Guidance on the use of PET for treatment planning in radiotherapy clinical trials

Clinical Evaluation of Deep Learning for Tumor Delineation on 18F-FDG PET/CT of Head and Neck Cancer

Artificial intelligence (AI) may decrease 18F-FDG PET/CT-based gross tumor volume (GTV) delineation variability and automate tumor-volume-derived image biomarker extraction. Hence, we aimed to identify and evaluate promising state-of-the-art deep learning methods for head and neck cancer (HNC) PET GTV delineation. Methods: We trained and evaluated deep learning methods using retrospectively included scans of HNC patients referred for radiotherapy between January 2014 and December 2019 (ISRCTN16907234). We used 3 test datasets: an internal set to compare methods, another internal set to compare AI-to-expert variability and expert interobserver variability (IOV), and an external set to compare internal and external AI-to-expert variability. Expert PET GTVs were used as the reference standard. Our benchmark IOV was measured using the PET GTV of 6 experts. The primary outcome was the Dice similarity coefficient (DSC). ANOVA was used to compare methods, a paired t test was used to compare AI-to-expert variability and expert IOV, an unpaired t test was used to compare internal and external AI-to-expert variability, and post hoc Bland-Altman analysis was used to evaluate biomarker agreement. Results: In total, 1,220 18F-FDG PET/CT scans of 1,190 patients (mean age ± SD, 63 ± 10 y; 858 men) were included, and 5 deep learning methods were trained using 5-fold cross-validation (n = 805). The nnU-Net method achieved the highest similarity (DSC, 0.80 [95% CI, 0.77-0.86]; n = 196). We found no evidence of a difference between expert IOV and AI-to-expert variability (DSC, 0.78 for AI vs. 0.82 for experts; mean difference of 0.04 [95% CI, -0.01 to 0.09]; P = 0.12; n = 64). We found no evidence of a difference between the internal and external AI-to-expert variability (DSC, 0.80 internally vs. 0.81 externally; mean difference of 0.004 [95% CI, -0.05 to 0.04]; P = 0.87; n = 125). PET GTV-derived biomarkers of AI were in good agreement with experts. Conclusion: Deep learning can be used to automate 18F-FDG PET/CT tumor-volume-derived imaging biomarkers, and the deep-learning-based volumes have the potential to assist clinical tumor volume delineation in radiation oncology.

Clinical use of positron emission tomography for radiotherapy planning - Medical physics considerations

PET/CT imaging plays an increasing role in radiotherapy treatment planning. The aim of this article was to identify the major use cases and technical as well as medical physics challenges during integration of these data into treatment planning. Dedicated aspects, such as (i) PET/CT-based radiotherapy simulation, (ii) PET-based target volume delineation, (iii) functional avoidance to optimized organ-at-risk sparing and (iv) functionally adapted individualized radiotherapy are discussed in this article. Furthermore, medical physics aspects to be taken into account are summarized and presented in form of check-lists.

The use of systematic review evidence to support the development of guidelines for positron emission tomography: a cross-sectional survey

OBJECTIVES

To examine to what degree guidelines for PET and PET/CT used systematic review evidence.

METHODS

The latest version of guidelines for PET, PET/CT or PET/MRI published in English in PubMed until December 2019 was analysed in two categories: (1) for indications, if mainly discussing the appropriate use of PET in diverse conditions; (2) for procedures, if providing step-by-step instructions for imaging. We surveyed the general characteristics and the use of systematic review evidence for developing recommendations across all guidelines, and surveyed the citation of evidence for five recommendation topics in guidelines for procedures.

RESULTS

Forty-seven guidelines, published between 2004 and 2020, were included. Guidelines for indications were developed mainly on systematic reviews (13 of 19, 68.4%). Among those, 12 (63.2%) reported the level of evidence, 4 (21.1%) reported the strength of recommendations, 3 (15.8%) described external review and 7 (36.8%) involved methodologists. Guidelines for procedures were seldom developed on systematic reviews (1 of 27, 3.7%). Among those, 1 (3.7%) reported the level of evidence, 1 (3.7%) reported the strength of recommendations, 3 (11.1%) described external review and 1 (3.7%) involved methodologists. Systematic review evidence was cited by 2 (7.4%) procedure guidelines per recommendation topic in median.

CONCLUSION

The use of systematic review evidence for developing recommendations among PET or PET/CT guidelines was suboptimal. While our survey is an icebreaking attempt to explore a key element (i.e. use of systematic review evidence) for developing nuclear medicine guidelines, assessments of other domains of guideline quality may help capture the entire picture.

KEY POINTS

• The use of systematic review evidence for developing recommendations among guidelines for PET or PET/CT was suboptimal. • Only 13 (68.4%) guidelines for indications and 1 (3.7%) guideline for procedures systematically reviewed the literature during guideline development. • For each recommendation topic we examined, only a median of 2 (7.4%) procedure guidelines cited systematic review evidence.