The impact of variable relative biological effectiveness in proton therapy for left-sided breast cancer when estimating normal tissue complications in the heart and lung

An updated variable RBE model for proton therapy

Objective.While a constant relative biological effectiveness (RBE) of 1.1 forms the basis for clinical proton therapy, variable RBE models are increasingly being used in plan evaluation. However, there is substantial variation across RBE models, and several newin vitrodatasets have not yet been included in the existing models. In this study, an updatedin vitroproton RBE database was collected and used to examine current RBE model assumptions, and to propose an up-to-date RBE model as a tool for evaluating RBE effects in clinical settings.Approach.A proton database (471 data points) was collected from the literature, almost twice the size of the previously largest model database. Each data point included linear-quadratic model parameters and linear energy transfer (LET). Statistical analyses were performed to test the validity of commonly applied assumptions of phenomenological RBE models, and new model functions were proposed forRBEmaxandRBEmin(RBE at the lower and upper dose limits). Previously published models were refitted to the database and compared to the new model in terms of model performance and RBE estimates.Main results.The statistical analysis indicated that the intercept of theRBEmaxfunction should be a free fitting parameter and RBE estimates were clearly higher for models with free intercept.RBEminincreased with increasing LET, while a dependency ofRBEminon the reference radiation fractionation sensitivity (α/βx) did not significantly improve model performance. Evaluating the models, the new model gave overall lowest RMSE and highest R2 score. RBE estimates in the distal part of a spread-out-Bragg-peak in water (α/βx= 2.1 Gy) were 1.24-1.51 for original models, 1.25-1.49 for refits and 1.42 for the new model.Significance.An updated RBE model based on the currently largest database among published phenomenological models was proposed. Overall, the new model showed better performance compared to refitted published RBE models.

Exploring Helium Ions' Potential for Post-Mastectomy Left-Sided Breast Cancer Radiotherapy

Proton therapy presents a promising modality for treating left-sided breast cancer due to its unique dose distribution. Helium ions provide increased conformality thanks to a reduced lateral scattering. Consequently, the potential clinical benefit of both techniques was explored. An explorative treatment planning study involving ten patients, previously treated with VMAT (Volumetric Modulated Arc Therapy) for 50 Gy in 25 fractions for locally advanced, node-positive breast cancer, was carried out using proton pencil beam therapy with a fixed relative biological effectiveness (RBE) of 1.1 and helium therapy with a variable RBE described by the mMKM (modified microdosimetric kinetic model). Results indicated that target coverage was improved with particle therapy for both the clinical target volume and especially the internal mammary lymph nodes compared to VMAT. Median dose value analysis revealed that proton and helium plans provided lower dose on the left anterior descending artery (LAD), heart, lungs and right breast than VMAT. Notably, helium therapy exhibited improved ipsilateral lung sparing over protons. Employing NTCP models as available in the literature, helium therapy showed a lower probability of grade ≤ 2 radiation pneumonitis (22% for photons, 5% for protons and 2% for helium ions), while both proton and helium ions reduce the probability of major coronary events with respect to VMAT.

Does variable RBE affect toxicity risks for mediastinal lymphoma patients? NTCP-based evaluation after proton therapy treatment

INTRODUCTION

Mediastinal lymphoma (ML) is a solid malignancy affecting young patients. Modern combined treatments allow obtaining good survival probability, together with a long life expectancy, and therefore with the need to minimize treatment-related toxicities. We quantified the expected toxicity risk for different organs and endpoints in ML patients treated with intensity-modulated proton therapy (IMPT) at our centre, accounting also for uncertainties related to variable RBE.

METHODS

Treatment plans for ten ML patients were recalculated with a TOPAS-based Monte Carlo code, thus retrieving information on LET and allowing the estimation of variable RBE. Published NTCP models were adopted to calculate the toxicity risk for hypothyroidism, heart valve defects, coronary heart disease and lung fibrosis. NTCP was calculated assuming both constant (i.e. 1.1) and variable RBE. The uncertainty associated with individual radiosensitivity was estimated by random sampling α/β values before RBE evaluation.

RESULTS

Variable RBE had a minor impact on hypothyroidism risk for 7 patients, while it led to significant increase for the remaining three (+24% risk maximum increase). Lung fibrosis was slightly affected by variable RBE, with a maximum increase of ≅ 1%. This was similar for heart valve dysfunction, with the exception of one patient showing an about 10% risk increase, which could be explained by means of large heart volume and D₁ increase.

DISCUSSION

The use of NTCP models allows for identifying those patients associated with a higher toxicity risk. For those patients, it might be worth including variable RBE in plan evaluation.

