Biological Effective Dose Distributions Research Articles

Predicting Local Failure after Partial Prostate Re-Irradiation Using a Dosiomic-Based Machine Learning Model.

The aim of this study is to predict local failure after partial prostate re-irradiation for the treatment of isolated locally recurrent prostate cancer by using a machine learning classifier based on radiomic features from pre-treatment computed tomography (CT), positron-emission tomography (PET) and biological effective dose distribution (BED) of the radiotherapy plan. The analysis was conducted on a monocentric dataset of 43 patients with evidence of isolated intraprostatic recurrence of prostate cancer after primary external beam radiotherapy. All patients received partial prostate re-irradiation delivered by volumetric modulated arc therapy. The gross tumor volume (GTV) of each patient was manually contoured from planning CT, choline-PET and dose maps. An ensemble machine learning pipeline including unbalanced data correction and feature selection was trained using the radiomic and dosiomic features as input for predicting occurrence of local failure. The model performance was assessed using sensitivity, specificity, accuracy and area under receiver operating characteristic curves of the score function in 10-fold cross validation repeated 100 times. Local failure was observed in 13 patients (30%), with a median time to recurrence of 36.7 months (range = 6.1–102.4 months). A four variables ensemble machine learning model resulted in accuracy of 0.62 and AUC 0.65. According to our results, a dosiomic machine learning classifier can predict local failure after partial prostate re-irradiation.

Combined Yttrium-90 microsphere selective internal radiation therapy and external beam radiotherapy in patients with hepatocellular carcinoma: From clinical aspects to dosimetry.

PurposeSelective internal radiation therapy (SIRT) is an effective treatment strategy for unresectable hepatocellular carcinoma (HCC) patients. However, the prognoses of patients with portal vein thrombosis, extra-hepatic metastases, or residual tumors remain poor when treated with SIRT alone. In these patients, sequential external beam radiotherapy (EBRT) may offer a chance of salvage. Here, we reported the clinical outcomes and the detailed dosimetry analysis of 22 patients treated with combination therapy.MethodsBetween October 2011 and May 2015, 22 consecutive patients who underwent EBRT after yttrium-90 (90Y) SIRT were included in this study. The post-SIRT 90Y bremsstrahlung SPECT/CT of each patient was transferred to dose distribution by adopting the local deposition hypothesis. The patient-specific 3-dimensional biological effective dose distribution of combined SIRT and EBRT was generated. The overall survival and safety were evaluated. The relationship between dosimetric parameters and liver toxicity was analyzed.ResultsThe mean administered activity of SIRT was 1.50 GBq (range: 0.5–2.8). The mean prescribed dose of EBRT was 42.3 Gy (range: 15–63) in 14 fractions (range: 5–15) and was targeted to the residual liver tumor in 12 patients (55%), portal vein thrombosis in 11 patients (50%), and perihilar lymphadenopathies in 4 patients (18%). The overall 1-, 2-, and 3-year survival rates were 59.8%, 47.9%, and 47.9%, respectively. Overall, 8 patients (36%) developed > grade 2 liver toxicities, and the Child-Pugh score prior to EBRT strongly affected the toxicity risk. A dosimetry analysis restricted to 18 Child-Pugh A/B patients showed that the V100 (The fraction of normal liver exposed to more than 100 Gy) to V140 significance differed between patients who did or did not experience hepatotoxicity. The V110 was the strongest predictor of hepatotoxicity (18.6±11.6% vs 29.5±5.8%; P = 0.030).ConclusionCombined therapy is feasible and safe if patients are carefully selected. Specifically, 3-dimensional dosimetry is crucial for the evaluation of efficacy and toxicity. The normal liver V100 to V140 values of the combined dose should be as low as possible to minimize the risk of liver toxicity.

Development of 3D biological effective dose distribution software program

AbstractPurposeTo develop a software program to convert physical dose distribution into biological effective dose (BED).MethodsThe MATLAB-based BED distribution software program was designed to import the radiotherapy treatment plan from the computer treatment planning system and to convert the physical dose distribution into the BED distribution. The BED calculation was based on the linear-quadratic-linear model (LQ-L model). Besides radiobiological parameters, other specific data could be fed in through the panel. The accuracy of the program was verified by comparing the BED distribution with manual calculation.ResultsThis software program was able to import the radiotherapy treatment plans and pull out pixel-wised physical dose for BED calculation, and display the isoBED lines on the computed tomographic (CT) image. The verification of BED dose distribution was performed in both phantom and clinical cases. It revealed that there were no differences between the program and manual BED calculations.ConclusionIt is feasible and practical to use this in-house BED distribution software program in clinical practices and research work. However, it should be used with caution as the validity of the program depends on the accuracy of the published biological parameters.

