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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. Modeling Competitive Dynamics Between Healthy and Cancerous Liver Cells with Yes-Associated Protein (YAP) Hyperactivation

   https://doi.org/10.3390/math13213479  

   Slack, Emma; Gandhi, Darsh; Roy, Souvik; Kojouharov, Hristo V; Chen-Charpentier, Benito (November 2025, Mathematics)

   Current treatments for hepatocellular carcinoma (HCC) include partial hepatectomy (PH), where two-thirds of the liver is removed. However, when some cancer remains after PH, competition for resources and survival occurs between healthy and cancerous cells. Recent studies suggest that hyperactivation of yes-associated protein (YAP) could be a non-invasive treatment option for HCC. In this study, we propose two simple ordinary differential equation models of resource competition in HCC livers after PH, one capturing the natural dynamics of resource competition between healthy and cancer cells and the other incorporating YAP hyperactivation therapy. We perform full qualitative and quantitative analyses of these models and validate them on experimental data. Our numerical simulations reveal that treatment schedules must be prescribed based on a patient’s tumor aggression. We also found that a high dose of treatment is necessary to completely clear a tumor in all patients, leading us to suggest prescribing YAP hyperactivation therapy in lower doses with other treatments for HCC for the best patient outcomes. 

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4. Validation of the graded prognostic assessment and recursive partitioning analysis as prognostic tools using a modern cohort of patients with brain metastases

   https://doi.org/10.1093/nop/npae057  

   Sperber, Jacob; Yoo, Seeley; Owolo, Edwin; Dalton, Tara; Zachem, Tanner J; Johnson, Eli; Herndon, James E; Nguyen, Annee D; Hockenberry, Harrison; Bishop, Brandon; et al (June 2024, Neuro-Oncology Practice)

   Abstract BackgroundPrognostic indices for patients with brain metastases (BM) are needed to individualize treatment and stratify clinical trials. Two frequently used tools to estimate survival in patients with BM are the recursive partitioning analysis (RPA) and the diagnosis-specific graded prognostic assessment (DS-GPA). Given recent advances in therapies and improved survival for patients with BM, this study aims to validate and analyze these 2 models in a modern cohort. MethodsPatients diagnosed with BM were identified via our institution’s Tumor Board meetings. Data were retrospectively collected from the date of diagnosis with BM. The concordance of the RPA and GPA was calculated using Harrell’s C index. A Cox proportional hazards model with backwards elimination was used to generate a parsimonious model predictive of survival. ResultsOur study consisted of 206 patients diagnosed with BM between 2010 and 2019. The RPA had a prediction performance characterized by Harrell’s C index of 0.588. The DS-GPA demonstrated a Harrell’s C index of 0.630. A Cox proportional hazards model assessing the effect of age, presence of lung, or liver metastases, and Eastern Cooperative Oncology Group (ECOG) performance status score of 3/4 on survival yielded a Harrell’s C index of 0.616. Revising the analysis with an uncategorized ECOG demonstrated a C index of 0.648. ConclusionsWe found that the performance of the RPA remains unchanged from previous validation studies a decade earlier. The DS-GPA outperformed the RPA in predicting overall survival in our modern cohort. Analyzing variables shared by the RPA and DS-GPA produced a model that performed analogously to the DS-GPA. 

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5. Explainable AI for Predicting Mortality Risk in Metastatic Cancer: Retrospective Cohort Study Using the Memorial Sloan Kettering-Metastatic Dataset

   https://doi.org/10.2196/74196  

   Nalela, Polycarp; Rao, Deepthi; Rao, Praveen (January 2026, JMIR Cancer)

   BackgroundMetastatic cancer remains one of the leading causes of cancer-related mortality worldwide. Yet, the prediction of survivability in this population remains limited by heterogeneous clinical presentations and high-dimensional molecular features. Advances in machine learning (ML) provide an opportunity to integrate diverse patient- and tumor-level factors into explainable predictive ML models. Leveraging large real-world datasets and modern ML techniques can enable improved risk stratification and precision oncology. ObjectiveThis study aimed to develop and interpret ML models for predicting overall survival in patients with metastatic cancer using the Memorial Sloan Kettering-Metastatic (MSK-MET) dataset and to identify key prognostic biomarkers through explainable artificial intelligence techniques. MethodsWe performed a retrospective analysis of the MSK-MET cohort, comprising 25,775 patients across 27 tumor types. After data cleaning and balancing, 20,338 patients were included. Overall survival was defined as deceased versus living at last follow-up. Five classifiers (extreme gradient boosting [XGBoost], logistic regression, random forest, decision tree, and naive Bayes) were trained using an 80/20 stratified split and optimized via grid search with 5-fold cross-validation. Model performance was assessed using accuracy, area under the curve (AUC), precision, recall, and F1-score. Model explainability was achieved using Shapley additive explanations (SHAP). Survival analyses included Kaplan-Meier estimates, Cox proportional hazards models, and an XGBoost-Cox model for time-to-event prediction. The positive predictive value and negative predictive value were calculated at the Youden index–optimal threshold. ResultsXGBoost achieved the highest performance (accuracy=0.74; AUC=0.82), outperforming other classifiers. In survival analyses, the XGBoost-Cox model with a concordance index (C-index) of 0.70 exceeded the traditional Cox model (C-index=0.66). SHAP analysis and Cox models consistently identified metastatic site count, tumor mutational burden, fraction of genome altered, and the presence of distant liver and bone metastases as among the strongest prognostic factors, a pattern that held at both the pan-cancer level and recurrently across cancer-specific models. At the cancer-specific level, performance varied; prostate cancer achieved the highest predictive accuracy (AUC=0.88), while pancreatic cancer was notably more challenging (AUC=0.68). Kaplan-Meier analyses demonstrated marked survival separation between patients with and without metastases (80-month survival: approximately 0.80 vs 0.30). At the Youden-optimal threshold, positive predictive value and negative predictive value were approximately 70% and 80%, respectively, supporting clinical use for risk stratification. ConclusionsExplainable ML models, particularly XGBoost combined with SHAP, can strongly predict survivability in metastatic cancers while highlighting clinically meaningful features. These findings support the use of ML-based tools for patient counseling, treatment planning, and integration into precision oncology workflows. Future work should include external validation on independent cohorts, integration with electronic health records via Fast Healthcare Interoperability Resources–based dashboards, and prospective clinician-in-the-loop evaluation to assess real-world use. 

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