Second primary cancer induction is a topic of growing concern. All the steps in the radiotherapy chain involving the use of ionizing radiation behave as a source of potential risks: from the diagnostic or planning computerized tomography (CT), to the peripheral (out-of-field) photon and neutron doses deposited during photon or hadron radiotherapy, passing through to the X-ray Cone-Beam Computed Tomography systems (CBCT) for Image-Guided Radiation Therapy (IGRT). There are a significant number of researchers working on that issue. Still, there is a lot more to do, such as, assessing problems associated with the dosimetry under no reference conditions, making improvements to the current risk models (with a more biology focused input), and the training regarding those models for more organs by using epidemiological data of patients treated with modern techniques.
We would like to address the idea of counting with a well trained risk mechanistic model which incorporates more of the relevant biology but without excessive complexity so that it can be considered clinically applicable on a broad scale. Ideally, these models would consider the absorbed dose to out-of-field organs of each patient during his RT treatment.
There are already models for risk estimations which are either very complex and difficult to be implemented in the clinic, or perhaps too simple as a result of a lack of individual biology and patient data. Another issue is that the parameterization of those models were done either using epidemiological data which did not include modern treatment techniques (at least IMRT) or without parameters for all radiosensitive organs.
There are already solutions for neutron and photon peripheral dose estimations. Peridose and PERIPHOCAL (PERIpheral PHOton CALculation) are examples of the latter, which can be used for specific radiation therapy (RT) treatments. However they still suffer from drawbacks as they are either not usable for modern RT (Peridose) or do not include 3D dose calculations (PERIPHOCAL). Additionally, they do not usually provide dose-volume histograms (DVHs) as required by some of the risk models.
There are already solutions for neutron and photon peripheral dosimetry, but the problem of absolute absorbed dose calculations for out-of-field conditions (i.e. non reference conditions) has not been solved.
According to the issues mentioned above, the specific interest areas are:
- Absolute photon dose determination under out-of-field conditions.
-3D peripheral dose models able to consider the specific patient and treatment characteristics and simple enough to be clinically applicable.
-Epidemiological studies which provide information of second cancer rate induction for different organs and specific modern RT treatments. They will serve to train the risk models and to check those once they are proposed.
- Developing risk models applicable in the clinic using individual patient and treatment parameters.
Important 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
Second primary cancer induction is a topic of growing concern. All the steps in the radiotherapy chain involving the use of ionizing radiation behave as a source of potential risks: from the diagnostic or planning computerized tomography (CT), to the peripheral (out-of-field) photon and neutron doses deposited during photon or hadron radiotherapy, passing through to the X-ray Cone-Beam Computed Tomography systems (CBCT) for Image-Guided Radiation Therapy (IGRT). There are a significant number of researchers working on that issue. Still, there is a lot more to do, such as, assessing problems associated with the dosimetry under no reference conditions, making improvements to the current risk models (with a more biology focused input), and the training regarding those models for more organs by using epidemiological data of patients treated with modern techniques.
We would like to address the idea of counting with a well trained risk mechanistic model which incorporates more of the relevant biology but without excessive complexity so that it can be considered clinically applicable on a broad scale. Ideally, these models would consider the absorbed dose to out-of-field organs of each patient during his RT treatment.
There are already models for risk estimations which are either very complex and difficult to be implemented in the clinic, or perhaps too simple as a result of a lack of individual biology and patient data. Another issue is that the parameterization of those models were done either using epidemiological data which did not include modern treatment techniques (at least IMRT) or without parameters for all radiosensitive organs.
There are already solutions for neutron and photon peripheral dose estimations. Peridose and PERIPHOCAL (PERIpheral PHOton CALculation) are examples of the latter, which can be used for specific radiation therapy (RT) treatments. However they still suffer from drawbacks as they are either not usable for modern RT (Peridose) or do not include 3D dose calculations (PERIPHOCAL). Additionally, they do not usually provide dose-volume histograms (DVHs) as required by some of the risk models.
There are already solutions for neutron and photon peripheral dosimetry, but the problem of absolute absorbed dose calculations for out-of-field conditions (i.e. non reference conditions) has not been solved.
According to the issues mentioned above, the specific interest areas are:
- Absolute photon dose determination under out-of-field conditions.
-3D peripheral dose models able to consider the specific patient and treatment characteristics and simple enough to be clinically applicable.
-Epidemiological studies which provide information of second cancer rate induction for different organs and specific modern RT treatments. They will serve to train the risk models and to check those once they are proposed.
- Developing risk models applicable in the clinic using individual patient and treatment parameters.
Important 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
