The influence of the local effect model parameters on the prediction of the tumor control probability for prostate cancer

Rapid effective dose calculation for raster-scanning 4He ion therapy with the modified microdosimetric kinetic model (mMKM)

PURPOSE

To develop and verify effective dose (D_RBE) calculation in ⁴He ion beam therapy based on the modified microdosimetric kinetic model (mMKM) and evaluate the bio-sensitivity of mMKM-based plans to clinical parameters using a fast analytical dose engine.

METHODS

Mixed radiation field particle spectra (MRFS) databases have been generated with Monte-Carlo (MC) simulations for ⁴He-ion beams. Relative biological effectiveness (RBE) and D_RBE calculation using MRFS were established within a fast analytical engine. Spread-out Bragg-Peaks (SOBPs) in water were optimized for two dose levels and two tissue types with photon linear-quadratic model parameters α_ph, β_ph, and (α/β)_ph to verify MRFS-derived database implementation against computations with MC-generated mixed-field α and β databases. Bio-sensitivity of the SOBPs was investigated by varying absolute values of β_ph, while keeping (α/β)_ph constant. Additionally, dose, dose-averaged linear energy transfer, and bio-sensitivity were investigated for two patient cases.

RESULTS

Using MRFS-derived databases, dose differences ≲2% in the plateau and SOBP are observed compared to computations with MC-generated databases. Bio-sensitivity studies show larger deviations when altering the absolute β_ph value, with maximum D_50% changes of ~5%, with similar results for patient cases. Bio-sensitivity analysis indicates a greater impact on D_RBE varying (α/β)_ph than β_ph in mMKM.

CONCLUSIONS

The MRSF approach yielded negligible differences in the target and small differences in the plateau compared to MC-generated databases. The presented analyses provide guidance for proper implementation of RBE-weighted ⁴He ion dose prescription and planning with mMKM. The MRFS-D_RBE calculation approach using mMKM will be implemented in a clinical treatment planning system.

Combination possibility and deep learning model as clinical decision-aided approach for prostate cancer

This study aims to introduce as proof of concept a combination model for classification of prostate cancer using deep learning approaches. We utilized patients with prostate cancer who underwent surgical treatment representing the various conditions of disease progression. All possible combinations of significant variables from logistic regression and correlation analyses were determined from study data sets. The combination possibility and deep learning model was developed to predict these combinations that represented clinically meaningful patient's subgroups. The observed relative frequencies of different tumor stages and Gleason score Gls changes from biopsy to prostatectomy were available for each group. Deep learning models and seven machine learning approaches were compared for the classification performance of Gleason score changes and pT2 stage. Deep models achieved the highest F1 scores by pT2 tumors (0.849) and Gls change (0.574). Combination possibility and deep learning model is a useful decision-aided tool for prostate cancer and to group patients with prostate cancer into clinically meaningful groups.

Sensitivity study of the microdosimetric kinetic model parameters for carbon ion radiotherapy

In carbon ion therapy treatment planning, the relative biological effectiveness (RBE) is accounted for by optimization of the RBE-weighted dose (biological dose). The RBE calculation methods currently applied clinically in carbon ion therapy are derived from the microdosimetric kinetic model (MKM) in Japan and the local effect model (LEM) in Europe. The input parameters of these models are based on fit to experimental data subjected to uncertainties. We therefore performed a sensitivity study of the MKM input parameters, i.e. the domain radius (r _d ), the nucleus radius (R _n ) and the parameters of the linear quadratic (LQ) model (α _x and β). The study was performed with the FLUKA Monte Carlo code, using spread out Bragg peak (SOBP) scenarios in water and a biological dose distribution in a clinical patient case. Comparisons were done between biological doses estimated applying the MKM with parameters based on HSG cells, and with HSG parameters varied separately by  ±{5, 25, 50}%. Comparisons were also done between parameter sets from different cell lines (HSG, V79, CHO and T1), as well as versions of the LEM. Of the parameters, r _d had the largest impact on the biological dose distribution, especially on the absolute dose values. Increasing this parameter by 25% decreased the biological dose level at the center of a 3 Gy(RBE) SOBP by 14%. Variations in R _n only influenced the biological dose distribution towards the particle range, and variations in α _x resulted in minor changes in the biological dose, with an increasing impact towards the particle range. β had the overall smallest influence on the SOBPs, but the impact could become more pronounced if alternative (LET dependent) implementations are used. The resulting percentage change in the SOBPs was generally less than the percentage change in the parameters. The patient case showed similar effects as with the SOBPs in water, and parameter variations had similar impact on the biological dose when using the clinical MKM and the general MKM. The clinical LEM calculated the highest biological doses to both tumor and surrounding healthy tissues, with a median target dose (D _50%) of 40.5 Gy(RBE), while the MKM with HSG and V79 parameters resulted in a D _50% of 34.2 and 36.9 Gy(RBE), respectively. In all, the observed change in biological dose distribution due to parameter variations demonstrates the importance of accurate input parameters when applying the MKM in treatment planning.

