A research article titled "Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy" featuring Moffitt authors was recently published in Scientific Reports. Subtle differences in a patient’s genetics and physiology may alter radiotherapy (RT) treatment responses, motivating the need for a more personalized treatment plan. Accordingly, investigators developed a novel quantum deep reinforcement learning (qDRL) framework for clinical decision support that can estimate an individual patient’s dose response mid-treatment and recommend an optimal dose adjustment. The framework considers patients’ specific information including biological, physical, genetic, clinical, and dosimetric factors. Analysis shows that the framework, which takes into consideration individual patient dose response in its decision-making, can potentially improve clinical RT decision-making by at least about 10% compared to unaided clinical practice. Further validation of the novel quantitative approach in a prospective study will provide a necessary framework for improving the standard of care in personalized RT. Congratulations to all Moffitt authors: Dipesh Niraula and Issam El Naqa! https://doi.org/10.1038/s41598-021-02910-y