPBP, CLU, PRDX6, PRG4, and MMP9) in serum samples from 243 healthy controls and 222 patients with breast cancer. Results: Based on cutoff values determined by an artificial intelligence-based deep learning model, the sensitivity and specificity of the nine-protein signature in diagnosing breast cancer among all participants was 83.3% and 88.1%, respectively, whereas those of the three-protein signature were 71.6% and 85.3%, respectively. The assay yielded a positive predictive value of 86.5% for breast cancer and 13.6% for healthy controls, with corresponding negative predictive values of 14.7% and 85.3%, respectively. The accuracies of nine- and three-protein signatures were 85.8% (area under the receiver operating characteristic curve: 0.8526) and 77.0%, respectively. Conclusions: The nine-protein signature may help detect breast cancer more accurately and effectively than the three-protein signature. Full article

Background: Breast cancer remains a critical public health problem worldwide and is a leading cause of cancer-related mortality. Optimizing clinical outcomes is contingent upon the early and precise detection of malignancies. Advances in medical imaging and artificial intelligence (AI), particularly in the fields of radiomics and deep learning (DL), have contributed to improvements in early detection methodologies. Nonetheless, persistent challenges, including limited data availability, model overfitting, and restricted generalization, continue to hinder performance. Methods: This study aims to overcome existing challenges by improving model accuracy and robustness through enhanced data augmentation and the integration of radiomics and deep learning features from the CBIS-DDSM dataset. To mitigate overfitting and improve model generalization, data augmentation techniques were applied. The PyRadiomics library was used to extract radiomics features, while transfer learning models were employed to derive deep learning features from the augmented training dataset. For radiomics feature selection, we compared multiple supervised feature selection methods, including RFE with random forest and logistic regression, ANOVA F-test, LASSO, and mutual information. Embedded methods with XGBoost, LightGBM, and CatBoost for GPUs were also explored. Finally, we integrated radiomics and deep features to build a unified multimodal feature space for improved classification performance. Based on this integrated set of radiomics and deep learning features, 13 pre-trained transfer learning models were trained and evaluated, including various versions of ResNet (50, 50V2, 101, 101V2, 152, 152V2), DenseNet (121, 169, 201), InceptionV3, MobileNet, and VGG (16, 19). Results: Among the evaluated models, ResNet152 achieved the highest classification accuracy of 97%, demonstrating the potential of this approach to enhance diagnostic precision. Other models, including VGG19, ResNet101V2, and ResNet101, achieved 96% accuracy, emphasizing the importance of the selected feature set in achieving robust detection. Conclusions: Future research could build on this work by incorporating Vision Transformer (ViT) architectures and leveraging multimodal data (e.g., clinical data, genomic information, and patient history). This could improve predictive performance and make the model more robust and adaptable to diverse data types. Ultimately, this approach has the potential to transform breast cancer detection, making it more accurate and interpretable. Full article

Background: Breast cancer is a major health concern in Poland, with significant incidence and mortality rates despite national screening programs. This retrospective study aimed to evaluate critical aspects of breast cancer management, focusing on waiting times, treatment coordination, cancer characteristics, diagnostic testing, and staging. Methods: We retrospectively analyzed 587 medical records of breast cancer patients (585 female, 2 male) collected between March 2023 and June 2024 through a data donation model. Data included tumor characteristics (histological type, grade, stage, biological subtype, receptor status, Ki-67), diagnostic and genetic tests, and timelines of key events in the diagnostic and therapeutic pathways. Results: Although referral to first oncology consult (18 days) and MDT referral/admission to treatment (10 days) met NFZ guidelines, diagnosis to surgery (94 days) and diagnosis to drug treatment (109 days) were significantly delayed. No records showed oncology coordinator assignment or educational material provision. Clinically, invasive carcinoma NST (77%) and early-stage (IA/IIA, 61%) were prevalent, with Luminal B (HER2-negative) being the most common biological subtype. BRCA1/2 testing was common, but Oncotype DX was not. For 314 HR+ HER2- patients, stage IA (44%) was most common, with no BRCA1/2 mutations found. Conclusion: Breast cancer care in the Łódź voivodeship falls short of national guidelines due to long waiting times and poor care coordination, a problem worsened by incomplete data. Improving record-keeping and speeding up diagnostic and treatment pathways are crucial for better breast cancer management in Poland. While patient data donation can help analyze real clinical pathways, data completeness, and consistency remain challenges. 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

