Cancer classification, treatment5 answersCancer classification and treatment involve various methods to predict patient outcomes and treatment effectiveness. Classification can be based on targetable events within a tumor, leading to more accurate personalized treatment approaches. Common cancer treatments include surgery, radiation therapy, chemotherapy, immunotherapy, targeted therapy, hormone therapy, and stem cell transplantation. Machine Learning techniques like Artificial Neural Networks (ANN), Decision Trees (DT), Support Vector Machines (SVM), and Bayesian Networks (BNs) have shown high accuracy in classifying cancer datasets, aiding in treatment decisions. Integrated cancer classification models may enhance future treatments by eliminating unclassifiable cancers and providing a more comprehensive understanding of cancer types and their treatments.
How has the way of disease diagnosis changed over time?4 answersThe evolution of disease diagnosis has seen significant changes over time. Initially relying on simple observations and patient history, modern diagnostics now incorporate advanced technologies like nanotechnology, enzyme-based diagnostics, and molecular biology. The emergence of digital medicine has further revolutionized the field by integrating medical modeling, large-scale computation, and smart technologies for noninvasive disease assessment. Future visions include personalized medicine programs addressing prevalent chronic conditions through collaborative efforts involving various stakeholders. Advancements in miniaturization and the development of "Lab-on-a-chip" systems are paving the way for faster, more precise, and minimally invasive diagnostic tools. These collective changes highlight a shift towards more efficient, accurate, and patient-centric approaches to disease diagnosis.
Cancer {Classification} at the {Crossroads}4 answersCancer classification is evolving in the era of cancer genomics, with the inclusion of biologic and molecular-genetic features. These advancements are impacting cancer diagnosis and treatment, allowing for refined classifications and personalized treatment approaches based on molecular profiling. Additionally, the crossroads of epigenetics and immune checkpoint therapy hold tremendous potential for improving anti-tumor immune responses and achieving sustained responses in immuno-oncology. Epigenetic modifiers, such as SETDB1 and LSD1, play critical roles in regulating tumor cell-intrinsic immunity and T-cell exhaustion, offering new avenues for enhancing the effectiveness of immune checkpoint inhibitors (ICI) in 'cold' tumor types. Furthermore, adoptive cell therapy (ACT) utilizing antitumor immune cells, such as natural killer (NK) cells and cytokine-induced killers, has shown promise in cancer treatment, particularly in enhancing immune responses to established tumors. Overall, cancer classification is at a crossroads, incorporating molecular-genetic features and exploring the intersection of epigenetics and immunotherapy to improve patient outcomes.
Cancer classification using deep learning4 answersDeep learning techniques have been widely used for cancer classification. In the field of colon cancer classification, several deep learning approaches such as convolutional neural network (CNN), recurrent neural network (RNN), transfer learning, AlexNet, and GoogLeNet have been compared. The comparative assessment showed that GoogLeNet achieved the highest accuracy, sensitivity, and specificity for colon cancer classification. In the case of breast cancer classification, deep convolutional neural networks have shown promising results. A new deep CNN architecture has been proposed for the classification of breast tumors in ultrasound images. Another study focused on the classification of breast cancer histopathological images using Fourier Ptychographic (FP) and deep learning. The proposed technique outperformed traditional techniques in classifying breast cancer images. For skin cancer detection, a Convolutional Neural Network (CNN) has been developed and achieved an accuracy of 90% in classifying skin lesions as cancerous or noncancerous. In the field of breast cancer detection, deep learning techniques have been used to identify regions of interest in histological images, and satisfactory outcomes have been obtained using the Gradient visualization method and the MeanShift selection method.
Cancer development as evolutionary process5 answersCancer development is considered an evolutionary process, involving the evolution of somatic cells within multicellular organisms. The familiar dynamics of organismal evolution, such as mutations, natural selection, genetic drift, and migration, also occur among the cells of multicellular organisms, leading to cancer in some cases. Medical interventions, including drugs, radiation, and the immune system, impose selective pressures on tumor cells, selecting for mutant cells that are resistant to the intervention. The somatic mutation theory (SMT) of cancer describes driver mutations that trigger oncogenesis and passenger mutations characteristic of genetic microevolution in cancer. Chronic inflammation plays a role in cancer development, with pro-inflammatory factors facilitating the generation of somatic mutations and viral mutations under the microenvironment of non-resolving inflammation. A proper evolutionary developmental framework for cancer can bridge the evolutionary and developmental perspectives, providing new insights for cancer prevention and treatment.
What is tumor evolution?5 answersTumor evolution refers to the process by which tumors develop and progress over time. It is driven by genetic and epigenetic alterations that enable uncontrolled growth, expansion to neighboring and distant tissues, and resistance to treatment. Tumors evolve through the acquisition of fitness-associated transcriptional programs, allowing for rapid expansion and clonal sweep of subclones capable of metastasis. The hierarchical nature of tumor evolution is characterized by the loss of stable cellular states, followed by the adoption of distinct transcriptional programs that enhance fitness. Tumor evolution is influenced by factors such as cell plasticity, which allows for reversible reprogramming and adaptation to environmental constraints and treatment. The presence of tumor heterogeneity provides intrinsic motivation for the process of tumor evolution. Understanding tumor evolution is crucial for developing effective strategies to combat cancer and overcome treatment resistance.