Citations

Abstract Background In order to compute the relative biological effectiveness (RBE) of ion radiation therapy with the Mayo Clinic Florida microdosimetric kinetic model (MCF MKM), it is necessary to process entire microdosimetric distributions. Therefore, a posteriori RBE recalculations (i.e., for a different cell line or another biological endpoint) would require whole spectral information. It is currently not practical to compute and store all this data for each clinical voxel. Purpose To develop a methodology that allows to store a limited amount of physical information without losing accuracy in the RBE calculations nor the possibility of a posteriori RBE recalculations. Methods Computer simulations for four monoenergetic 12 C ion beams and a 12 C ion spread‐out Bragg peak (SOBP) were performed to assess lineal energy distributions as a function of the depth within a water phantom. These distributions were used in combination with the MCF MKM to compute the in vitro clonogenic survival RBE for human salivary gland tumor cells (HSG cell line) and human skin fibroblasts (NB1RGB cell line). The RBE values were also calculated with a new abridged microdosimetric distribution methodology (AMDM) and compared with the reference RBE calculations using the entire distributions. Results The maximum relative deviation between the RBE values computed using the entire distributions and the AMDM was 0.61% (monoenergetic beams) and 0.49% (SOBP) for the HSG cell line, while 0.45% (monoenergetic beams) and 0.26% (SOBP) for the NB1RGB cell line. Conclusion The excellent agreement between the RBE values computed using the entire lineal energy distributions and the AMDM represents a milestone for the clinical implementation of the MCF MKM.

AbstractBackgroundDiscrete spot scanning (DSS) is the commonly used method for proton pencil beam scanning (PBS). There is lack of data on the dose‐driven continuous scanning (DDCS).PurposeTo investigate delivery benefits and dosimetric implications of DDCS versus DSS for PBS systems.MethodsThe irradiation duty factor, beam delivery time (BDT), and dose deviation were simulated for eight treatment plans in prostate, head and neck, liver, and lung, with both conventional fractionation and hypofractionation schemes. DDCS results were compared with those of DSS.ResultsThe DDCS irradiation duty factor (range, 11%–41%) was appreciably improved compared to DSS delivery (range, 4%–14%), within which, hypofractionation schemes had greater improvement than conventional fractionation. With decreasing stop ratio constraints, the DDCS BDT reduction was greater, but dose deviation also increased. With stop ratio constraints of 2, 1, 0.5, and 0, DDCS BDT reduction reached to 6%, 10%, 12%, and 15%, respectively, and dose deviation reached to 0.6%, 1.7%, 3.0%, and 5.2% root mean square error in PTV DVH, respectively. The 3%/2‐mm gamma passing rate was greater than 99% with stop ratio constraints of 2 and 1, and greater than 95% with a stop ratio of 0.5. When the stop ratio constraint was removed, five of the eight treatment plans had a 3%/2‐mm gamma passing rate greater than 95%, and the other three plans had a 3%/2‐mm gamma passing rate between 90% and 95%.ConclusionsThe irradiation duty factor was considerably improved with DDCS. Smaller stop ratio constraints led to shorter BDTs, but with the cost of larger dose deviations. Our finding suggested that a stop ratio of 1 constraint seems to yield acceptable DDCS dose deviation.

Surgical treatment of pelvic sarcoma involving the bone is the standard of care but is associated with several sequelae and reduced functional quality of life (QOL). Treatment with photon and proton radiotherapy is associated with relapse. Carbon ion radiotherapy (CIRT) may reduce both relapse rates and treatment sequelae. The PROSPER study is a tricontinental, nonrandomized, prospective, three-arm, pragmatic trial evaluating treatments of pelvic sarcoma involving the bone. Patients aged at least 15 years are eligible for inclusion. Participants must have an Eastern Cooperative Oncology Group Performance Status score of two or less, newly diagnosed disease, and histopathologic confirmation of pelvic chordoma, chondrosarcoma, osteosarcoma, Ewing sarcoma with bone involvement, rhabdomyosarcoma (RMS) with bone involvement, or non-RMS soft tissue sarcoma with bone involvement. Treatment arms include (1) CIRT (n = 30) delivered in Europe and Asia, (2) surgical treatment with or without adjuvant radiotherapy (n = 30), and (3) proton therapy (n = 30). Arms two and three will be conducted at Mayo Clinic campuses in Arizona, Florida, and Minnesota. The primary end point is to compare the 1-year change in functional QOL between CIRT and surgical treatment. Additional comparisons among the three arms will be made between treatment sequelae, local control, and other QOL measures.