Functionalized Hybrid Iron Oxide-Gold Nanoparticles Targeting Membrane Hsp70 Radiosensitize Triple-Negative Breast Cancer Cells by ROS-Mediated Apoptosis

Triple-negative breast cancer (TNBC) a highly aggressive tumor entity with an unfavorable prognosis, is treated by multimodal therapies, including ionizing radiation (IR). Radiation-resistant tumor cells, as well as induced normal tissue toxicity, contribute to the poor clinical outcome of the disease. In this study, we investigated the potential of novel hybrid iron oxide (Fe₃O₄)-gold (Au) nanoparticles (FeAuNPs) functionalized with the heat shock protein 70 (Hsp70) tumor-penetrating peptide (TPP) and coupled via a PEG4 linker (TPP-PEG4-FeAuNPs) to improve tumor targeting and uptake of NPs and to break radioresistance in TNBC cell lines 4T1 and MDA-MB-231. Hsp70 is overexpressed in the cytosol and abundantly presented on the cell membrane (mHsp70) of highly aggressive tumor cells, including TNBCs, but not on corresponding normal cells, thus providing a tumor-specific target. The Fe₃O₄ core of the NPs can serve as a contrast agent enabling magnetic resonance imaging (MRI) of the tumor, and the nanogold shell radiosensitizes tumor cells by the release of secondary electrons (Auger electrons) upon X-ray irradiation. We demonstrated that the accumulation of TPP-PEG4-FeAuNPs into mHsp70-positive TNBC cells was superior to that of non-conjugated FeAuNPs and FeAuNPs functionalized with a non-specific, scrambled peptide (NGL). After a 24 h co-incubation period of 4T1 and MDA-MB-231 cells with TPP-PEG4-FeAuNPs, but not with control hybrid NPs, ionizing irradiation (IR) causes a cell cycle arrest at G2/M and induces DNA double-strand breaks, thus triggering apoptotic cell death. Since the radiosensitizing effect was completely abolished in the presence of the ROS inhibitor N-acetyl-L-cysteine (NAC), we assume that the TPP-PEG4-FeAuNP-induced apoptosis is mediated via an increased production of ROS.

How can we consider variable RBE and LETd prediction during clinical practice? A pediatric case report at the Normandy Proton Therapy Centre using an independent dose engine

BACKGROUND

To develop an auxiliary GPU-accelerated proton therapy (PT) dose and LETd engine for the IBA Proteus®ONE PT system. A pediatric low-grade glioma case study is reported using FRoG during clinical practice, highlighting potential treatment planning insights using variable RBE dose (DvRBE) and LETd as indicators for clinical decision making in PT.

METHODS

The physics engine for FRoG has been modified for compatibility with Proteus®ONE PT centers. Subsequently, FRoG was installed and commissioned at NPTC. Dosimetric validation was performed against measurements and the clinical TPS, RayStation (RS-MC). A head patient cohort previously treated at NPTC was collected and FRoG forward calculations were compared against RS-MC for evaluation of 3D-Γ analysis and dose volume histogram (DVH) results. Currently, treatment design at NPTC is supported with fast variable RBE and LETd calculation and is reported in a representative case for pediatric low-grade glioma.

RESULTS

Simple dosimetric tests against measurements of iso-energy layers and spread-out Bragg Peaks in water verified accuracy of FRoG and RS-MC. Among the patient cohort, average 3D-Γ applying 2%/2 mm, 3%/1.5 mm and 5%/1 mm were > 97%. DVH metrics for targets and OARs between FRoG and RayStation were in good agreement, with ∆D50,CTV and ∆D2,OAR both ⪅1%. The pediatric case report demonstrated implications of different beam arrangements on DvRBE and LETd distributions. From initial planning in RayStation sharing identical optimization constraints, FRoG analysis led to plan selection of the most conservative approach, i.e., minimized DvRBE,max and LETd,max in OARs, to avoid optical system toxicity effects (i.e., vision loss).

CONCLUSION

An auxiliary dose calculation system was successfully integrated into the clinical workflow at a Proteus®ONE IBA facility, in excellent agreement with measurements and RS-MC. FRoG may lead to further insight on DvRBE and LETd implications to help clinical decision making, better understand unexpected toxicities and establish novel clinical procedures with metrics currently absent from the standard clinical TPS.

A review on AI in PET imaging

Artificial intelligence (AI) has been applied to various medical imaging tasks, such as computer-aided diagnosis. Specifically, deep learning techniques such as convolutional neural network (CNN) and generative adversarial network (GAN) have been extensively used for medical image generation. Image generation with deep learning has been investigated in studies using positron emission tomography (PET). This article reviews studies that applied deep learning techniques for image generation on PET. We categorized the studies for PET image generation with deep learning into three themes as follows: (1) recovering full PET data from noisy data by denoising with deep learning, (2) PET image reconstruction and attenuation correction with deep learning and (3) PET image translation and synthesis with deep learning. We introduce recent studies based on these three categories. Finally, we mention the limitations of applying deep learning techniques to PET image generation and future prospects for PET image generation.

