In recent years, the field of personalized medicine has seen remarkable advancements, particularly in the field of cancer research. The advent of novel genetic and statistical approaches has revolutionized our ability to estimate individual cancer risk and tailor therapeutic strategies to meet the unique needs of patients. With the increasing availability of genomic data and sophisticated analytical tools, researchers are now better equipped than ever to delve into the intricate genetic role in cancer development and progression.
Despite these significant strides, there remains a pressing need to further explore and harness the potential of these innovative approaches. While traditional risk assessment models have served as valuable tools in cancer prevention and management, they often lack the granularity needed to accurately predict an individual's likelihood of developing cancer or their response to specific treatments. By integrating cutting-edge genetic technologies and advanced statistical methodologies, we aim to refine and enhance our ability to predict cancer risk on an individualized basis and tailor therapeutic interventions accordingly.
The overarching goal of this Research Topic is to showcase the latest advancements in the application of novel genetic and statistical approaches in the era of personalized medicine, with a specific focus on individual cancer risk estimation and patients' therapeutic strategy decision-making. We seek to highlight research that pushes the boundaries of traditional risk assessment models, leveraging innovative techniques to improve the accuracy and precision of cancer risk prediction. Additionally, we aim to explore how these advancements are shaping clinical decision-making processes, empowering healthcare providers to deliver more personalized and effective treatment strategies to cancer patients.
We welcome Original Research, Review, Mini Review and Perspective articles on themes including, but not limited to:
• Integration of genomic data and advanced statistical models for individualized cancer risk assessment.
• Development and validation of predictive biomarkers for cancer risk stratification.
• Application of machine learning and artificial intelligence algorithms in cancer risk prediction and therapeutic decision-making.
• Exploration of novel genetic variants, their functional role by novel approaches as CRISPR/cas9 and their impact on cancer susceptibility and treatment response.
• Implementation of personalized medicine approaches in clinical practice, including challenges and opportunities for adoption and implementation.
• Ethical considerations and implications of utilizing genetic and statistical approaches in personalized cancer care.
Overall, combining contributions from a diverse range of leading researchers in the field, we aim to provide a comprehensive overview of the current landscape of personalized cancer risk estimation and therapeutic decision-making. Through this special issue, we hope to foster interdisciplinary dialogue and collaboration, ultimately driving forward the translation of innovative research findings into improved clinical outcomes for cancer patients.
Keywords:
personalized medicine, cancer risk estimation, therapeutic decision-making, genomic data, statistical approaches, biomarkers, machine learning, artificial intelligence, genetic variants, clinical practice, ethical considerations.
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
In recent years, the field of personalized medicine has seen remarkable advancements, particularly in the field of cancer research. The advent of novel genetic and statistical approaches has revolutionized our ability to estimate individual cancer risk and tailor therapeutic strategies to meet the unique needs of patients. With the increasing availability of genomic data and sophisticated analytical tools, researchers are now better equipped than ever to delve into the intricate genetic role in cancer development and progression.
Despite these significant strides, there remains a pressing need to further explore and harness the potential of these innovative approaches. While traditional risk assessment models have served as valuable tools in cancer prevention and management, they often lack the granularity needed to accurately predict an individual's likelihood of developing cancer or their response to specific treatments. By integrating cutting-edge genetic technologies and advanced statistical methodologies, we aim to refine and enhance our ability to predict cancer risk on an individualized basis and tailor therapeutic interventions accordingly.
The overarching goal of this Research Topic is to showcase the latest advancements in the application of novel genetic and statistical approaches in the era of personalized medicine, with a specific focus on individual cancer risk estimation and patients' therapeutic strategy decision-making. We seek to highlight research that pushes the boundaries of traditional risk assessment models, leveraging innovative techniques to improve the accuracy and precision of cancer risk prediction. Additionally, we aim to explore how these advancements are shaping clinical decision-making processes, empowering healthcare providers to deliver more personalized and effective treatment strategies to cancer patients.
We welcome Original Research, Review, Mini Review and Perspective articles on themes including, but not limited to:
• Integration of genomic data and advanced statistical models for individualized cancer risk assessment.
• Development and validation of predictive biomarkers for cancer risk stratification.
• Application of machine learning and artificial intelligence algorithms in cancer risk prediction and therapeutic decision-making.
• Exploration of novel genetic variants, their functional role by novel approaches as CRISPR/cas9 and their impact on cancer susceptibility and treatment response.
• Implementation of personalized medicine approaches in clinical practice, including challenges and opportunities for adoption and implementation.
• Ethical considerations and implications of utilizing genetic and statistical approaches in personalized cancer care.
Overall, combining contributions from a diverse range of leading researchers in the field, we aim to provide a comprehensive overview of the current landscape of personalized cancer risk estimation and therapeutic decision-making. Through this special issue, we hope to foster interdisciplinary dialogue and collaboration, ultimately driving forward the translation of innovative research findings into improved clinical outcomes for cancer patients.
Keywords:
personalized medicine, cancer risk estimation, therapeutic decision-making, genomic data, statistical approaches, biomarkers, machine learning, artificial intelligence, genetic variants, clinical practice, ethical considerations.
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.