![Sample our Medicine, Dentistry, Nursing & Allied Health journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5944/TOC-Subject-Sample-2021-Medicine-Dentistry-Nursing-Allied-Health.jpg"})
ABSTRACT
Introduction
Patient selection remains challenging as the clinical use of re-irradiation (re-RT) increases. Re-RT data are limited to retrospective studies and small prospective single-institution reports, resulting in small, heterogenous data sets. Validated prognostic and predictive biomarkers are derived from large-volume studies with long-term follow-up. This review aims to examine existing re-RT publications and available data sets and discuss strategies using artificial intelligence (AI) to approach small data sets to optimize the use of re-RT data.
Methods
Re-RT publications were identified where associated public data were present. The existing literature on small data sets to identify biomarkers was also explored.
Results
Publications with associated public data were identified, with glioma and nasopharyngeal cancers emerging as the most common tumor sites where the use of re-RT was the primary management approach. Existing and emerging AI strategies have been used to approach small data sets including data generation, augmentation, discovery, and transfer learning.
Conclusions
Further data is needed to generate adaptive frameworks, improve the collection of specimens for molecular analysis, and improve the interpretability of results in re-RT data.
Article highlights
Given prolonged survival for certain malignancies and technological improvements of radiation delivery, the indications for re-RT have been increasing.
Most re-RT data are limited to retrospective studies or single-institution experiences.
Given the limitations of small data sets for re-RT patients, computational methods may be used to enhance existing data to arrive at clinically actionable biomarkers.
Limitations to clinically use biomarkers include validation and identifying the optimal method to collect specimens for molecular analysis.
Abbreviations
| CNS | = | Central Nervous System |
| CGGA | = | Chinese Glioma Genome Atlas |
| CV | = | Cross-Validation |
| DB-MTD | = | Distance-Based Mega-Trend-Diffusion |
| EPV | = | Events per Predictor Variable |
| GA | = | Genetic Algorithms |
| GVES | = | Gene Vector for Each Sample |
| GyE | = | Gray equivalent |
| HSRT | = | Hypofractionated stereotactic radiation therapy |
| IMIT | = | Intensity Modulated Ion Therapy |
| IMPT | = | Intensity Modulated Proton Therapy |
| IMRT | = | Intensity Modulated Radiation Therapy |
| KPS | = | Karnofsky Performance Score |
| LASSO | = | Least Absolute Shrinkage and Selection Operator |
| MEVT | = | - Modified Extreme Value Theory |
| mRMR | = | Maximum Relevance Minimum Redundancy |
| OS | = | Overall Survival |
| P | = | - Prospective |
| PFS | = | Progression-Free Survival |
| R | = | Retrospective |
| RFE | = | Recursive Feature Elimination |
| RBE | = | Relative Biological Effectiveness |
| Re-RT | = | Reirradiation |
| S | = | Systematic review |
| SRS | = | Stereotactic radiosurgery |
| TCGA | = | The Cancer Genome Atlas |
Declaration of interests
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
Reviewer disclosures
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Author contributions
All authors have substantially contributed to the conception and design of the review article and interpreting the relevant literature, and have been involved in writing the review article or revised it for intellectual content. All authors have read and agreed to the published version of the manuscript.