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Med. Biol."],"published-print":{"date-parts":[[2024,10,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> Cachexia is a devastating condition, characterized by involuntary loss of muscle mass with or without loss of adipose tissue mass. It affects more than half of patients with lung cancer, diminishing treatment effects and increasing mortality. Cone-beam computed tomography (CBCT) images, routinely acquired during radiotherapy treatment, might contain valuable anatomical information for monitoring body composition changes associated with cachexia. For this purpose, we propose an automatic artificial intelligence (AI)-based workflow, consisting of CBCT to CT conversion, followed by segmentation of pectoralis muscles. <jats:italic>Approach.<\/jats:italic> Data from 140 stage III non-small cell lung cancer patients was used. Two deep learning models, cycle-consistent generative adversarial network (CycleGAN) and contrastive unpaired translation (CUT), were used for unpaired training of CBCT to CT conversion, to generate synthetic CT (sCT) images. The no-new U-Net (nnU-Net) model was used for automatic pectoralis muscle segmentation. To evaluate tissue segmentation performance in the absence of ground truth labels, an uncertainty metric (UM) based on Monte Carlo dropout was developed and validated. <jats:italic>Main results.<\/jats:italic> Both CycleGAN and CUT restored the Hounsfield unit fidelity of the CBCT images compared to the planning CT (pCT) images and visually reduced streaking artefacts. The nnU-Net model achieved a Dice similarity coefficient (DSC) of 0.93, 0.94, 0.92 for the CT, sCT and CBCT images, respectively, on an independent test set. The UM showed a high correlation with DSC with a correlation coefficient of \u22120.84 for the pCT dataset and \u22120.89 for the sCT dataset. <jats:italic>Significance.<\/jats:italic> This paper shows a proof-of-concept for automatic AI-based monitoring of the pectoralis muscle area of lung cancer patients during radiotherapy treatment based on CBCT images, which provides an unprecedented time resolution of muscle mass loss during cachexia progression. Ultimately, the proposed workflow could provide valuable information for early intervention of cachexia, ideally resulting in improved cancer treatment outcome.<\/jats:p>","DOI":"10.1088\/1361-6560\/ad7d5b","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T22:57:37Z","timestamp":1726786657000},"page":"205005","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Towards a fully automatic workflow for investigating the dynamics of lung cancer cachexia during radiotherapy using cone beam computed tomography"],"prefix":"10.1088","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3521-6521","authenticated-orcid":true,"given":"Lars H B A","family":"Daenen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wouter R P H","family":"van de Worp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2125-1154","authenticated-orcid":true,"given":"Behzad","family":"Rezaeifar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00ebl","family":"de Bruijn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiyu","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justine M","family":"Webster","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0128-3121","authenticated-orcid":true,"given":"St\u00e9phanie","family":"Peeters","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dirk","family":"De Ruysscher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramon C J","family":"Langen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6428-2762","authenticated-orcid":true,"given":"Cecile J A","family":"Wolfs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8470-386X","authenticated-orcid":true,"given":"Frank","family":"Verhaegen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2024,10,4]]},"reference":[{"key":"pmbad7d5bbib1","doi-asserted-by":"publisher","first-page":"2083","DOI":"10.3390\/s21062083","article-title":"Deep learning automated segmentation for muscle and adipose tissue from abdominal computed tomography in polytrauma patients","volume":"21","author":"Ackermans","year":"2021","journal-title":"Sensors"},{"key":"pmbad7d5bbib2","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2024.100566","article-title":"Evaluation of a cone-beam computed tomography system calibrated for accurate radiotherapy dose calculation","volume":"29","author":"Bogowicz","year":"2024","journal-title":"Phys. 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