Breast cancer is one of the most rapidly increasing diseases throughout the world. It has many prognostic, pathological, and clinical subgroups rendering it a heterogeneous disease. Although early-stage cancer diagnosis renders higher chances of survival techniques like mammography, ultrasound, tomography, biopsy, etc. are time-consuming, expensive, and sometimes painful. Even after diagnosis, there are multiple regimes of medicine and treatment options and oncologists often find it difficult to select appropriate choice as every patient does not respond in a similar way.
Due to the complex physiology and molecular etiology of breast cancer that varies with its subtypes, the response of each individual to cancer care differs. The selection of appropriate preventive measures, diagnostic tools, drug choices, and treatment strategies is very important and so are the enormous chances of human errors. Human life cannot be subjected to the trial and error process for reducing the cancer burden and increasing the survival rate; this gave rise to the concept of personalized medicine. It deals with the genetic and epigenetic makeup of an individual exploring the pathways by which drug acts and devising a suitable strategy. Based on the genetic profile of an individual, personalized care for breast cancer improves the chances of prevention, diagnosis, recovery, and survival. With the advancements in the field of molecular biology, a reservoir of data based on the genomic makeup of breast cancer patients has been obtained over the years. The progress of omics has revolutionized the field, enhanced the reservoir of information related to breast cancer, and has recently incorporated artificial intelligence into it. Clinicians and bioengineers are frequently using different tools of artificial intelligence to decipher big data and overcome human errors.
The topic ‘An era of personalized medicine in breast cancer: Integrating artificial intelligence into practice’ is a relatively new field and an emerging concept that has wide applicability in the fields of oncology, clinical medicine, bioinformatics, bioengineering, and molecular biology. In this Research Topic, we invite papers in the field of how artificial intelligence is being used to decipher and use big data to improve breast cancer treatment, drug selection and personalized medicine.
Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.
Breast cancer is one of the most rapidly increasing diseases throughout the world. It has many prognostic, pathological, and clinical subgroups rendering it a heterogeneous disease. Although early-stage cancer diagnosis renders higher chances of survival techniques like mammography, ultrasound, tomography, biopsy, etc. are time-consuming, expensive, and sometimes painful. Even after diagnosis, there are multiple regimes of medicine and treatment options and oncologists often find it difficult to select appropriate choice as every patient does not respond in a similar way.
Due to the complex physiology and molecular etiology of breast cancer that varies with its subtypes, the response of each individual to cancer care differs. The selection of appropriate preventive measures, diagnostic tools, drug choices, and treatment strategies is very important and so are the enormous chances of human errors. Human life cannot be subjected to the trial and error process for reducing the cancer burden and increasing the survival rate; this gave rise to the concept of personalized medicine. It deals with the genetic and epigenetic makeup of an individual exploring the pathways by which drug acts and devising a suitable strategy. Based on the genetic profile of an individual, personalized care for breast cancer improves the chances of prevention, diagnosis, recovery, and survival. With the advancements in the field of molecular biology, a reservoir of data based on the genomic makeup of breast cancer patients has been obtained over the years. The progress of omics has revolutionized the field, enhanced the reservoir of information related to breast cancer, and has recently incorporated artificial intelligence into it. Clinicians and bioengineers are frequently using different tools of artificial intelligence to decipher big data and overcome human errors.
The topic ‘An era of personalized medicine in breast cancer: Integrating artificial intelligence into practice’ is a relatively new field and an emerging concept that has wide applicability in the fields of oncology, clinical medicine, bioinformatics, bioengineering, and molecular biology. In this Research Topic, we invite papers in the field of how artificial intelligence is being used to decipher and use big data to improve breast cancer treatment, drug selection and personalized medicine.
Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.
