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Normal Tissue Toxicity Prediction: Clinical Translation on the Horizon. Semin Radiat Oncol 2023 Jul;33(3):307-316

Date

06/19/2023

Pubmed ID

37331785

DOI

10.1016/j.semradonc.2023.03.010

Scopus ID

2-s2.0-85158119838 (requires institutional sign-in at Scopus site)   3 Citations

Abstract

Improvements in radiotherapy delivery have enabled higher therapeutic doses and improved efficacy, contributing to the growing number of long-term cancer survivors. These survivors are at risk of developing late toxicity from radiotherapy, and the inability to predict who is most susceptible results in substantial impact on quality of life and limits further curative dose escalation. A predictive assay or algorithm for normal tissue radiosensitivity would allow more personalized treatment planning, reducing the burden of late toxicity, and improving the therapeutic index. Progress over the last 10 years has shown that the etiology of late clinical radiotoxicity is multifactorial and informs development of predictive models that combine information on treatment (eg, dose, adjuvant treatment), demographic and health behaviors (eg, smoking, age), co-morbidities (eg, diabetes, collagen vascular disease), and biology (eg, genetics, ex vivo functional assays). AI has emerged as a useful tool and is facilitating extraction of signal from large datasets and development of high-level multivariable models. Some models are progressing to evaluation in clinical trials, and we anticipate adoption of these into the clinical workflow in the coming years. Information on predicted risk of toxicity could prompt modification of radiotherapy delivery (eg, use of protons, altered dose and/or fractionation, reduced volume) or, in rare instances of very high predicted risk, avoidance of radiotherapy. Risk information can also be used to assist treatment decision-making for cancers where efficacy of radiotherapy is equivalent to other treatments (eg, low-risk prostate cancer) and can be used to guide follow-up screening in instances where radiotherapy is still the best choice to maximize tumor control probability. Here, we review promising predictive assays for clinical radiotoxicity and highlight studies that are progressing to develop an evidence base for clinical utility.

Author List

Kerns SL, Hall WA, Marples B, West CML

Authors

William Adrian Hall MD Chair, Professor in the Radiation Oncology department at Medical College of Wisconsin
Sarah L. Kerns PhD Associate Professor in the Radiation Oncology department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Humans
Male
Prostatic Neoplasms
Quality of Life
Radiation Injuries
Radiation Tolerance
Radiotherapy Dosage