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1. Incorporating variable RBE in IMPT optimization for ependymoma

   https://doi.org/10.1002/acm2.14207  

   Goudarzi, Hadis Moazami; Lim, Gino; Grosshans, David; Mohan, Radhe; Cao, Wenhua (January 2024, Journal of Applied Clinical Medical Physics)

   Abstract PurposeTo study the dosimetric impact of incorporating variable relative biological effectiveness (RBE) of protons in optimizing intensity‐modulated proton therapy (IMPT) treatment plans and to compare it with conventional constant RBE optimization and linear energy transfer (LET)‐based optimization. MethodsThis study included 10 pediatric ependymoma patients with challenging anatomical features for treatment planning. Four plans were generated for each patient according to different optimization strategies: (1) constant RBE optimization (ConstRBEopt) considering standard‐of‐care dose requirements; (2) LET optimization (LETopt) using a composite cost function simultaneously optimizing dose‐averaged LET (LET_d) and dose; (3) variable RBE optimization (VarRBEopt) using a recent phenomenological RBE model developed by McNamara et al.; and (4) hybrid RBE optimization (hRBEopt) assuming constant RBE for the target and variable RBE for organs at risk. By normalizing each plan to obtain the same target coverage in either constant or variable RBE, we compared dose, LET_d, LET‐weighted dose, and equivalent uniform dose between the different optimization approaches. ResultsWe found that the LETopt plans consistently achieved increased LET in tumor targets and similar or decreased LET in critical organs compared to other plans. On average, the VarRBEopt plans achieved lower mean and maximum doses with both constant and variable RBE in the brainstem and spinal cord for all 10 patients. To compensate for the underdosing of targets with 1.1 RBE for the VarRBEopt plans, the hRBEopt plans achieved higher physical dose in targets and reduced mean and especially maximum variable RBE doses compared to the ConstRBEopt and LETopt plans. ConclusionWe demonstrated the feasibility of directly incorporating variable RBE models in IMPT optimization. A hybrid RBE optimization strategy showed potential for clinical implementation by maintaining all current dose limits and reducing the incidence of high RBE in critical normal tissues in ependymoma patients. 

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2. A novel stochastic optimization method for handling misalignments of proton and photon doses in combined treatments

   https://doi.org/10.1088/1361-6560/ac858f  

   Fabiano, Silvia; Torelli, Nathan; Papp, Dávid; Unkelbach, Jan (September 2022, Physics in Medicine & Biology)

   Abstract Objective.Combined proton–photon treatments, where most fractions are delivered with photons and only a few are delivered with protons, may represent a practical approach to optimally use limited proton resources. It has been shown that, when organs at risk (OARs) are located within or near the tumor, the optimal multi-modality treatment uses protons to hypofractionate parts of the target volume and photons to achieve near-uniform fractionation in dose-limiting healthy tissues, thus exploiting the fractionation effect. These plans may be sensitive to range and setup errors, especially misalignments between proton and photon doses. Thus, we developed a novel stochastic optimization method to directly incorporate these uncertainties into the biologically effective dose (BED)-based simultaneous optimization of proton and photon plans.Approach.The method considers the expected value $E\left(b\right)$and standard deviation $\sigma \left(b\right)$of the cumulative BED $b$in every voxel of a structure. For the target, a piecewise quadratic penalty function of the form ${\left(b^{\min }-\left(E\left(b\right)-2\sigma \left(b\right)\right)\right)}_{+}^2$is minimized, aiming for plans in which the expected BED minus two times the standard deviation exceeds the prescribed BED $b^{\min }.$Analogously, ${\left(\left(E\left(b\right)+2\sigma \left(b\right)\right)-b^{\max }\right)}_{+}^2$is considered for OARs.Main results.Using a spinal metastasis case and a liver cancer patient, it is demonstrated that the novel stochastic optimization method yields robust combined treatment plans. Tumor coverage and a good sparing of the main OARs are maintained despite range and setup errors, and especially misalignments between proton and photon doses. This is achieved without explicitly considering all combinations of proton and photon error scenarios.Significance.Concerns about range and setup errors for safe clinical implementation of optimized proton–photon radiotherapy can be addressed through an appropriate stochastic planning method. 

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3. Multi-organ spatial stratification of 3-D dose distributions improves risk prediction of long-term self-reported severe symptoms in oropharyngeal cancer patients receiving radiotherapy: development of a pre-treatment decision support tool

   https://doi.org/10.3389/fonc.2023.1210087  

   Wentzel, Andrew; Mohamed, Abdallah_S R; Naser, Mohamed A; van_Dijk, Lisanne V; Hutcheson, Katherine; Moreno, Amy M; Fuller, Clifton D; Canahuate, Guadalupe; Marai, G Elisabeta (August 2023, Frontiers in Oncology)

