{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T11:00:39Z","timestamp":1791198039445,"version":"4.1.0"},"reference-count":24,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:00:00Z","timestamp":1791158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Purpose: Volumetric PET biomarkers carry substantial prognostic value in head and neck squamous cell carcinoma (HNSCC), yet their clinical implementation is hampered by segmentation heterogeneity. A fundamental and unresolved question is which segmentation method is most suitable for routine clinical use, both for a reader experienced in PET segmentation and for a reader without PET segmentation experience, such as an ENT surgeon or a radiation oncologist. This study addressed this question by assessing the intra- and interobserver reproducibility of four PET segmentation methods in MIM Maestro Software Version 7.4.2, identifying tumour-related determinants of variability, and examining concordance between PET-derived metabolic tumour volume (MTV) and CT-based gross tumour volume (GTV). Methods: Two readers with contrasting PET segmentation experience, a nuclear medicine physician experienced in PET segmentation (R1) and an ENT surgeon without prior PET segmentation training (R2), applied four methods to the primary tumours of 154 patients with HNSCC (oral cavity, oropharynx, hypopharynx or larynx) treated with definitive (chemo)radiotherapy between 2010 and 2021: fixed absolute thresholds at SUV 3.0 and 4.0, a relative threshold at 41% of the maximum standardised uptake value (SUVmax), and a gradient edge-detection algorithm (PET Edge+). R1 segmented twice with a washout of at least 4 weeks; R2 performed one session and delineated CT-GTV in the 85 patients whose PET\/CT-derived CT served directly as the radiotherapy planning scan. The second session of R1 served as the reference for all interobserver and MTV\u2013GTV analyses. Agreement was assessed by ICC (two-way mixed model), coefficient of variation, repeatability coefficient and Bland\u2013Altman analysis; determinants of variability by a multivariable linear mixed-effects model with a random intercept per patient. Results: All four methods demonstrated excellent intraobserver (ICC 0.993 to &gt;0.999) and interobserver (ICC 0.989\u20130.996) reproducibility. SUV3.0 generated significantly more relative error than SUV41% (\u03b2 = +3.1%; 95% CI 0.89, 5.3). PET Edge+ was the only method for which a significant method \u00d7 reader interaction was observed in this reader pair: the reader without PET segmentation experience generated more error than the reader experienced in PET segmentation (\u03b2 = +7.1%; 95% CI 3.9, 10), an effect concentrated in smaller tumours (below approximately 15 mL); in the 85-patient GTV subset, where tumours were larger on average, the interobserver ICC for PET Edge+ was 0.997. GTV concordance was good for three of four methods (ICC 0.808\u20130.895); SUV41% showed only moderate concordance (ICC 0.572). Conclusions: All four methods provided excellent volumetric reproducibility within a single clinical software platform, but they differed in reader dependence and in agreement with CT-defined GTV. Rather than supporting a universal segmentation standard, these findings suggest that the method should be selected according to its intended application, pending multicentre confirmation.<\/jats:p>","DOI":"10.3390\/cancers18193214","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T10:02:27Z","timestamp":1791194547000},"page":"3214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PET Tumour Segmentation in Head and Neck Cancer: Reproducibility, Reader Dependence and Concordance with CT-Defined Tumour Volume in a 154-Patient Cohort"],"prefix":"10.3390","volume":"18","author":[{"given":"Anne-Sophie","family":"Pirson","sequence":"first","affiliation":[{"name":"Department of Nuclear Medicine, CHU UCL Namur, 5530 Yvoir, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gilles","family":"Delahaut","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology\u2014Head and Neck Surgery, CHU UCL Namur, 5530 Yvoir, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3781-5145","authenticated-orcid":false,"given":"Rapha\u00ebl","family":"Georis","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, CHU UCL Namur, 5530 Yvoir, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"St\u00e9phanie","family":"Deheneffe","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, CHU UCL Namur, 5000 Namur, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u00e9bastien","family":"Van der Vorst","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology\u2014Head and Neck Surgery, CHU UCL Namur, 5530 Yvoir, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ken","family":"Kudura","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, CHU UCL Namur, 5530 Yvoir, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1053\/j.semnuclmed.2014.12.005","article-title":"PET\/MR in cancers of the head and neck","volume":"45","author":"Queiroz","year":"2015","journal-title":"Semin. Nucl. Med."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Caldarella, C., De Risi, M., Massaccesi, M., Miccich\u00e8, F., Bussu, F., Galli, J., Rufini, V., and Leccisotti, L. (2024). Role of 18F-FDG PET\/CT in head and neck squamous cell carcinoma: Current evidence and innovative applications. Cancers, 16.","DOI":"10.3390\/cancers16101905"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"289","DOI":"10.2214\/AJR.17.18301","article-title":"Clinical practice in PET\/CT for the management of head and neck squamous cell cancer","volume":"209","author":"Goel","year":"2017","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Marcus, C. (2025). Use of FDG PET for staging and re-staging of head and neck squamous cell carcinoma. Cancers, 17.","DOI":"10.3390\/cancers17193140"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"884","DOI":"10.2967\/jnumed.113.133801","article-title":"Prognostic value of metabolic tumor volume and total lesion glycolysis in head and neck cancer: A systematic review and meta-analysis","volume":"55","author":"Pak","year":"2014","journal-title":"J. Nucl. