Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review

Background: Checkpoint inhibition has radically improved the perspective for patients with metastatic cancer, but predicting who will not respond with high certainty remains difficult. Imaging-derived biomarkers may be able to provide additional insights into the heterogeneity in tumour response between patients. In this systematic review, we aimed to summarise and qualitatively assess the current evidence on imaging biomarkers that predict response and survival in patients treated with checkpoint inhibitors in all cancer types. Methods: PubMed and Embase were searched from database inception to 29th November 2021. Articles eligible for inclusion described baseline imaging predictive factors, radiomics and/or imaging machine learning models for predicting response and survival in patients with any kind of malignancy treated with checkpoint inhibitors. Risk of bias was assessed using the QUIPS and PROBAST tools and data was extracted.Results: In total, 119 studies including 15,580 patients were selected. Of these studies, 73 investi-gated simple imaging factors. 45 studies investigated radiomic features or deep learning models. Predictors of worse survival were (i) higher tumour burden, (ii) presence of liver metastases, (iii) less subcutaneous adipose tissue, (iv) less dense muscle and (v) presence of symptomatic brain me-tastases. Hazard rate ratios did not exceed 2.00 for any predictor in the larger and higher quality studies. The added value of baseline fluorodeoxyglucose positron emission tomography parame-ters in predicting response to treatment was limited. Pilot studies of radioactive drug tracer imag-ing showed promising results. Reports on radiomics were almost unanimously positive, but numerous methodological concerns exist. Conclusions: There is well-supported evidence for several imaging biomarkers that can be used in clinical decision making. Further research, however, is needed into biomarkers that can more accurately identify which patients who will not benefit from checkpoint inhibition. Radiomics and radioactive drug labelling appear to be promising approaches for this purpose. 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This systematic review summarizes the current evidence on imaging biomarkers that predict response and survival in patients treated with checkpoint inhibitors in all cancer types. The review found that several imaging biomarkers, including simple imaging factors and radiomic features or deep learning models, can be used in clinical decision making. However, further research is needed to identify more accurate biomarkers for predicting which patients will not benefit from checkpoint inhibition.