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

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

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

Background/Objectives: Triple negative breast cancer (TNBC) is an aggressive, molecularly heterogeneous subtype of breast cancer, accounting for approximately 10–15% of all cases. While reproductive and metabolic factors contribute to breast cancer development, growing concerns about environmental exposures, alongside biological and socio-cultural influences, underscore the need for targeted prevention strategies across diverse populations. Despite increasing evidence linking biological, socioeconomic, and environmental factors to TNBC outcomes, the molecular mechanisms underlying these relationships remain poorly understood. Micro-RNAs (miRNAs), which regulate gene expression and play critical roles in cancer development, have emerged as potential mediators between environmental exposures and TNBC progression. The goal of this research is to identify environmental risk factors that directly relate to TNBC stages and enhance understanding of the mechanisms underlying how miRNAs link environmental exposures to TNBC stages. Methods: In this study, we analyzed 434 Formalin-Fixed, Paraffin-Embedded (FFPE) tumor samples from 434 women diagnosed with TNBC between 2009 and 2019, encompassing diverse cancer stages (184 cases from early stage and 250 cases from advanced stage), racial backgrounds, and socioeconomic statuses. The sequencing data were linked with the Louisiana Tumor Registry data and the Environmental Justice index. Results: A total of 348 unique miRNAs were identified as differentially expressed across environmental risk factors statistically associated with TNBC stage, adjusting for plate effects. An UpSet plot revealed 44 miRNAs commonly differentially expressed across TNBC stages and multiple environmental exposures. At least one differentially expressed (DE) miRNA was shared between the TNBC stage and each environmental factor, with many associated with receptor-negative and aggressive breast cancer subtypes. Conclusions: These findings highlight potential biological pathways through which exposures may drive the TNBC progression and contribute to disparities in outcomes. Full article

Background: Recently, the adoption of AI-based technologies has been accelerating in the field of medical image analysis. For the early diagnosis and treatment planning of breast cancer, Automated Breast Ultrasound (ABUS) has emerged as a safe and non-invasive imaging method, especially for women with dense breasts. However, the increasing computational cost due to the minute size and complexity of 3D ABUS data remains a major challenge. Methods: In this study, we propose a novel model based on the Mamba state–space model architecture for 3D tumor segmentation in ABUS images. The model uses Mamba blocks to effectively capture the volumetric spatial features of tumors, and integrates a deep spatial pyramid pooling (DASPP) module to extract multiscale contextual information from lesions of different sizes. Results: On the TDSC-2023 ABUS dataset, the proposed model achieved a Dice Similarity Coefficient (DSC) of 0.8062, and Intersection over Union (IoU) of 0.6831, using only 3.08 million parameters. Conclusions: These results show that the proposed model improves the performance of tumor segmentation in ABUS, offering both diagnostic precision and computational efficiency. The reduced computational space suggests a strong potential for real-world medical applications, where accurate early diagnosis can reduce costs and improve patient survival. Full article