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e30798","DOI":"10.1097\/MD.0000000000030798","article-title":"Prognostic value of MTV and TLG of 18F-FDG PET in patients with head and neck squamous cell carcinoma: A meta-analysis","volume":"101","author":"Jin","year":"2022","journal-title":"Medicine"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.1016\/j.ijrobp.2007.07.2333","article-title":"Comparison of five segmentation tools for 18F-fluoro-deoxy-glucose-positron emission tomography-based target volume definition in head and neck cancer","volume":"69","author":"Schinagl","year":"2007","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.radonc.2016.12.008","article-title":"Head and neck target delineation using a novel PET automatic segmentation algorithm","volume":"122","author":"Berthon","year":"2017","journal-title":"Radiother. Oncol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1007\/s00259-012-2127-7","article-title":"Use of pretreatment metabolic tumour volumes to predict the outcome of pharyngeal cancer treated by definitive radiotherapy","volume":"39","author":"Kao","year":"2012","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1007\/s00259-006-0363-4","article-title":"A gradient-based method for segmenting FDG-PET images: Methodology and validation","volume":"34","author":"Geets","year":"2007","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1016\/j.ijrobp.2014.12.023","article-title":"Metabolic tumor volume as a prognostic imaging-based biomarker for head-and-neck cancer: Pilot results from Radiation Therapy Oncology Group protocol 0522","volume":"91","author":"Schwartz","year":"2015","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A guideline of selecting and reporting intraclass correlation coefficients for reliability research","volume":"15","author":"Koo","year":"2016","journal-title":"J. Chiropr. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13550-020-00744-9","article-title":"Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET","volume":"11","author":"Pfaehler","year":"2021","journal-title":"EJNMMI Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1007\/s11604-022-01378-8","article-title":"The reproducibility of MTV and TLG of soft tissue tumors calculated by FDG-PET: Comparison between the lower limit by the fixed value SUV 2.5 and that value by 30% of SUVmax","volume":"41","author":"Iwasa","year":"2023","journal-title":"Jpn. J. Radiol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guezennec, C., Bourhis, D., Orlhac, F., Robin, P., Corre, J.-B., Delcroix, O., Gobel, Y., Schick, U., Sala\u00fcn, P.-Y., and Abgral, R. (2019). Inter-observer and segmentation method variability of textural analysis in pre-therapeutic FDG PET\/CT in head and neck cancer. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0214299"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1097\/MNM.0000000000000317","article-title":"Quantification of metabolic tumor activity and burden in patients with non-small-cell lung cancer: Is manual adjustment of semiautomatic gradient-based measurements necessary?","volume":"36","author":"Obara","year":"2015","journal-title":"Nucl. Med. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.radonc.2011.05.040","article-title":"Correlation between metabolic tumor volume and pathologic tumor volume in squamous cell carcinoma of the oral cavity","volume":"101","author":"Murphy","year":"2011","journal-title":"Radiother. Oncol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s12094-012-0914-z","article-title":"Tumor volume delineation in head and neck cancer with 18-fluor-fluorodeoxiglucose positron emission tomography: Adaptive thresholding method applied to primary tumors and metastatic lymph nodes","volume":"15","year":"2013","journal-title":"Clin. Transl. Oncol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2822","DOI":"10.21037\/qims-22-798","article-title":"Impact of tumour region of interest delineation method for mid-treatment FDG-PET response prediction in head and neck squamous cell carcinoma undergoing radiotherapy","volume":"13","author":"Trada","year":"2023","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1016\/j.ijrobp.2008.10.060","article-title":"Metabolic tumor volume predicts for recurrence and death in head-and-neck cancer","volume":"74","author":"La","year":"2009","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s00259-013-2528-2","article-title":"Predictive value of pre-therapy 18F-FDG PET\/CT for the outcome of 18F-FDG PET-guided radiotherapy in patients with head and neck cancer","volume":"41","author":"Picchio","year":"2014","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1007\/s00259-014-2961-x","article-title":"FDG PET\/CT: EANM procedure guidelines for tumour imaging: Version 2.0","volume":"42","author":"Boellaard","year":"2015","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s40708-026-00296-z","article-title":"U-Net-based transfer learning for automated tumour segmentation enabling fully automated [18F]F-DOPA PET analysis in paediatric gliomas","volume":"13","author":"Mureddu","year":"2026","journal-title":"Brain Inform."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.ijrobp.2006.12.039","article-title":"Intraobserver and interobserver variability in GTV delineation on FDG-PET-CT images of head and neck cancers","volume":"68","author":"Breen","year":"2007","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/19\/3214\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T10:16:27Z","timestamp":1791195387000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/19\/3214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,5]]},"references-count":24,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["cancers18193214"],"URL":"https:\/\/doi.org\/10.3390\/cancers18193214","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,5]]}}}