Benefits of automated gross tumor volume segmentation in head and neck cancer using multi-modality information

Purpose: Gross tumor volume (GTV) delineation for head and neck cancer (HNC) radiation therapy plan-ning is time consuming and prone to interobserver variability (IOV). The aim of this study was (1) to develop an automated GTV delineation approach of primary tumor (GTVp) and pathologic lymph nodes (GTVn) based on a 3D convolutional neural network (CNN) exploiting multi-modality imaging input as required in clinical practice, and (2) to validate its accuracy, efficiency and IOV compared to manual delineation in a clinical setting.Methods: Two datasets were retrospectively collected from 150 clinical cases. CNNs were trained for GTV delineation with consensus delineation as ground truth, with either single (CT) or co-registered multi -modal (CT + PET or CT + MRI) imaging data as input. For validation, GTVs were delineated on 20 new cases by two observers, once manually, once by correcting the delineations generated by the CNN.Results: Both multi-modality CNNs performed better than the single-modality CNN and were selected for clinical validation. Mean Dice Similarity Coefficient (DSC) for (GTVp, GTVn) respectively between auto-mated and manual delineations was (69%, 79%) for CT + PET and (59%,71%) for CT + MRI. Mean DSC between automated and corrected delineations was (81%,89%) for CT + PET and (69%,77%) for CT + MRI. Mean DSC between observers was (76%,86%) for manual delineations and (95%,96%) for cor-rected delineations, indicating a significant decrease in IOV (p < 10-5), while efficiency increased signif-icantly (48%, p < 10-5).Conclusion: Multi-modality automated delineation of GTV of HNC was shown to be more efficient and consistent compared to manual delineation in a clinical setting and beneficial over a single-modality approach.(c) 2023 The Author(s). Published by Elsevier B.V. Radiotherapy and Oncology 182 (2023) 1-8 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

An automated gross tumor volume (GTV) delineation approach based on a 3D convolutional neural network (CNN) was developed for head and neck cancer (HNC) radiation therapy planning. The multi-modality CNNs showed better performance compared to the single-modality CNN, and were proven to be more efficient and consistent than manual delineation in a clinical setting, leading to increased efficiency and reduced interobserver variability.