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Simple Summary: Diagnosing glioblastoma multiforme (GBM), solitary brain metastases (SBM), and primary central nervous system lymphoma (PCNSL) in malignant tumors of the central nervous system using multi-modal magnetic resonance imaging (MRI) is significantly important in helping physicians develop treatment plans and enhance patient prognosis. In this paper, MFFC-Net is developed and validated using deep learning methods to predict these three tumor categories from multi-modal MRI without the manual region of interest (ROI). MFFC-Net first uses a multi-encoder with DenseBlocks to extract deep features from multi-modal MRI. Then, the feature fusion layer fuses the deep information between different modalities and tissues. Finally, the spatial-channel attention module suppresses redundant new information and activates tumor classification-related features. Compared with radiomics models, MFFC-Net demonstrated higher accuracy. In addition, the results in the different sequences provide important references for future clinical work on MRI image acquisition. We believe that MFFC-Net has the potential to assist in the diagnosis and treatment of brain tumors in the future. (1) Background: Diagnosis of glioblastoma (GBM), solitary brain metastases (SBM), and primary central nervous system lymphoma (PCNSL) plays a decisive role in the development of personalized treatment plans. Constructing a deep learning classification network to diagnose GBM, SBM, and PCNSL with multi-modal MRI is important and necessary. (2) Subjects: GBM, SBM, and PCNSL were confirmed by histopathology with the multi-modal MRI examination (study from 1225 subjects, average age 53 years, 671 males), 3.0 T T2 fluid-attenuated inversion recovery (T2-Flair), and Contrast-enhanced T1-weighted imaging (CE-T1WI). (3) Methods: This paper introduces MFFC-Net, a classification model based on the fusion of multi-modal MRIs, for the classification of GBM, SBM, and PCNSL. The network architecture consists of parallel encoders using DenseBlocks to extract features from different modalities of MRI images. Subsequently, an L 1 − n o r m feature fusion module is applied to enhance the interrelationships among tumor tissues. Then, a spatial-channel self-attention weighting operation is performed after the feature fusion. Finally, the classification results are obtained using the full convolutional layer (FC) and Soft-max. (4) Results: The ACC of MFFC-Net based on feature fusion was 0.920, better than the radiomics model (ACC of 0.829). There was no significant difference in the ACC compared to the expert radiologist (0.920 vs. 0.924, p = 0.774). (5) Conclusions: Our MFFC-Net model could distinguish GBM, SBM, and PCNSL preoperatively based on multi-modal MRI, with a higher performance than the radiomics model and was comparable to radiologists. [ABSTRACT FROM AUTHOR]

Cancer is a significant global public health issue with increasing morbidity and mortality rates. To address this challenge, novel drug carriers such as nano-materials, liposomes, hydrogels, fibers, and microspheres have been extensively researched and utilized in oncology. Among them, polymer microspheres are gaining popularity due to their ease of preparation, excellent performance, biocompatibility, and drug-release capabilities. This paper categorizes commonly used materials for polymer microsphere preparation, summarizes various preparation methods (emulsification, phase separation, spray drying, electrospray, microfluidics, and membrane emulsification), and reviews the applications of polymer microspheres in cancer diagnosis, therapy, and postoperative care. The current status and future development directions of polymer microspheres in cancer treatment are analyzed, highlighting their importance and potential for improving patient outcomes. [ABSTRACT FROM AUTHOR]

Breast cancer remains the most prevalent cancer among women worldwide, necessitating advancements in diagnostic methods. The integration of artificial intelligence (AI) into mammography has shown promise in enhancing diagnostic accuracy. However, understanding patient perspectives, particularly considering the psychological impact of breast cancer diagnoses, is crucial. This narrative review synthesizes literature from 2000 to 2023 to examine breast cancer patients' attitudes towards AI in breast imaging, focusing on trust, acceptance, and demographic influences on these views. Methodologically, we employed a systematic literature search across databases such as PubMed, Embase, Medline, and Scopus, selecting studies that provided insights into patients' perceptions of AI in diagnostics. Our review included a sample of seven key studies after rigorous screening, reflecting varied patient trust and acceptance levels towards AI. Overall, we found a clear preference among patients for AI to augment rather than replace the diagnostic process, emphasizing the necessity of radiologists' expertise in conjunction with AI to enhance decision-making accuracy. This paper highlights the importance of aligning AI implementation in clinical settings with patient needs and expectations, emphasizing the need for human interaction in healthcare. Our findings advocate for a model where AI augments the diagnostic process, underlining the necessity for educational efforts to mitigate concerns and enhance patient trust in AI-enhanced diagnostics. [ABSTRACT FROM AUTHOR]

Overhydration (OH) is a prevalent medical problem that occurs in patients with kidney failure, but a specific marker has still not been found. Patients requiring kidney replacement therapy suffer from a water imbalance, which is correlated with mortality rates in this population. Currently, clinicians employ techniques such as bioimpedance spectroscopy (BIS) and ultrasound (USG) markers of overhydration or markers of heart and kidney function, namely NT-pro-BNP, GFR, or creatinine levels. New serum markers, including but not limited to Ca-125, galectin-3 (Gal-3), adrenomedullin (AMD), and urocortin-2 (UCN-2), are presently under research and have displayed promising results. Ca-125, which is a protein mainly used in ovarian cancer diagnoses, holds great potential to become an OH marker. It is currently being investigated by cardiologists as it corresponds to the volume status in heart failure (HF) and ventricular hypertrophy, which are also associated with OH. The need to ascertain a more precise marker of overhydration is urgent mainly because physical examinations are exceptionally inaccurate. The signs and symptoms of overhydration, such as edema or a gradual increase in body mass, are not always present, notably in patients with chronic kidney disease. Metabolic disruptions and cachexia can give a false picture of the hydration status. This review paper summarizes the existing knowledge on the assessment of a patient's hydration status, focusing specifically on kidney diseases and the role of Ca-125. [ABSTRACT FROM AUTHOR]