   PURPOSE: Identify Oropharyngeal cancer (OPC) patients at high-risk of developing long-term severe radiation-associated symptoms using dose volume histograms for organs-at-risk, via unsupervised clustering. MATERIAL AND METHODS: All patients were treated using radiation therapy for OPC. Dose-volume histograms of organs-at-risk were extracted from patients’ treatment plans. Symptom ratings were collected via the MD Anderson Symptom Inventory (MDASI) given weekly during, and 6 months post-treatment. Drymouth, trouble swallowing, mucus, and vocal dysfunction were selected for analysis in this study. Patient stratifications were obtained by applying Bayesian Mixture Models with three components to patient’s dose histograms for relevant organs. The clusters with the highest total mean doses were translated into dose thresholds using rule mining. Patient stratifications were compared against Tumor staging information using multivariate likelihood ratio tests. Model performance for prediction of moderate/severe symptoms at 6 months was compared against normal tissue complication probability (NTCP) models using cross-validation. RESULTS: A total of 349 patients were included for long-term symptom prediction. High-risk clusters were significantly correlated with outcomes for severe late drymouth (p <.0001, OR = 2.94), swallow (p = .002, OR = 5.13), mucus (p = .001, OR = 3.18), and voice (p = .009, OR = 8.99). Simplified clusters were also correlated with late severe symptoms for drymouth (p <.001, OR = 2.77), swallow (p = .01, OR = 3.63), mucus (p = .01, OR = 2.37), and voice (p <.001, OR = 19.75). Proposed cluster stratifications show better performance than NTCP models for severe drymouth (AUC.598 vs.559, MCC.143 vs.062), swallow (AUC.631 vs.561, MCC.20 vs -.030), mucus (AUC.596 vs.492, MCC.164 vs -.041), and voice (AUC.681 vs.555, MCC.181 vs -.019). Simplified dose thresholds also show better performance than baseline models for predicting late severe ratings for all symptoms. CONCLUSION: Our results show that leveraging the 3-D dose histograms from radiation therapy plan improves stratification of patients according to their risk of experiencing long-term severe radiation associated symptoms, beyond existing NTPC models. Our rule-based method can approximate our stratifications with minimal loss of accuracy and can proactively identify risk factors for radiation-associated toxicity. 

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4. A practical algorithm for VMAT optimization using column generation techniques

   https://doi.org/10.1002/mp.15776  

   Wang, Yuanbo; Liu, Hongcheng; Yang, Yu; Lu, Bo (June 2022, Medical Physics)

   Abstract PurposeAs a challenging but important optimization problem, the inverse planning for volumetric modulated arc therapy (VMAT) has attracted much research attention. The column generation (CG) type method is so far one of the most effective solution schemes. However, it often relies on simplifications leading to significant gaps between the output and the actual feasible plan. This paper presents a novel column generation (NCG) approach to push the planning results substantially closer to practice. MethodsThe proposed NCG algorithm is equipped with multiple new quality‐enhancing and computation‐facilitating modules as below: (1) Flexible constraints are enabled on both dose rates and treatment time to adapt to machine capabilities as well as planner's preferences, respectively; (2) a cross‐control‐point intermediate aperture simulation is incorporated to better conform to the underlying physics; (3) new pricing and pruning subroutines are adopted to achieve better optimization outputs. To evaluate the effectiveness of this NCG, five VMAT plans, that is, three prostate cases and two head‐and‐neck cases, were computed using proposed NCG. The planning results were compared with those yielded by a historical benchmark planning scheme. ResultsThe NCG generated plans of significantly better quality than the benchmark planning algorithm. For prostate cases, NCG plans satisfied all planning target volume (PTV) criteria whereas CG plans failed on D10% criteria of PTVs for over 9 Gy or more on all cases. For head‐and‐neck cases, again, NCG plans satisfied all PTVs criteria while CG plans failed on D10% criteria of PTVs for over 3 Gy or more on all cases as well as the max dose criteria of both cord and brain stem for over 13 Gy on one case. Moreover, the pruning scheme was found to be effective in enhancing the optimization quality. ConclusionsThe proposed NCG inherits the computational advantages of the traditional CG, while capturing a more realistic characterization of the machine capability and underlying physics. The output solutions of the NCG are substantially closer to practical implementation. 

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5. Drug target ranking for glioblastoma multiforme

   https://doi.org/10.1186/s42490-021-00052-w  

   Saraf, Radhika; Agah, Shaghayegh; Datta, Aniruddha; Jiang, Xiaoqian (April 2021, BMC Biomedical Engineering)

   Abstract BackgroundGlioblastoma Multiforme, an aggressive primary brain tumor, has a poor prognosis and no effective standard of care treatments. Most patients undergoing radiotherapy, along with Temozolomide chemotherapy, develop resistance to the drug, and recurrence of the tumor is a common issue after the treatment. We propose to model the pathways active in Glioblastoma using Boolean network techniques. The network captures the genetic interactions and possible mutations that are involved in the development of the brain tumor. The model is used to predict the theoretical efficacies of drugs for the treatment of cancer. ResultsWe use the Boolean network to rank the critical intervention points in the pathway to predict an effective therapeutic strategy for Glioblastoma. Drug repurposing helps to identify non-cancer drugs that could be effective in cancer treatment. We predict the effectiveness of drug combinations of anti-cancer and non-cancer drugs for Glioblastoma. ConclusionsGiven the genetic profile of a GBM tumor, the Boolean model can predict the most effective targets for treatment. We also identified two-drug combinations that could be more effective in killing GBM cells than conventional chemotherapeutic agents. The non-cancer drug Aspirin could potentially increase the cytotoxicity of TMZ in GBM patients. 

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