Machine-learned target volume delineation of 18F-FDG PET images after one cycle of induction chemotherapy

Abstract

Biological tumour volume (GTVPET) delineation on 18 F-FDG PET acquired during induction chemotherapy (ICT) is challenging due to the reduced metabolic uptake and volume of the GTVPET. Automatic segmentation algorithms applied to 18 F-FDG PET (PET-AS) imaging have been used for GTVPET delineation on 18 F-FDG PET imaging acquired before ICT. However, their role has not been investigated in 18 F-FDG PET imaging acquired after ICT. In this study we investigate PET-AS techniques, including ATLAAS a machine learned method, for accurate delineation of the GTVPET after ICT. Twenty patients were enrolled onto a prospective phase I study (FiGaRO). PET/CT imaging was acquired at baseline and 3 weeks following 1 cycle of induction chemotherapy. The GTVPET was manually delineated by a nuclear medicine physician and clinical oncologist. The resulting GTVPET was used as the reference contour. The ATLAAS original statistical model was expanded to include images of reduced metabolic activity and the ATLAAS algorithm was retrained on the new reference dataset. Estimated GTVPET contours were derived using sixteen PET-AS methods and compared to the GTVPET using the Dice Similarity Coefficient (DSC). The mean DSC for ATLAAS, 60% Peak Thresholding (PT60), Adaptive Thresholding (AT) and Watershed Thresholding (WT) was 0.72, 0.61, 0.63 and 0.60 respectively. The GTVPET generated by ATLAAS compared favourably with manually delineated volumes and in comparison, to other PET-AS methods, was more accurate for GTVPET delineation after ICT. ATLAAS would be a feasible method to reduce inter-observer variability in multi-centre trials.

Applications of artificial intelligence in oncologic 18F-FDG PET/CT imaging: a systematic review

“…Delineation of post-therapeutic biologic tumor volume in the 18 F-FDG PET images of patients with oropharyngeal squamous cell carcinoma is challenging secondary to decreased 18 F-FDG avidity and smaller volumes. Parkinson et al developed an ML model for biological tumor volume delineation after one cycle of chemotherapy (30). 20 patients with oropharyngeal squamous cell cancer who had 18 F-FDG PET/CT at baseline and 3 weeks post-chemotherapy were included.…”

Artificial intelligence in radiation oncology: A review of its current status and potential application for the radiotherapy workforce

“…Further to this, the difficulty of adapting RT plans during treatment can be eased. An example of this is, intratreatment auto-contouring [3], [35], [36] after chemotherapy and the adaptive RT planning of image-guided radiation therapy (IGRT) is being investigated within the PEARL clinical trial [37], [38]. AI-based auto contouring of the MTV, GTV and OAR is viable across a variety of imaging modalities and tumour sites [35], [36], [39]- [46].…”

“…An example of this is, intratreatment auto-contouring [3], [35], [36] after chemotherapy and the adaptive RT planning of image-guided radiation therapy (IGRT) is being investigated within the PEARL clinical trial [37], [38]. AI-based auto contouring of the MTV, GTV and OAR is viable across a variety of imaging modalities and tumour sites [35], [36], [39]- [46]. Image-guided radiation therapy relies upon the acquisition of computed tomography (CT) and repeated cone-beam computed tomography (CBCT) imaging.…”