AbstractBackgroundRecent advancements in Deep Learning (DL) methodologies have led to state‐of‐the‐art performance in a wide range of applications especially in object recognition, classification, and segmentation of medical images. However, training modern DL models requires a large amount of computation and long training times due to the complex nature of network structures and the large number of training datasets involved. Moreover, it is an intensive, repetitive manual process to select the optimized configuration of hyperparameters for a given DL network.PurposeIn this study, we present a novel approach to accelerate the training time of DL models via the progressive feeding of training datasets based on similarity measures for medical image segmentation. We term this approach Progressive Deep Learning (PDL).MethodsThe two‐stage PDL approach was tested on the auto‐segmentation task for two imaging modalities: CT and MRI. The training datasets were ranked according to similarity measures between each sample based on Mean Square Error (MSE), Peak Signal‐to‐Noise Ratio (PSNR), Structural Similarity Index (SSIM), and the Universal Quality Image Index (UQI) values. At the start of the training process, a relatively coarse sampling of training datasets with higher ranks was used to optimize the hyperparameters of the DL network. Following this, the samples with higher ranks were used in step 1 to yield accelerated loss minimization in early training epochs and the total dataset was added in step 2 for the remainder of training.ResultsOur results demonstrate that the PDL approach can reduce the training time by nearly half (∼49%) and can predict segmentations (CT U‐net/DenseNet dice coefficient: 0.9506/0.9508, MR U‐net/DenseNet dice coefficient: 0.9508/0.9510) without major statistical difference (Wilcoxon signed‐rank test) compared to the conventional DL approach. The total training times with a fixed cutoff at 0.95 DSC for the CT dataset using DenseNet and U‐Net architectures, respectively, were 17 h, 20 min and 4 h, 45 min in the conventional case compared to 8 h, 45 min and 2 h, 20 min with PDL. For the MRI dataset, the total training times using the same architectures were 2 h, 54 min and 52 min in the conventional case and 1 h, 14 min and 25 min with PDL.ConclusionThe proposed PDL training approach offers the ability to substantially reduce the training time for medical image segmentation while maintaining the performance achieved in the conventional case.

Abstract The Mayo Clinic Florida Integrated Oncology Building will be the home of the first spot-scanning only carbon/proton hybrid therapy system by Hitachi, Ltd. It will provide proton beams up to kinetic energies of 230 MeV and carbon beams up to 430 MeV n −1 for clinical deployment. To provide adequate radiation protection, the Geant4 (v10.6) Monte Carlo toolkit was utilized to quantify the ambient dose equivalent at a 10 mm depth ( H *(10)) for photons and neutrons. To perform accurate calculations of the ambient dose equivalent, three-dimensional computer-aided design files of the entire planned facility were imported into Geant4, as well as certain particle system components such as the bending magnets, fast Faraday cup, and gantry. Particle fluence was scored using 60 cm diameter spheres, which were strategically placed throughout areas of interests. Analytical calculations were performed as first-pass design checks. Major shielding slabs were optimized using Geant4 simulations iteratively, with more than 20 alternative designs evaluated within Geant4. The 430 MeV n −1 carbon beams played the most significant role in concrete thickness Requirements. The primary wall thickness for the carbon fixed beam room is 4 meters. The presence of the proton gantry structure in the simulation caused the ambient dose equivalent to increase by around 67% at the maze entrance, but a decrease in the high energy beam transport corridor. All shielding primary and secondary goals for clinical operations were met per state regulation and national guidelines.

The computation of the relative biological effectiveness (RBE) is a fundamental step in the planning of cancer radiotherapy treatments with accelerated ions. Numerical parameters derived analyzing the dose response of the chosen cell line after irradiation to photons (i.e., α and β, namely the linear and quadratic terms of the linear-quadratic model of cell survival) are generally used as input to biophysical models to predict the ion RBE. The α/β ratio for the photon exposure is generally regarded as an indicator of cell radiosensitivity. However, previous studies suggest that α/β might not be a sufficient parameter to model the RBE of relatively high linear energy transfer (LET) radiation such as carbon ions. For a fixed α/β, the effect of the absolute values of α and β on the computed RBE is underexplored. Furthermore, since α and β are anticorrelated during the fit of the photon-exposed in vitro survival data, different linear-quadratic fits could produce different sets of α and β, thus affecting the RBE calculations. This article reports the combined effect of the α/β ratio and the absolute values α and β on the RBE computed with the Mayo Clinic Florida microdosimetric kinetic model (MCF MKM) for 12C ions of different LET. Furthermore, we introduce a theory-based strategy to potentially mitigate the anticorrelation between α and β during the fit of the photon dose-response biological data.