NanOx, a new model to predict cell survival in the context of particle therapy

Particle therapy is increasingly attractive for the treatment of tumors and the number of facilities offering it is rising worldwide. Due to the well-known enhanced effectiveness of ions, it is of utmost importance to plan treatments with great care to ensure tumor killing and healthy tissues sparing. Hence, the accurate quantification of the relative biological effectiveness (RBE) of ions, used in the calculation of the biological dose, is critical. Nevertheless, the RBE is a complex function of many parameters and its determination requires modeling. The approaches currently used have allowed particle therapy to thrive, but still show some shortcomings. We present herein a short description of a new theoretical framework, NanOx, to calculate cell survival in the context of particle therapy. It gathers principles from existing approaches, while addressing some of their weaknesses. NanOx is a multiscale model that takes the stochastic nature of radiation at nanometric and micrometric scales fully into account, integrating also the chemical aspects of radiation-matter interaction. The latter are included in the model by means of a chemical specific energy, determined from the production of reactive chemical species induced by irradiation. Such a production represents the accumulation of oxidative stress and sublethal damage in the cell, potentially generating non-local lethal events in NanOx. The complementary local lethal events occur in a very localized region and can, alone, lead to cell death. Both these classes of events contribute to cell death. The comparison between experimental data and model predictions for the V79 cell line show a good agreement. In particular, the dependence of the typical shoulders of cell survival curves on linear energy transfer are well described, but also the effectiveness of different ions, including the overkill effect. These results required the adjustment of a number of parameters compatible with the application of the model in a clinical scenario thereby showing the potential of NanOx. Said parameters are discussed in detail in this paper.

Mathematical modeling of tumor response to radiation: radio-sensitivity correlation with BOLD, TOLD, ΔR1 and ΔR2* investigated in large Dunning R3327-AT1 rat prostate tumors

Tumor response to radiation therapy can vary highly across patients. Several factors, both tumor- and environment-specific, can influence the radio-sensitivity, one of the most well-known being hypoxia. In this work, we investigated possible correlations between the radio-sensitivity parameters determined by means of a simple mathematical model of tumor volume evolution, and the MRI-based indicators of oxygenation in Dunning R3327-AT1 rats. Prior to irradiation the rats were subjected to an oxygen-breathing challenge, which was evaluated by MRI. The tumors were administered a single irradiation dose (30 Gy), while breathing air or oxygen. Despite a poor fitting performance, the model was able to identify two different tumor volume regression patterns. Moreover, the radio-sensitivity of the oxygen-breathing group was found to correlate with the variation of the transverse relaxation rate ΔR2* (-0.89). This suggests that MRI-based indices of tumor oxygenation may provide information about radio-sensitivity.

Dosimetric robustness against setup errors in charged particle radiotherapy of skull base tumors

Background

It is expected that physical dose deposition properties render charged particle dose distributions sensitive to targeting uncertainties. Purpose of this work was to investigate the robustness of scanned-beam particle therapy plans against setup errors for different optimization modalities, beam setups and ion species.

Material and methods

For 15 patients with skull base tumors, localized in regions of severe tissue density heterogeneity, scanned lateral-opposed-beam treatment plans were prepared with the treatment planning system TRiP98, employing different optimization settings (single- and multiple-field modulation) and ion species (carbon ions and protons). For 10 of the patients, additional plans were prepared with individually selected beam setups, aiming at avoiding severe tissue heterogeneities. Subsequently, multiple rigid positioning errors of magnitude 1–2 mm (i.e. within planning target expansion) were simulated by introducing a shift of the irradiation fields with respect to the computed tomography (CT) data and recomputing the plans.

Results

In presence of shifts, in carbon ion plans using a lateral-opposed beam setup and fulfilling clinical healthy tissue dose constraints, the median reduction in CTV V_95% was up to 0.7 percentage points (pp) and 3.5 pp, for shifts of magnitude 1 mm and 2 mm respectively, however, in individual cases, the reduction reached 5.1 pp and 9.7 pp. In the corresponding proton plans similar median CTV V_95% reductions of up to 0.9 pp (1 mm error) and 3.4 pp (2 mm error) were observed, with respective individual-case reductions of at most 3.2 pp and 11.7 pp. Unconstrained plans offered slightly higher coverage values, while no relevant differences were observed between different field modulation methods. Individually selected beam setups had a visible dosimetric advantage over lateral-opposed beams, for both particle species. While carbons provided more conformal plans and generally more advantageous absolute dose values, in presence of setup errors, protons showed greater dosimetric stability, in most of the investigated scenarios.

Conclusion

Residual patient setup errors may lead to substantial dose perturbation in scanned-beam particle therapy of skull base tumors, which cannot be dealt with by planning target expansion alone. Choice of irradiation directions avoiding extreme density heterogeneities can improve plan stability against such delivery-time uncertainties.