Literature DB >> 30562547

Needs and Challenges for Radiation Oncology in the Era of Precision Medicine.

Harry Quon1, Todd McNutt2, Junghoon Lee2, Michael Bowers2, Wei Jiang3, Pranav Lakshminarayanan2, Zhi Cheng2, Peijin Han2, Xuan Hui2, Veeraj Shah2, Joseph Moore2, Minoru Nakatsugawa2, Scott Robertson2, Emilie Cecil2, Brandi Page2, Ana Kiess2, John Wong2, Theodore DeWeese2.   

Abstract

Modern medicine, including the care of the cancer patient, has significantly advanced, with the evidence-based medicine paradigm serving to guide clinical care decisions. Yet we now also recognize the tremendous heterogeneity not only of disease states but of the patient and his or her environment as it influences treatment outcomes and toxicities. These reasons and many others have led to a reevaluation of the generalizability of randomized trials and growing interest in accounting for this heterogeneity under the rubric of precision medicine as it relates to personalizing clinical care predictions, decisions, and therapy for the disease state. For the cancer patient treated with radiation therapy, characterizing the spatial treatment heterogeneity has been a fundamental tenet of routine clinical care facilitated by established database and imaging platforms. Leveraging these platforms to further characterize and collate all clinically relevant sources of heterogeneity that affect the longitudinal health outcomes of the irradiated cancer patient provides an opportunity to generate a critical informatics infrastructure on which precision radiation therapy may be realized. In doing so, data science-driven insight discoveries, personalized clinical decisions, and the potential to accelerate translational efforts may be realized ideally within a network of institutions with locally developed yet coordinated informatics infrastructures. The path toward realizing these goals has many needs and challenges, which we summarize, with many still to be realized and understood. Early efforts by our group have identified the feasibility of this approach using routine clinical data sets and offer promise that this transformation can be successfully realized in radiation oncology.
Copyright © 2018. Published by Elsevier Inc.

Entities:  

Mesh:

Year:  2018        PMID: 30562547     DOI: 10.1016/j.ijrobp.2018.11.017

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  4 in total

Review 1.  Harnessing data science to advance radiation oncology.

Authors:  Ivan R Vogelius; Jens Petersen; Søren M Bentzen
Journal:  Mol Oncol       Date:  2020-05-18       Impact factor: 6.603

Review 2.  Personalization in Modern Radiation Oncology: Methods, Results and Pitfalls. Personalized Interventions and Breast Cancer.

Authors:  Cynthia Aristei; Elisabetta Perrucci; Emanuele Alì; Fabio Marazzi; Valeria Masiello; Simonetta Saldi; Gianluca Ingrosso
Journal:  Front Oncol       Date:  2021-03-18       Impact factor: 6.244

Review 3.  Clinical and Preclinical Outcomes of Combining Targeted Therapy With Radiotherapy.

Authors:  May Elbanna; Nayela N Chowdhury; Ryan Rhome; Melissa L Fishel
Journal:  Front Oncol       Date:  2021-10-18       Impact factor: 6.244

4.  Pan-Cancer Analysis of Radiotherapy Benefits and Immune Infiltration in Multiple Human Cancers.

Authors:  Pengbo Wen; Yang Gao; Bin Chen; Xiaojing Qi; Guanshuo Hu; An Xu; Junfeng Xia; Lijun Wu; Huayi Lu; Guoping Zhao
Journal:  Cancers (Basel)       Date:  2020-04-13       Impact factor: 6.639

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.