Impact of a spatially dependent dose delivery time structure on the biological effectiveness of scanning proton therapy

PURPOSE

In the scanning beam delivery of protons, different portions of the target are irradiated with different linear energy transfer protons with various time intervals and irradiation times. This research aimed to evaluate the spatially dependent biological effectiveness of protracted irradiation in scanning proton therapy.

METHODS

One and two parallel opposed fields plans were created in water phantom with the prescribed dose of 2 Gy. Three scenarios (instantaneous, continuous, and layered scans) were used with the corresponding beam delivery models. The biological dose (physical dose × relative biological effectiveness) was calculated using the linear quadratic model and the theory of dual radiation action to quantitatively evaluate the dose delivery time effect. In addition, simulations using clinical plans (postoperative seminoma and prostate tumor cases) were conducted to assess the impact of the effects on the dose volume histogram parameters and homogeneity coefficient (HC) in targets.

RESULTS

In a single-field plan of water phantom, when the treatment time was 19 min, the layered-scan scenario showed a decrease of <0.2% (almost 3.3%) in the biological dose from the plan on the distal (proximal) side because of the high (low) dose rate. This is in contrast to the continuous scenario, where the biological dose was almost uniformly decreased over the target by approximately 3.3%. The simulation with clinical geometry showed that the decrease rates in D_99% were 0.9% and 1.5% for every 10 min of treatment time prolongation for postoperative seminoma and prostate tumor cases, respectively, whereas the increase rates in HC were 0.7% and 0.2%.

CONCLUSIONS

In protracted irradiation in scanning proton therapy, the spatially dependent dose delivery time structure in scanning beam delivery can be an important factor for accurate evaluation of biological effectiveness.

Development and Implementation of Proton Therapy for Hodgkin Lymphoma: Challenges and Perspectives

Simple Summary

Hodgkin lymphoma (HL) is a highly curable disease; proton therapy for mediastinal HL irradiation might theoretically reduce late toxicities compared with classical radiotherapy techniques. However, optimal patient selection for this technique is subject to debate. While implementation at a larger scale of proton therapy for HL may face organizational, political, and societal challenges, new highly effective systematic drugs are being widely evaluated for this disease.

Abstract

Consolidative radiation therapy for early-stage Hodgkin lymphoma (HL) improves progression-free survival. Unfortunately, first-generation techniques, relying on large irradiation fields, were associated with an increased risk of secondary cancers, and of cardiac and lung toxicity. Fortunately, the use of smaller target volumes combined with technological advances in treatment techniques currently allows efficient organs-at-risk sparing without altering tumoral control. Recently, proton therapy has been evaluated for mediastinal HL treatment due to its potential to significantly reduce the dose to organs-at-risk, such as cardiac substructures. This is expected to limit late radiation-induced toxicity and possibly, second-neoplasm risk, compared with last-generation intensity-modulated radiation therapy. However, the democratization of this new technique faces multiple issues. Determination of which patient may benefit the most from proton therapy is subject to intense debate. The development of new effective systemic chemotherapy and organizational, societal, and political considerations might represent impediments to the larger-scale implementation of HL proton therapy. Based on the current literature, this critical review aims to discuss current challenges and controversies that may impede the larger-scale implementation of mediastinal HL proton therapy.

Prediction of an oxygen extraction fraction map by convolutional neural network: validation of input data among MR and PET images

Purpose

Oxygen extraction fraction (OEF) is a biomarker for the viability of brain tissue in ischemic stroke. However, acquisition of the OEF map using positron emission tomography (PET) with oxygen-15 gas is uncomfortable for patients because of the long fixation time, invasive arterial sampling, and radiation exposure. We aimed to predict the OEF map from magnetic resonance (MR) and PET images using a deep convolutional neural network (CNN) and to demonstrate which PET and MR images are optimal as inputs for the prediction of OEF maps.

Methods

Cerebral blood flow at rest (CBF) and during stress (sCBF), cerebral blood volume (CBV) maps acquired from oxygen-15 PET, and routine MR images (T1-, T2-, and T2*-weighted images) for 113 patients with steno-occlusive disease were learned with U-Net. MR and PET images acquired from the other 25 patients were used as test data. We compared the predicted OEF maps and intraclass correlation (ICC) with the real OEF values among combinations of MRI, CBF, CBV, and sCBF.

Results

Among the combinations of input images, OEF maps predicted by the model learned with MRI, CBF, CBV, and sCBF maps were the most similar to the real OEF maps (ICC: 0.597 ± 0.082). However, the contrast of predicted OEF maps was lower than that of real OEF maps.

Conclusion

These results suggest that the deep CNN learned useful features from CBF, sCBF, CBV, and MR images and predict qualitatively realistic OEF maps. These findings suggest that the deep CNN model can shorten the fixation time for ¹⁵O PET by skipping ¹⁵O₂ scans. Further training with a larger data set is required to predict accurate OEF maps quantitatively.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11548-021-02356-7.