The role of radiotherapy in the treatment of breast cancer is well established to reduce the risk of locoregional recurrence, and distant metastases as well as improve overall survival. The choice of radiotherapy planning and delivery techniques, however, depend heavily on mulitple clinical and logistical factors such as the laterality of treatment, the extent of disease, patient body habitus, age, ability to travel to the facility for treatment, and risk of developing secondary cancers in the future.
Regardless of the clinical requirements, the technique used for planning and delivery needs to be optimized. Treatment planning and delivery for breast cancer have in recent years seen a significant change in application and advances of novel methods and techniques. Advances have been made in the application of volumetric modulated arc therapy (VMAT) or IMRT in the treatment of complex anatomies. Knowledge-based planning (KBP), artificial intelligence (AI), machine learning and automated treatment planning are being used to predict optimal dose distributions as well as anatomies that could benefit from specific treatment techniques. Proton therapy for breast cancer has seen a tremendous growth potentially offering dosimetric benefits for patients receiving re-irradation or a mastectomy with immediate breast reconstruction. The application of hypofractionation for whole breast radiation therapy as well as the availability of long-term randomized phase III trial data comparing whole breast to accelerated partial breast radiotherapy is favoring the movement away from standard fractionation in turn reducing toxicity, improving cosmesis and improving patient outcomes. The ability of the MR LINAC technology to visualize the breast tumor in real-time with superior soft-tissue contrast compared to computed tomography enabling adaptive radiotherapy has the potential to further improve outcomes.
This Research Topic aims to discuss the advances in radiotherapy for breast cancer with the goal of imrpoving application of the latest techniques to improve dosimetric as well as clinical outcomes for the patient with the potential to improve the quality of life. Potential topics include but are not limited to the following:
1) VMAT for breast and risk of secondary cancers
2) Machine learning and RapidPlan in breast treatment planning
3) Proton therapy and its role in a select cohort of patients such as post-mastectomy, re-treatment or in a younger population.
4) MR Linac and adaptive planning for breast cancer
5) Partial breast
6) Role of deep inspiration breath hold for dosimetric improvement in the supraclavicular region
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.
The role of radiotherapy in the treatment of breast cancer is well established to reduce the risk of locoregional recurrence, and distant metastases as well as improve overall survival. The choice of radiotherapy planning and delivery techniques, however, depend heavily on mulitple clinical and logistical factors such as the laterality of treatment, the extent of disease, patient body habitus, age, ability to travel to the facility for treatment, and risk of developing secondary cancers in the future.
Regardless of the clinical requirements, the technique used for planning and delivery needs to be optimized. Treatment planning and delivery for breast cancer have in recent years seen a significant change in application and advances of novel methods and techniques. Advances have been made in the application of volumetric modulated arc therapy (VMAT) or IMRT in the treatment of complex anatomies. Knowledge-based planning (KBP), artificial intelligence (AI), machine learning and automated treatment planning are being used to predict optimal dose distributions as well as anatomies that could benefit from specific treatment techniques. Proton therapy for breast cancer has seen a tremendous growth potentially offering dosimetric benefits for patients receiving re-irradation or a mastectomy with immediate breast reconstruction. The application of hypofractionation for whole breast radiation therapy as well as the availability of long-term randomized phase III trial data comparing whole breast to accelerated partial breast radiotherapy is favoring the movement away from standard fractionation in turn reducing toxicity, improving cosmesis and improving patient outcomes. The ability of the MR LINAC technology to visualize the breast tumor in real-time with superior soft-tissue contrast compared to computed tomography enabling adaptive radiotherapy has the potential to further improve outcomes.
This Research Topic aims to discuss the advances in radiotherapy for breast cancer with the goal of imrpoving application of the latest techniques to improve dosimetric as well as clinical outcomes for the patient with the potential to improve the quality of life. Potential topics include but are not limited to the following:
1) VMAT for breast and risk of secondary cancers
2) Machine learning and RapidPlan in breast treatment planning
3) Proton therapy and its role in a select cohort of patients such as post-mastectomy, re-treatment or in a younger population.
4) MR Linac and adaptive planning for breast cancer
5) Partial breast
6) Role of deep inspiration breath hold for dosimetric improvement in the supraclavicular region
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.
