Literature DB >> 33160001

Mini review: Personalization of the radiation therapy management of prostate cancer using MRI-based radiomics.

Michelle Leech1, Sarah Osman2, Suneil Jain2, Laure Marignol3.   

Abstract

Decisions on how to treat prostate cancer with radiation therapy are guideline-based but as such guidelines have been developed for populations of patients, this invariably leads to overly aggressive treatment in some patients and insufficient treatment in others. Heterogeneity within prostate tumors and in metastatic sites, even within the same patient, is believed to be a major cause of treatment failure. Radiomics biomarkers, more commonly referred to as radiomics 'features", provide readily available, cost-effective, non-invasive tools for screening, detecting tumors and serial monitoring of patients, including assessments of response to therapy and identification of therapeutic complications. Radiomics offers the potential to analyse whole tumors in 3D, as well as sub-regions or 'habitats' within tumors. Combining quantitative information from imaging with pathology, demographic details and other biomarkers will pave the way for personalised treatment selection and monitoring in prostate cancer. The aim of this review is to consider if MRI-based radiomics can bridge the gap between population-based management and personalised management of prostate cancer.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Personalization; Prostate radiation therapy; Radiomics

Year:  2020        PMID: 33160001     DOI: 10.1016/j.canlet.2020.10.033

Source DB:  PubMed          Journal:  Cancer Lett        ISSN: 0304-3835            Impact factor:   8.679


  1 in total

1.  A Novel Combined Nomogram Model for Predicting the Pathological Complete Response to Neoadjuvant Chemotherapy in Invasive Breast Carcinoma of No Specific Type: Real-World Study.

Authors:  Xuelin Zhu; Jing Shen; Huanlei Zhang; Xiulin Wang; Huihui Zhang; Jing Yu; Qing Zhang; Dongdong Song; Liping Guo; Dianlong Zhang; Ruiping Zhu; Jianlin Wu
Journal:  Front Oncol       Date:  2022-06-06       Impact factor: 5.738

  1 in total

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