Journal Article

DKFZ-2024-00192

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Segmentation of 71 Anatomical Structures Necessary for the Evaluation of Guideline-Conforming Clinical Target Volumes in Head and Neck Cancers.

Walter, A. (First author)DKFZ* ; Hoegen-Saßmannshausen, P.DKFZ* ; Stanic, G.DKFZ* ; Rodrigues, J. P.DKFZ* ; Adeberg, S. ; Jäkel, O.DKFZ* ; Frank, M. ; Giske, K. (Last author)DKFZ*

2024
MDPI Basel

This record in other databases:

Please use a persistent id in citations: doi:10.3390/cancers16020415

Abstract: The delineation of the clinical target volumes (CTVs) for radiation therapy is time-consuming, requires intensive training and shows high inter-observer variability. Supervised deep-learning methods depend heavily on consistent training data; thus, State-of-the-Art research focuses on making CTV labels more homogeneous and strictly bounding them to current standards. International consensus expert guidelines standardize CTV delineation by conditioning the extension of the clinical target volume on the surrounding anatomical structures. Training strategies that directly follow the construction rules given in the expert guidelines or the possibility of quantifying the conformance of manually drawn contours to the guidelines are still missing. Seventy-one anatomical structures that are relevant to CTV delineation in head- and neck-cancer patients, according to the expert guidelines, were segmented on 104 computed tomography scans, to assess the possibility of automating their segmentation by State-of-the-Art deep learning methods. All 71 anatomical structures were subdivided into three subsets of non-overlapping structures, and a 3D nnU-Net model with five-fold cross-validation was trained for each subset, to automatically segment the structures on planning computed tomography scans. We report the DICE, Hausdorff distance and surface DICE for 71 + 5 anatomical structures, for most of which no previous segmentation accuracies have been reported. For those structures for which prediction values have been reported, our segmentation accuracy matched or exceeded the reported values. The predictions from our models were always better than those predicted by the TotalSegmentator. The sDICE with 2 mm margin was larger than 80% for almost all the structures. Individual structures with decreased segmentation accuracy are analyzed and discussed with respect to their impact on the CTV delineation following the expert guidelines. No deviation is expected to affect the rule-based automation of the CTV delineation.

Keyword(s): anatomical structures ; automatic segmentation ; clinical target volume delineation ; expert guidelines ; head and neck cancer ; lymph-node-level segmentation ; multi-label segmentation

Note: #EA:E040#LA:E040#

---

Contributing Institute(s):

1. E040 Med. Physik in der Strahlentherapie (E040)
2. E050 KKE Strahlentherapie (E050)

Research Program(s):

1. 315 - Bildgebung und Radioonkologie (POF4-315) (POF4-315)

Appears in the scientific report 2024

---

Database coverage:
; ; ; Article Processing Charges ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; Fees ; IF >= 5 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

---