A systematic review and meta-analysis of the utility of quantitative, imaging-based approaches to predict radiation-induced toxicity in lung cancer patients.
Authors
Affiliations (8)
Affiliations (8)
- Department of Radiation Oncology, University of Toronto (UTDRO), Toronto, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.
- Department of Radiation Oncology, University of Toronto (UTDRO), Toronto, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, Canada.
- Department of Radiation Oncology, University of Toronto (UTDRO), Toronto, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada.
- Department of Radiation Oncology, University of Toronto (UTDRO), Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
- Department of Radiation Oncology, University of Toronto (UTDRO), Toronto, Canada; Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Canada.
- Department of Radiation Oncology, Sheba Medical Center, Ramat Gan, Israel; Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA.
- Department of Radiation Oncology, University of Toronto (UTDRO), Toronto, Canada; Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada.
- Department of Radiation Oncology, University of Toronto (UTDRO), Toronto, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, Canada. Electronic address: [email protected].
Abstract
To conduct a systematic review and meta-analysis of the performance of radiomics, dosiomics and machine learning in generating toxicity prediction in thoracic radiotherapy. An electronic database search was conducted and dual-screened by independent authors to identify eligible studies for systematic review and meta-analysis. Data was extracted and study quality was assessed using TRIPOD for machine learning studies, RQS for Radiomics and RoB for dosiomics. 10,703 studies were identified, and 5252 entered screening. 106 studies including 23,373 patients were eligible for systematic review. Primary toxicity predicted was radiation pneumonitis (81), followed by esophagitis (12) and lymphopenia (4). Fourty-two studies studying radiation pneumonitis were eligible for meta-analysis, with pooled area-under-curve (AUC) of 0.82 (95% CI 0.79-0.85). Studies with machine learning had the best performance, with classical and deep learning models having similar performance. There is a trend towards an improvement of the performance of models with the year of publication. There is variability in study quality among the three study categories and dosiomic studies scored the highest among these. Publication bias was not observed. The majority of existing literature using radiomics, dosiomics and machine learning has focused on radiation pneumonitis prediction. Future research should focus on toxicity prediction of other organs at risk and the adoption of these models into clinical practice.