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.

Abstract Objective. To investigate synchrotron-based proton pencil beam scanning (PBS) beam delivery time (BDT) using novel continuous scanning mode. Approach. A BDT calculation model was developed for the Hitachi particle therapy system. The model was validated against the measured BDT of 36 representative clinical proton PBS plans with discrete spot scanning (DSS) in the current Hitachi proton therapy system. BDTs were calculated with the next generation using Mayo Clinic Florida system operating parameters for conventional DSS, and novel dose driven continuous scanning (DDCS). BDTs of DDCS with and without Break Spots were investigated. Main results. For DDCS without Break Spots, the use of Stop Ratio to control the transit dose largely reduced the beam intensity and consequently, severely prolonged the BDT. DDCS with Break Spots was able to maintain a sufficiently high beam intensity while controlling transit dose. In DDCS with Break Spots, tradeoffs were made between beam intensity and number of Break Spots. Therefore, BDT decreased with increased beam intensity but reached a plateau for beam intensity larger than 10 MU s −1 . Averaging over all clinical plans, BDT was reduced by 10% for DDCS with Break Spots compared to DSS. Significance. DDCS with Break Spots reduced BDT. DDCS has the potential to further reduce BDT under the ideal scenario which requests both stable beam intensity extraction and accurately modelling the transit dose. Further investigation is warranted.

BNCT is a high LET radiation therapy modality that allows for biologically targeted radiation delivery to tumors while reducing normal tissue impacts. Although the clinical use of BNCT has largely been limited to phase I/II trials and has primarily focused on difficult-to-treat malignancies such as recurrent head and neck cancer and recurrent gliomas, recently there has been a renewed interest in expanding the use of BNCT to other disease sites, including breast cancer. Given its high LET characteristics, its biologically targeted and tumor specific nature, as well as its potential for use in complex treatment settings including reirradiation and widespread metastatic disease, BNCT offers several unique advantages over traditional external beam radiation therapy. The two main boron compounds investigated to date in BNCT clinical trials are BSH and BPA. Of these, BPA in particular shows promise in breast cancer given that is taken up by the LAT-1 amino acid transporter that is highly overexpressed in breast cancer cells. As the efficacy of BNCT is directly dependent on the extent of boron accumulation in tumors, extensive preclinical efforts to develop novel boron delivery agents have been undertaken in recent years. Preclinical studies have shown promise in antibody linked boron compounds targeting ER/HER2 receptors, boron encapsulating liposomes, and nanoparticle-based boron delivery systems. This review aims to summarize the physical and biological basis of BNCT, the preclinical and limited clinical data available to date, and discuss its potential to be utilized for the successful treatment of various breast cancer disease states.

Abstract Objective . To investigate similarities and differences in the formalism, processing, and the results of relative biological effectiveness (RBE) calculations with a biological weighting function (BWF), the microdosimetric kinetic model (MKM) and subsequent modifications (non-Poisson MKM, modified MKM). This includes: (a) the extension of the V79-RBE 10% BWF to model the RBE for other clonogenic survival levels; (b) a novel implementation of MKMs as weighting functions; (c) a benchmark against Chinese Hamster lung fibroblast (V79) in vitro data; (d) a study on the effect of pre- or post- processing the average biophysical quantities used for the RBE calculations; (e) a possible modification of the modified MKM parameters to improve the model accuracy at high linear energy transfer (LET). Methodology . Lineal energy spectra were simulated for two spherical targets (diameter = 0.464 or 1.0 μ m) using PHITS for 1 H, 4 He, 12 C, 20 Ne, 40 Ar, 56 Fe and 132 Xe ions. The results of the in silico calculations were compared with published in vitro data. Main results . All models appear to underestimate the RBE α of hydrogen ions. All MKMs generally overestimate the RBE 50% , RBE 10% and RBE 1% for ions with an LET greater than ∼200 keV μ m −1 . This overestimation is greater for small surviving fractions and is likely due to the assumption of a radiation-independent quadratic term of clonogenic survival ( ß ). The overall RBE trends seem to be best described by the novel ‘post-processing average’ implementation of the non-Poisson MKM. In case of calculations with the non-Poisson MKM, pre- or post- processing the average biophysical quantities affects the computed RBE values significantly. Significance . This study presents a systematic analysis of the formalism and results of widely used microdosimetric models of clonogenic survival for ions relevant for cancer particle therapy and space radiation protection. Points for improvements were highlighted and will contribute to the development of upgraded biophysical models.