TH‐C‐213AB‐05: Correlating Areas of Radiation Toxicity with Relative Biological Effectiveness‐Weighted Dose Distributions in Proton Radiotherapy

Purpose: To determine if biologically effective proton dose calculated using a relative biological effectiveness (RBE) model with multiple parameters may be used to explain areas of radiation induced brain necrosis indicated on follow‐up imaging studies for a patient treated with postoperative intensity modulated proton radiotherapy for malignant meningioma. Methods: In this study, treatment plan information is extracted from a clinical treatment planning system (TPS) and used to recalculate physical proton dose and linear energy transfer (LET) distributions using Monte Carlo simulations. An RBE model which accounts for tissue specific parameters, LET, biological endpoint, and dose per treatment fraction is applied on a voxel‐by‐voxel basis to compute a biological effective dose distribution. This dose distribution is subsequently compared to follow‐up imaging studies which have indicated the presence of radiation induced necrosis in the treated region of the patient being analyzed. Corresponding areas of toxicity and dose/RBE are compared to determine the governing relationship. Results: Areas of increased biological effective dose compared to the original dose distribution calculated by the clinical TPS are found to correspond to areas of radiation induced necrosis identified on follow‐up imaging. Biologically effective dose is found to be increased over TPS dose by as much as 8% of the prescribed dose for the patient. Conclusions: Dose distributions which account for the variable nature of proton RBE may provide insight into toxicity risk for patients treated with proton radiotherapy.

SU‐E‐T‐654: Calculation of Cumulative Biological Effective Dose Distributions for Multimodality Treatment Regimes

Purpose: Evidence has recently been reported supporting the role of stereotactic body radiotherapy (SBRT) as a supplemental treatment option in the form of a boost or salvage reirradiation following conventional radiotherapy. It may therefore be desirable to augment the process of physical dose summation in the cumulative plan evaluation process by constructing corresponding biological effective dose (BED) distributions that incorporate the biological effects of the different dose fractionation schemes required for multimodality treatments. To that end, we developed a computational process for calculating cumulative BED distributions for patients receiving conventional radiotherapy followed by SBRT. Methods: We developed a program designed to accept two DICOM dose matrices, as calculated in standard treatment planning systems, and calculate the BED at each matrix element obeying the dose per fraction dependence in the BED formalism. It then calculates the cumulative BED distribution for multimodality treatment regimes by matrix summation and outputs the result in DICOM format. Two clinical head and neck IMRT cases (large field (2.13Gy × 33) and small field (2Gy × 32)) were evaluated as part of a pilot study to demonstrate the overall feasibility of the calculation scheme. Following clinical IMRT planning, each case was planned in a mock SBRT scenario (6Gy × 5) where a smaller planning volume was drawn to simulate a boost or salvage reirradiation. The clinical IMRT and mock SBRT dose matrices were then processed to generate cumulative BED distributions. Results: Our computational process was successful in producing cumulative BED distributions. For the large field IMRT case, the overall BED distribution beyond the initial treated volume was dominated by the initial IMRT course, whereas clear additive increase in the BED distribution beyond the initial treated volume was evident in the small field case. Conclusions: Cumulative BED distributions may reveal useful clinical information during plan evaluation.

The relevance of very low energy ions for heavy-ion therapy

Heavy-ion radiotherapy exploits the high biological effectiveness of localized energy deposition delivered by so-called Bragg-peak particles. Recent publications have challenged the established procedures to calculate biological effective dose distributions in treatment planning. They emphasize the importance of very low energy (<500 keV amu−1) ions, either as primary particles or originating from molecular and nuclear fragmentations. We show, however, that slow heavy ions with energies below 500 keV amu−1 only play a negligible role in cancer treatments for several reasons. Their residual range is very small compared to the relevant length scale of treatment planning. Moreover, their relative frequency and also their relative dose distribution are insignificant, since energy loss and range straggling in ion slowing down processes as well as the necessary superposition of Bragg peaks wash out small-scale special effects. Additionally, we show that even a 1000 times larger biological damage of such slow ions would not result in a clinically relevant increase of the photon-equivalent dose. Therefore, neither a more precise physical description of ions in the very distal part of the Bragg peak nor the consideration of radiation damage induced by hyperthermal ions would result in a meaningful improvement of current models for heavy-ion treatment planning.

Composite Plans and Biological Effective Dose Distributions over Multi-modality/Multi-plan Radiotherapy for Plan Evaluation and Toxicity Risk Analysis

Composite Plans and Biological Effective Dose Distributions over Multi-modality/Multi-plan Radiotherapy for Plan Evaluation and Toxicity Risk Analysis

Treatment planning for light ions: how to take into account relative biological effectiveness (RBE).

A new treatment planning program was developed for the heavy ion therapy facility at GSI, which is tailored to the special needs for an active beam delivery using a magnetic raster scanner. It also includes a biological model for the estimation of biological effective dose for carbon ions and realizes a fully biological treatment planning. Biological effective dose distributions and RBE maps can be displayed and assessed from the graphical user interface.