Cancer info
Searched over 200M research papers for "cancer info"
Summary
10 papers analyzed
These studies suggest that cancer patients and their families have significant information needs, advanced machine learning models can improve cancer detection and diagnosis, and understanding cancer's physical and molecular traits can aid in developing better treatments.
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Comprehensive Overview of Cancer Information Needs and Sources
Cancer Patients' Information Needs and Sources
Understanding the information needs of cancer patients is crucial for providing quality care. A systematic review of research from 1980 to 2003 identified that the most frequent information need among cancer patients is treatment-related, accounting for 38.1% of the information sought. Health professionals are the primary source of information, providing 27.3% of the needed details. During the diagnosis and treatment phases, patients predominantly seek information about the stage of the disease, treatment options, and side effects. Post-treatment, the need for information about recovery becomes significant.
Prognostic Information Preferences in Advanced Cancer Patients
Advanced cancer patients generally desire honest communication from their healthcare providers. Most patients want a broad indication of their prognosis, although preferences for detailed quantitative information vary. Realistic awareness of their condition helps patients gain control and plan for the end of life, which can foster hope. However, for some, detailed prognostic information can diminish hope, necessitating a careful, individualized approach to prognostic discussions.
Information Needs of Partners and Family Members
The information needs of partners and family members of cancer patients are often overlooked. A review of literature from 1998 to 2008 revealed that partners and family members primarily need information about supportive care rather than medical details. This area requires more empirical research to better understand and address these needs.
Deep Learning in Breast Cancer Information Extraction
Deep learning methods, particularly those fine-tuning BERT (Bidirectional Encoder Representations from Transformers), have shown superior performance in extracting comprehensive clinical information from breast cancer documents. These methods achieved high accuracy in named entity recognition (NER) and relation extraction, outperforming traditional machine learning algorithms. This advancement supports broader applications in medical information extraction tasks.
Cancer as a Disease of Information
An alternative perspective on cancer views it as a disease of information, building on Claude Shannon's information theory. This approach suggests that cancer results from missing, noisy, or distorted information. Understanding cancer through this lens could lead to novel treatment strategies focused on restoring normal information flows within cells, contrasting with current treatments that may exacerbate information disruptions.
Physical Traits of Cancer
The physical properties of tumors, such as solid stresses, interstitial fluid pressure, stiffness, and altered microarchitecture, play a significant role in cancer progression and treatment resistance. These physical traits interact with biological mechanisms to promote tumor growth, immune evasion, and metastasis. Understanding these physical traits can lead to new therapeutic strategies and improve treatment outcomes.
Machine Learning in Cancer Detection
Machine learning techniques, including supervised, unsupervised, and deep learning, have significantly advanced cancer detection. These methods have been applied to various cancer types, such as breast, brain, lung, liver, skin cancer, and leukemia, improving accuracy, sensitivity, and specificity. Despite these advancements, challenges remain, such as the need for better diagnostic tools and treatment strategies.
Histological Types of Breast Cancer
Breast cancer is a heterogeneous disease with multiple histological and biological subtypes. Recent studies have identified specific genetic mutations associated with different histological types, such as the ETV6-NTRK3 fusion gene in secretory carcinomas and the MYB-NFIB translocation in adenoid cystic carcinomas. Understanding these genotypic-phenotypic correlations can lead to the identification of novel therapeutic targets.
Tumor Immunology and Evolution
The interaction between tumors and their microenvironment, particularly the immune system, is crucial in cancer progression. Immune cells play different roles at various stages of tumor development, and understanding these interactions can inform therapeutic strategies. Integrating immune parameters into clinical stratification systems can improve prognostic and predictive information, guiding better clinical decisions.
Conclusion
Cancer research spans a wide array of topics, from understanding patients' information needs to leveraging advanced machine learning techniques for better diagnosis and treatment. Addressing the diverse needs of patients, their families, and healthcare providers, while integrating new scientific perspectives and technologies, is essential for advancing cancer care and improving patient outcomes.
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