State-of-the-Art and Future Prospects of Ion Beam Therapy: Physical and Radiobiological Aspects

Scholz, M.

doi:10.1109/trpms.2019.2935240

Cited by 15 publications

(8 citation statements)

References 178 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, since the RBE of carbon ions strongly depends on the ion energy, treatment plans should be optimized by accounting for the variation of the RBE over the irradiated volume. This optimization generally aims to obtain a uniform cell survival over the tumor volume and is performed by coupling computer simulations at the subcellular scale with biophysical models of radiation-action (Kanai et al 1999, Inaniwa et al 2010, Inaniwa et al 2015, Scholz 2019. The importance of this process was recently highlighted by clinical proton therapy studies which proved that disregarding these RBE changes could result in adverse treatment outcomes such as zones of increased toxicity in patients (Peelers et al 2016) and a higher rib fracture rate due to a shift in the ion biological range (Wang et al 2020).…”

Section: Introductionmentioning

confidence: 99%

The Mayo Clinic Florida microdosimetric kinetic model of clonogenic survival: formalism and first benchmark against in vitro and in silico data

2022

View full text Add to dashboard Cite

Objective. To develop a new model (Mayo Clinic Florida microdosimetric kinetic model, MCF MKM) capable of accurately describing the in vitro clonogenic survival at low and high linear energy transfer (LET) using single-event microdosimetric spectra in a single target. Methodology. The MCF MKM is based on the ‘post-processing average’ implementation of the non-Poisson microdosimetric kinetic model and includes a novel expression to compute the particle-specific quadratic-dependence of the cell survival with respect to dose (β of the linear-quadratic model). A new methodology to a priori calculate the mean radius of the MCF MKM subnuclear domains is also introduced. Lineal energy spectra were simulated with the Particle and Heavy Ion Transport code System (PHITS) for 1H, 4He, 12C, 20Ne, 40Ar, 56Fe, and 132Xe ions and used in combination with the MCF MKM to calculate the ion-specific LET-dependence of the relative biological effectiveness (RBE) for Chinese hamster lung fibroblasts (V79 cell line) and human salivary gland tumor cells (HSG cell line). The results were compared with in vitro data from the Particle Irradiation Data Ensemble (PIDE) and in silico results of different models. The possibility of performing experiment-specific predictions to explain the scatter in the in vitro RBE data was also investigated. Finally, a sensitivity analysis on the model parameters is also included. Main results. The RBE values predicted with the MCF MKM were found to be in good agreement with the in vitro data for all tested conditions. Though all MCF MKM model parameters were determined a priori, the accuracy of the MCF MKM was found to be comparable or superior to that of other models. The model parameters determined a priori were in good agreement with the ones obtained by fitting all available in vitro data. Significance. The MCF MKM will be considered for implementation in cancer radiotherapy treatment planning with accelerated ions.

show abstract

Section: Introductionmentioning

confidence: 99%

The Mayo Clinic Florida microdosimetric kinetic model of clonogenic survival: formalism and first benchmark against in vitro and in silico data

2022

View full text Add to dashboard Cite

show abstract

“…Cancer radiotherapy treatments with ions are optimized to account for their different relative biological effectiveness (RBE) with respect to conventional X-rays [ 1 ]. The modelled endpoint considered for treatment planning is the cellular clonogenic survival [ 2 ] due to its relevance for clinical tumor control calculations [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

The Mayo Clinic Florida Microdosimetric Kinetic Model of Clonogenic Survival: Application to Various Repair-Competent Rodent and Human Cell Lines

Parisi

Beltran

Furutani

2022

IJMS

View full text Add to dashboard Cite

The relative biological effectiveness (RBE) calculations used during the planning of ion therapy treatments are generally based on the microdosimetric kinetic model (MKM) and the local effect model (LEM). The Mayo Clinic Florida MKM (MCF MKM) was recently developed to overcome the limitations of previous MKMs in reproducing the biological data and to eliminate the need for ion-exposed in vitro data as input for the model calculations. Since we are considering to implement the MCF MKM in clinic, this article presents (a) an extensive benchmark of the MCF MKM predictions against corresponding in vitro clonogenic survival data for 4 rodent and 10 cell lines exposed to ions from 1H to 238U, and (b) a systematic comparison with published results of the latest version of the LEM (LEM IV). Additionally, we introduce a novel approach to derive an approximate value of the MCF MKM model parameters by knowing only the animal species and the mean number of chromosomes. The overall good agreement between MCF MKM predictions and in vitro data suggests the MCF MKM can be reliably used for the RBE calculations. In most cases, a reasonable agreement was found between the MCF MKM and the LEM IV.

show abstract

“…Proton radiotherapy (RT) has shown several advantages in dose conformity, tumor control probability, and normal-tissue complications over conventional RT such as x-ray or electron therapy [1][2][3]. However, limitations in our ability to accurately determine the position of the proton Bragg peak (BP) during planning, and to verify that it matches the actual BP position and range of the beam in the patient during treatment, have thus far limited the ability of RT practitioners to take full advantage of the high conformality and steep distal dose gradients achievable with proton RT [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Applications of Machine Learning to Improve the Clinical Viability of Compton Camera Based in vivo Range Verification in Proton Radiotherapy

et al. 2022

View full text Add to dashboard Cite

We studied the application of a deep, fully connected Neural Network (NN) to process prompt gamma (PG) data measured by a Compton camera (CC) during the delivery of clinical proton radiotherapy beams. The network identifies 1) recorded “bad” PG events arising from background noise during the measurement, and 2) the correct ordering of PG interactions in the CC to help improve the fidelity of “good” data used for image reconstruction. PG emission from a tissue-equivalent target during irradiation with a 150 MeV proton beam delivered at clinical dose rates was measured with a prototype CC. Images were reconstructed from both the raw measured data and the measured data that was further processed with a neural network (NN) trained to identify “good” and “bad” PG events and predict the ordering of individual interactions within the good PG events. We determine if NN processing of the CC data could improve the reconstructed PG images to a level in which they could provide clinically useful information about the in vivo range and range shifts of the proton beams delivered at full clinical dose rates. Results showed that a deep, fully connected NN improved the achievable contrast to noise ratio (CNR) in our images by more than a factor of 8x. This allowed the path, range, and lateral width of the clinical proton beam within a tissue equivalent target to easily be identified from the PG images, even at the highest dose rates of a 150 MeV proton beam used for clinical treatments. On average, shifts in the beam range as small as 3 mm could be identified. However, when limited by the amount of PG data measured with our prototype CC during the delivery of a single proton pencil beam (∼1 × 109 protons), the uncertainty in the reconstructed PG images limited the identification of range shift to ∼5 mm. Substantial improvements in CC images were obtained during clinical beam delivery through NN pre-processing of the measured PG data. We believe this shows the potential of NNs to help improve and push CC-based PG imaging toward eventual clinical application for proton RT treatment delivery verification.

show abstract

State-of-the-Art and Future Prospects of Ion Beam Therapy: Physical and Radiobiological Aspects

Cited by 15 publications

References 178 publications

The Mayo Clinic Florida microdosimetric kinetic model of clonogenic survival: formalism and first benchmark against in vitro and in silico data

The Mayo Clinic Florida microdosimetric kinetic model of clonogenic survival: formalism and first benchmark against in vitro and in silico data

The Mayo Clinic Florida Microdosimetric Kinetic Model of Clonogenic Survival: Application to Various Repair-Competent Rodent and Human Cell Lines

Applications of Machine Learning to Improve the Clinical Viability of Compton Camera Based in vivo Range Verification in Proton Radiotherapy

Contact Info

Product

Resources

About