Homologous recombination deficiency (HRD) is a pivotal biomarker in precision oncology, driving therapeutic strategies for ovarian and breast cancers through impaired DNA double-strand break repair. This narrative review synthesizes recent advances (2021–2025) in HRD’s biological basis, prevalence, detection methods, and clinical implications, focusing on high-grade serous ovarian carcinoma (HGSOC; ~50% HRD prevalence) and triple-negative breast cancer (TNBC; 50–70% prevalence). HRD arises from genetic (BRCA1/2, RAD51C/D, PALB2) and epigenetic alterations (e.g., BRCA1 methylation), leading to genomic instability detectable via scars (LOH, TAI, LST) and mutational signatures (e.g., COSMIC SBS3). Advanced detection integrates genomic assays (Myriad myChoice CDx, Caris HRD, FoundationOne CDx), functional assays (RAD51 foci), and epigenetic profiling, with tools like HRProfiler and GIScar achieving >90% sensitivity. HRD predicts robust responses to PARP inhibitors (PARPi) and platinum therapies, extending progression-free survival by 12–36 months in HGSOC. However, resistance mechanisms (BRCA reversion, SETD1A/EME1, SOX5) and assay variability (60–70% non-BRCA concordance) pose challenges. We propose a conceptual framework in ^{Section 10}, integrating multi-omics, methylation analysis, and biallelic reporting to enhance detection and therapeutic stratification. Regional variations (e.g., Asian cohorts) and disparities in access underscore the need for standardized, cost-effective diagnostics. Future priorities include validating novel biomarkers (SBS39, miR-622) and combination therapies (PARPi with ATR inhibitors) to overcome resistance and broaden HRD’s applicability across cancers. Full article

Background and Objectives: Breast cancer is the most common malignant disease among women. The aim of this study is to compare clinical, histological, and radiological findings of breast cancer between younger and older women. Materials and Methods: This retrospective study enrolled 241 patients with histologically diagnosed breast cancer from January 2015 to December 2021. The patients were divided into two groups: the first group comprised young women aged 49 years or younger and the second group consisted of older women aged over 70 years. Because preventive mammograms were only performed on patients between the ages of 50 and 69 until 2025, we did not include this interval in the study; therefore, only younger and older women who had not undergone preventive mammograms were selected. All patients underwent radiological examinations, including mammography, ultrasound, and magnetic resonance imaging. The parameters were compared between the two groups to evaluate clinical, histological, and radiological features. Results: During the study period, a total of 241 patients were included in the final analysis, with 94 (39%) being younger women and 147 (61%) being older women. Clinical signs were analyzed, revealing that redness and swelling (19%) and pain (17.7%) were statistically significantly more common in the older women group compared to the younger group (p < 0.013 and p < 0.002, respectively). The hormone receptor status of patients in both cohorts did not differ significantly, except for human epidermal growth factor receptor 2 (HER2). Older patients had a significantly higher percentage of HER2-negative disease (83.7%) compared to younger patients (70.2%) (p < 0.013). Older women were statistically significantly more likely to have G2 (68.5%) and G3 (21.7%) tumors compared to younger women (G2—36.6% and G3—46.6%) (p < 0.001). Ki67 < 40% (61.2%) was statistically significantly more common in the older women group, while Ki67 ≥ 40% was more prevalent in the younger women group (p < 0.001). Lobular (19.7%) and ductal (62.6%) histological types of cancer were more common in the older women group (p < 0.001). Comparing cancer changes in MRI and ultrasound scans with the results of postoperative histology showed a sensitivity of 87.8% for MRI and 82.7% for ultrasound. Our study suggests that younger women have a higher percentage of proliferation index Ki67 > 40% (73.9%), which is statistically more significant than in the older women group with Ki67 < 40% (63%) (p < 0.001). Conclusions: All diagnostic tools are essential for early breast cancer detection. Malignant microcalcifications are typically identified through mammography. Breast MRI was found to be more sensitive in detecting breast cancer compared to mammography and ultrasound. While ultrasound is considered the most sensitive and specific diagnostic tool for axillary lymph node evaluation, it is unfortunately not sensitive enough to determine the exact extent of cancer spread. During our retrospective study, T1 and T2 histological sizes were identified most frequently; the earlier the diagnosis is made, the higher the chances of survival and improved quality of life. Full article