Multi-Phase Mandible-Anchored Automated Segmentation of Oropharyngeal GTVs in FDG-PET/CT

Dominic LaBella, Keshav Jha, Kevin Goff, Esther Yu, Jared Robbins

Keywords: Oropharyngeal Cancer, FDG-PET/CT, Automated Segmentation, MONAI, Auto3DSeg, SegResNet, Radiotherapy Planning, Dose De-escalation

Abstract: Accurate delineation of gross tumor volumes (GTVs) in oropharyngeal carcinoma remains central to radiotherapy (RT) planning, and recent advances in automated segmentation are beginning to influence multiple aspects of clinical workflow. However, automated segmentation of head and neck primary tumors and nodal metastases in multi-modal FDG-PET/CT is challenging due to anatomical complexity and varying image resolution. We describe the DLaBella29 team’s approach for Task 1 of the MICCAI 2025 HECKTOR challenge, which involves a multi-phase deep learning pipeline for GTVp and GTVn segmentation. Our method leverages the MONAI Auto3DSeg framework with a 3D SegResNet backbone, trained on co-registered PET/CT scans using a cross-validation strategy (3/7/15 folds across Phases). Key innovations include a mandible-anchored region-of-interest cropping strategy derived from automated mandible segmentation to focus the model on the oropharyngeal region and improve efficiency. A multi-phase segmentation pipeline was employed: an initial Phase infers GTVp and GTVn in the focused ROI, and a second Phase independently refines these predictions. Post-processing ensues to merge GTVp and GTVn outputs to the original image coordinates. On the HECKTOR 2025 test set, our more limited and resource-constrained algorithm achieved a primary tumor Dice Similarity Coefficient (DSC) of 0.5289, an aggregated nodal DSC of 0.6156, and a GTVn detection F1-score of 0.5561, indicating fair performance in the challenge. This paper details the clinical context, methodology, cross-validation and testing results, and the implications of PET/CT-guided automated segmentation for oropharyngeal carcinoma.

Submission Number: 8