Cancer Investigation最新文献

Breast cancer (BC) is the second most prevalent cause of death for women and the most frequently diagnosed malignancy. Early identification of this deadly illness lowers treatment costs while significantly improving survival rates. In contrast, skilled radiologists and pathologists analyze radiographic and histopathological images, respectively. In addition to being expensive, the procedure is prone to errors. The paper offers a solution to these challenges by presenting an innovative approach that combines a Modified U-Net architecture with sophisticated self-supervised learning methods to the accuracy and efficiency of breast cancer detection in WSIs. The proposed model improves the accuracy of tumor detection by integrating a multi-stage process: starting with Gaussian filtering for image preprocessing to remove noise, followed by the Modified U-Net for precise tumor segmentation including multi-scale processing and attention mechanisms. Feature extraction is achieved through the Bag of Visual Words (BoW), Improved Local Gradient and Intensity Pattern (LGIP), and Pyramidal Histogram of Oriented Gradients (PHOG) techniques to capture diverse image characteristics. The classification phase employs an Improved Self-Supervised Learning (ISSL) method, which improves feature representation via a novel loss function and an improved Multiple Instance Pooling (IMIP) mechanism. This method is designed to overcome the limitations of conventional techniques by offering clearer tumor boundaries and more accurate classifications, thereby improving the overall reliability and efficacy of breast cancer detection in clinical practice. Moreover, the ISSL strategy yielded the highest performance metrics, including an accuracy of 0.924, a sensitivity of 0.886, and a negative predictive value (NPV) of 0.943.

Background: Breast cancer (BC) is one of the most prevalent malignant tumors among women globally. The incidence and mortality rates of female BC exhibit significant variation across different countries and regions.

Objective: This study analyzed the trends of BC among Chinese women from 1997 to 2021 to support evidence-based for the prevention, screening and treatment strategies of female BC in China.

Methods: We extracted data on BC incidence, mortality, prevalence, disability-adjusted life years (DALYs), years lived with disability (YLDs) and years of life lost (YLLs) among Chinese women from 1997 to 2021 from the Global Burden of Disease (GBD)database. Join point regression analysis was used to identify the major turning points of disease burden trends, and to calculate the annual percentage change (APC) and average annual percentage change (AAPC). We applied age-period-cohort (A-P-C) models to separately evaluate the effects of age, period, and cohort on trends in female BC in China.

Results: In 2021, the age standardized incidence rate (ASIR) and DALYs of female BC in China were 37.12 (95% CI: 28.23,46.95) and 281.54(95% CI: 216.87,358.11) per 100,000 women respectively. The AAPC values of the incidence and mortality of female BC were 2.42% (95% CI 2.04-2.80) and -0.49% (95% CI -0.70--0.28) respectively (p < 0.05). A-P-C model indicated that both the rates of incidence, prevalence and deaths increased with age from 1997 to 2021. The period effect analysis revealed that the prevalence and incidence risk of BC peaked between 2015 and 2020, with the highest rate ratio (RR) value 1.28 (95% CI 1.25-1.31) and 1.22 (95% CI 1.19-1.25). The cohort born in 2002 exhibited the lowest risk of mortality and the highest risk of incidence and prevalence.

Conclusions: Over the past 25 years, the large population size and aging population structure in China have led to female BC becoming an important public health issue. Effective preventive strategies and individualized treatment approaches are urgently required to enhance the control of BC in China.

Breast cancer's heterogeneity demands innovative therapies. Co-delivery of therapeutics using nanocarriers, especially siRNA combined with other chemotherapeutic drugs, presents a promising avenue. These systems safeguard siRNA, enhance its cellular uptake, and facilitate simultaneous targeting of multiple oncogenic pathways. This multifaceted approach holds potential for superior efficacy and reduced toxicity, addressing the limitations of conventional treatments and paving the way for improved breast cancer therapy.

This review analyzed 12 studies to evaluate the safety, immunogenicity, and therapeutic efficacy of peptide-based cancer vaccines across various tumor types, including breast, gynecological, head and neck, and gastrointestinal cancers. The included studies involved a total of 520 patients and preclinical models. The findings indicated that peptide vaccines are generally safe, with no serious adverse events reported in clinical trials, and demonstrated robust immunogenicity, eliciting specific T-cell responses in up to 85.7% of patients. Importantly, the durability of T-cell responses varied across studies, with some demonstrating sustained immune memory that could enhance long-term protection against tumor recurrence.

