{"id":149,"date":"2024-02-22T19:18:04","date_gmt":"2024-02-22T19:18:04","guid":{"rendered":"https:\/\/dornsife.usc.edu\/kuhn\/?page_id=149"},"modified":"2024-12-23T23:09:34","modified_gmt":"2024-12-23T23:09:34","slug":"mathematical-oncology","status":"publish","type":"page","link":"https:\/\/dornsife.usc.edu\/kuhn\/mathematical-oncology\/","title":{"rendered":"Mathematical Oncology"},"content":{"rendered":"\n\n\n\n  \n    \n\n\n\n\n\n\n<div\n  class=\"cc--component-container cc--rich-text \"\n\n  \n  \n  \n  \n  \n  \n  >\n  <div class=\"c--component c--rich-text\"\n    \n      >\n\n    \n      \n<div class=\"f--field f--wysiwyg\">\n\n    \n  <p><span style=\"font-weight: 400;\">The computational research endeavors at CSI-Cancer are centered on three key areas: disease forecasting, therapy efficacy, and survival prediction. By focusing on disease forecasting, the team aims to predict disease progression and potential relapses, enabling proactive and timely interventions. Research on therapy efficacy evaluates how well treatments work for different patient groups, identifying the most effective therapies and minimizing side effects. Survival prediction models estimate patient outcomes based on various clinical and molecular factors, helping tailor personalized treatment plans. By advancing these specific areas, CSI-Cancer aims to develop effective, individualized treatment strategies that improve patient outcomes and quality of life.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Our work is transforming how we understand and manage cancer metastasis, bringing hope for more effective treatments and better patient care. Here\u2019s how our work is transforming patient care:<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\"><strong>1.<\/strong> <\/span><b>Predicting How Cancer Spreads<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"color: #0000ff;\"><a style=\"color: #0000ff;\" href=\"https:\/\/dornsife.usc.edu\/kuhn\/bladder_cancer\"><b>Bladder Cancer<\/b><\/a><\/span><span style=\"font-weight: 400;\">: We&#8217;ve developed an innovative online tool to predict how bladder cancer spreads over time. By analyzing patient data, including genetics and imaging, our platform helps doctors tailor treatments to individual patients, leading to better outcomes.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/dornsife.usc.edu\/kuhn\/?page_id=722&amp;preview=true\"><b><span style=\"color: #0000ff;\">Breast Cancer<\/span><\/b><\/a><span style=\"font-weight: 400;\">: For patients with inflammatory breast cancer, our advanced model predicts the likelihood and timing of bone metastasis. This helps doctors intervene earlier and more effectively to prevent the spread of cancer to bones.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Lung Cancer<\/b><span style=\"font-weight: 400;\">: In non-small cell lung cancer with specific genetic mutations, brain metastasis often follows lung metastasis. Our research highlights this pattern, emphasizing the importance of monitoring lung metastases to prevent the spread to the brain.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\"><strong>2.<\/strong> <\/span><b>Predicting Patient Survival<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Bladder Cancer<\/b><span style=\"font-weight: 400;\">: Using cutting-edge machine learning, we can predict the likelihood of cancer returning and patient survival after bladder cancer surgery. These personalized predictions help guide follow-up care and improve patient management.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\"><strong>3.<\/strong> <\/span><b>Assessing Therapy Effectiveness<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Breast Cancer<\/b><span style=\"font-weight: 400;\">: We developed a model to predict how well patients with advanced breast cancer will respond to a specific drug (CDK4\/6 inhibitors). This helps doctors choose the best treatment plans for individual patients, increasing the chances of successful outcomes.<\/span><\/li>\n<\/ul>\n<p><strong>4. <\/strong><b>Monitoring Symptoms<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>COVID-19<\/b><span style=\"font-weight: 400;\">: Our models predict the onset and progression of COVID-19 symptoms, taking into account different viral variants. This helps with early detection, timely interventions, and understanding how different strains of the virus affect symptom development.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Our research is revolutionizing the way we understand and treat cancer. By predicting disease spread, survival rates, therapy response, and symptom progression, we are empowering doctors with powerful tools to personalize treatments, improve patient outcomes, and intervene early when it matters most. Together, we are working towards a future where every cancer patient receives the best possible treatment tailored to their unique needs.<\/span><\/p>\n<p>&nbsp;<\/p>\n\n\n\n<\/div>\n\n\n  <\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":17,"featured_media":340,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-149","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Mathematical Oncology - Convergent Science Institute in Cancer<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dornsife.usc.edu\/kuhn\/mathematical-oncology\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Mathematical Oncology - 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