Clinical Acceptability of Automatically Generated Lymph Node Levels and Structures of Deglutition and Mastication for Head and Neck Radiation Therapy

Authors

Sean Maroongroge
Abdallah Sr Mohamed
Callistus Nguyen
Jean Guma De la Vega
Steven J Frank
Adam S Garden
Brandon G Gunn
Anna Lee
Lauren Mayo
Amy Moreno
William H Morrison
Jack Phan
Michael T Spiotto
Laurence E Court
Clifton D Fuller
David I Rosenthal
Tucker J Netherton

Publication Date

1-1-2024

Journal

Physics and Imaging in Radiation Oncology

Abstract

BACKGROUND AND PURPOSE: Auto-contouring of complex anatomy in computed tomography (CT) scans is a highly anticipated solution to many problems in radiotherapy. In this study, artificial intelligence (AI)-based auto-contouring models were clinically validated for lymph node levels and structures of swallowing and chewing in the head and neck.

MATERIALS AND METHODS: CT scans of 145 head and neck radiotherapy patients were retrospectively curated. One cohort (n = 47) was used to analyze seven lymph node levels and the other (n = 98) used to analyze 17 swallowing and chewing structures. Separate nnUnet models were trained and validated using the separate cohorts. For the lymph node levels, preference and clinical acceptability of AI vs human contours were scored. For the swallowing and chewing structures, clinical acceptability was scored. Quantitative analyses of the test sets were performed for AI vs human contours for all structures using overlap and distance metrics.

RESULTS: Median Dice Similarity Coefficient ranged from 0.77 to 0.89 for lymph node levels and 0.86 to 0.96 for chewing and swallowing structures. The AI contours were superior to or equally preferred to the manual contours at rates ranging from 75% to 91%; there was not a significant difference in clinical acceptability for nodal levels I-V for manual versus AI contours. Across all AI-generated lymph node level contours, 92% were rated as usable with stylistic to no edits. Of the 340 contours in the chewing and swallowing cohort, 4% required minor edits.

CONCLUSIONS: An accurate approach was developed to auto-contour lymph node levels and chewing and swallowing structures on CT images for patients with intact nodal anatomy. Only a small portion of test set auto-contours required minor edits.

Keywords

Deep learning, Segmentation, Chewing and swallowing structures, Lymph node levels, Radiotherapy, Contouring

Comments

Supplementary Materials

PMID: 38356692