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Articles by Radhe Mohan
Bayesian mediation multi-state model elucidates the impact of cancer immunity on multiple oncology endpoints after chemoradiotherapy for esophageal cancer
Abstract Unraveling the complex relationships among interventions, intermediate health states, and survival outcomes is crucial to translating advances in clinical research into optimal cancer therapies. We propose a novel Bayesian multi-state mediation model to evaluate the direct and indirect effects of radiation modalities on overall survival (OS).
"Clinical Translation of a Deep Learning Model of Radiation-Induced Lym" by Zongsheng Hu, Radhe Mohan et al.
PURPOSE: Radiation-induced lymphopenia is a common immune toxicity that adversely impacts treatment outcomes. We report here our approach to translate a deep-learning (DL) model developed to predict severe lymphopenia risk among esophageal cancer into a strategy for incorporating the immune system as an organ-at-risk (iOAR) to mitigate the risk.
Incorporating Variable RBE in IMPT Optimization for Ependymoma
PURPOSE: To 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. METHODS: This study included 10 pediatric ependymoma patients with challenging anatomical features for treatment planning.
GPU-accelerated calculation of proton microdosimetric spectra as a function of target size, proton energy, and bounding volume size
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Declarations of independence: How embedded multicollinearity errors affect dosimetric and other complex analyses in radiation oncology
The statistical technique of multiple regression, commonly referred to as “multivariable regression,” is often used in clinical research to quantify the relationships between multiple predictor variables and a single outcome variable of interest. The foundational theory underpinning multivariable regression assumes that all predictor variables are independent of one another.
Identifying Individualized Risk Profiles for Radiotherapy-Induced Lymphopenia Among Patients With Esophageal Cancer Using Machine Learning
Abstract Radiotherapy (RT)-induced lymphopenia (RIL) is commonly associated with adverse clinical outcomes in patients with cancer. Using machine learning techniques, a retrospective study was conducted for patients with esophageal cancer treated with proton and photon therapies to characterize the principal pretreatment clinical and radiation dosimetric risk factors of grade 4 RIL (G4RIL) as well as to establish G4RIL risk profiles.
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