Currently, thyroid cancer and thyroid nodules disorders are increasing globally. The diagnosis of these conditions relies on the development of medical technology. Current methods often suffer from the overfitting issue due to a small dataset and a lack of generalizability to diverse clinical settings. Some of the traditional methods encounter challenges with false positive and false negative rates, which affects the performance of the model. To overcome these challenges, a novel module called Pyramid-SpinalNet (Py-SpinalNet) has been developed for thyroid cancer classification. From the given database, the image is pre-processed through the Wiener filter. After this, 3D-UNet is employed for nodule segmentation. In addition, key features are derived through the process of feature extraction. Eventually, the Py-SpinalNet is used for the classification of thyroid cancer. The Py-SpinalNet is developed by merging PyramidNet and SpinalNet. Here, Accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) are the metrics employed for Py-SpinalNet acquired 91.9, 90.9 and 92.8%. The Py-SpinalNet model can accurately detect thyroid cancer at the early stage, thereby minimizing both false-positive and false-negative rates. Thus, it offers a more efficient and reliable classification of thyroid cancer.

Computer-based technologies significantly improve melanoma and non-melanoma skin cancer detection by providing non-invasive, cost-effective, and rapid diagnostic solutions. In this context, the study proposes a novel Deep Learning (DL)-based skin cancer detection approach that leverages an advanced segmentation technique called Improved DeepJoint Segmentation (IDJS). This method is designed to enhance the accuracy and precision of the detection process. Initially, the proposed Modified LeNet (MLeNet)-based model applies a Gaussian filter during preprocessing to reduce speckle noise in the input skin images effectively. Following this, the preprocessed images undergo the IDJS segmentation process, which effectively partitions the cancerous regions with high accuracy. Subsequently, three types of features are extracted from the segmented images and they are Multi-Texton Histogram (MTH)-based features, Improved Pyramid Histogram of Oriented Gradient (IPHOG)-based features, and Median Binary Pattern (MBP). These extracted features serve as the input to the MLeNet model for the final skin cancer detection. The datasets used in this work are the HAM10000 dataset and the ISIC 2019 dataset. With a positive metric value of 0.952, the MLeNet model outperforms the traditional models, with LeNet achieving the highest score of 0.932.

Lung cancer detection (LCD) is a process of identifying an occurrence of lung cancer (LC) or irregularities in the lungs. Early detection of lung cancer is crucial for improving patient survival and enabling effective treatment. Computed Tomography (CT) images and Positron emission tomography (PET) are employed for screening and detecting LC. These methods offer full cross-sectional images of the lungs to detect smaller lesions. Several techniques are developed for LCD, but they often fall into uncertainty. Therefore, a Pyramidal Cascade Neuro-Fuzzy Fractional Network (PCNFFN) is introduced for LCD utilizing CT and PET images. Initially, PET and CT images are pre-processed employing a Bilateral filter (BF). Then, lung lobes are segmented from both images utilizing Dual-Attention V-Network (DAV-Net). Thereafter, Black Hole Entropic Fuzzy Clustering (BHEFC) is employed to segment tumor locations from both lung lobe segmented images. Next, features are extracted from tumor location segmented images. Lastly, LCD is performed by PCNFFN. However, PCNFFN is a combination of Deep Pyramidal residual Network (PyramidNet) and Cascade Neuro-Fuzzy Network (NFN) with Fractional Calculus (FC). In addition, PCNFFN achieved an accuracy of about 91.002%, a true negative rate (TNR) of about 90.504% and a true positive rate (TPR) of about 92.571%.

Introduction: Depressive symptoms (DS) are prevalent among cancer survivors and may be exacerbated by chemotherapy. However, longitudinal data on this relationship within the Mexican population are lacking. The current study aimed to analyze the relationship between chemotherapy and the persistence of depressive symptoms over time in cancer survivors.

Methods: Retrospective observational study using Mexican Study of Health and Aging (MHAS) data (2012-2021). Participants aged 50+ included chemotherapy patients (n = 30) and healthy controls (n = 6,970). Depressive symptoms were assessed with a modified Center for Epidemiologic Studies Depression Scale. Mann-Whitney U, X² tests, and generalized estimating equations analyzed chemotherapy's impact on depressive symptoms over time.

Results: Chemotherapy recipients showed significantly higher depressive symptoms at early follow-ups (2012, 2015, 2018; p < 0.05), with no significant difference by 2021. Adjusted analyses indicated chemotherapy was associated with a more than twofold increase in odds of depression (OR = 2.165; 95% CI: 1.220-3.810). Lower education and comorbidities such as diabetes and hypertension were also independently linked to increased depression risk.

Conclusions: Chemotherapy is a significant predictor of persistent depressive symptoms among Mexican cancer survivors aged 50 and above. These findings highlight the critical need for integrated mental health screening and targeted psychosocial care within oncology settings.