{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T14:18:40Z","timestamp":1792333120156,"version":"4.4.0"},"reference-count":44,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100015766","name":"Danone Nutricia Research","doi-asserted-by":"publisher","award":["Preclinical development of intervention strategies in lung cancer cachexia"],"award-info":[{"award-number":["Preclinical development of intervention strategies in lung cancer cachexia"]}],"id":[{"id":"10.13039\/100015766","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models.<\/jats:p>","DOI":"10.3390\/cancers13184585","type":"journal-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T21:37:12Z","timestamp":1631569032000},"page":"4585","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans"],"prefix":"10.3390","volume":"13","author":[{"given":"Wouter R. P. H.","family":"van de Worp","sequence":"first","affiliation":[{"name":"Department of Respiratory Medicine, NUTRIM\u2014School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, 6229 ER Maastricht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8412-6417","authenticated-orcid":false,"given":"Brent","family":"van der Heyden","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology (MAASTRO), GROW\u2014School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ER Maastricht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios","family":"Lappas","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology (MAASTRO), GROW\u2014School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ER Maastricht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3585-0676","authenticated-orcid":false,"given":"Ardy","family":"van Helvoort","sequence":"additional","affiliation":[{"name":"Department of Respiratory Medicine, NUTRIM\u2014School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, 6229 ER Maastricht, The Netherlands"},{"name":"Danone Nutricia Research, 3584 CT Utrecht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9718-8251","authenticated-orcid":false,"given":"Jan","family":"Theys","sequence":"additional","affiliation":[{"name":"Department of Precision Medicine, GROW\u2014School for Oncology and Developmental Biology, Maastricht University Medical Center+, 6229 ER Maastricht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annemie M. W. J.","family":"Schols","sequence":"additional","affiliation":[{"name":"Department of Respiratory Medicine, NUTRIM\u2014School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, 6229 ER Maastricht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Verhaegen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology (MAASTRO), GROW\u2014School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ER Maastricht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramon C. J.","family":"Langen","sequence":"additional","affiliation":[{"name":"Department of Respiratory Medicine, NUTRIM\u2014School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, 6229 ER Maastricht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21590","article-title":"Cancer statistics, 2020","volume":"70","author":"Siegel","year":"2020","journal-title":"CA A Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1002\/jcsm.12402","article-title":"Orphan disease status of cancer cachexia in the USA and in the European Union: A systematic review","volume":"10","author":"Anker","year":"2019","journal-title":"J. Cachexia Sarcopenia Muscle"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/S0149-2918(05)80001-3","article-title":"Prognostic effect of weight loss prior tochemotherapy in cancer patients","volume":"69","author":"Dewys","year":"1980","journal-title":"Am. J. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1038\/sj.bjc.6601781","article-title":"Do patients with weight loss have a worse outcome when undergoing chemotherapy for lung cancers?","volume":"90","author":"Ross","year":"2004","journal-title":"Br. J. Cancer"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1699","DOI":"10.1007\/s00520-014-2534-3","article-title":"Prognostic impact of cancer cachexia in patients with advanced non-small cell lung cancer","volume":"23","author":"Kimura","year":"2015","journal-title":"Support. Care Cancer"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.cell.2015.08.068","article-title":"Preclinical mouse cancer models: A maze of opportunities and challenges","volume":"163","author":"Day","year":"2015","journal-title":"Cell"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"20160441","DOI":"10.1259\/bjr.20160441","article-title":"Tumour and normal tissue radiobiology in mouse models: How close are mice to mini-humans?","volume":"90","author":"Koontz","year":"2017","journal-title":"Br. J. Radiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1038\/nature12626","article-title":"Influence of tumour micro-environment heterogeneity on therapeutic response","volume":"501","author":"Junttila","year":"2013","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mordant, P., Loriot, Y., Lahon, B., Castier, Y., Les\u00e8che, G., Soria, J.-C., Vozenin, M.-C., Decraene, C., and Deutsch, E. (2011). Bioluminescent Orthotopic Mouse Models of Human Localized Non-Small Cell Lung Cancer: Feasibility and Identification of Circulating Tumour Cells. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0026073"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"14.27.11","DOI":"10.1002\/0471141755.ph1427s62","article-title":"Utility and applications of orthotopic models of human non-small cell lung cancer (NSCLC) for the evaluation of novel and emerging cancer therapeutics","volume":"62","author":"Justilien","year":"2013","journal-title":"Curr. Protoc. Pharmacol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"20180476","DOI":"10.1259\/bjr.20180476","article-title":"An orthotopic non-small cell lung cancer model for image-guided small animal radiotherapy platforms","volume":"92","author":"Iglesias","year":"2019","journal-title":"Br. J. Radiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1038\/sj.neo.7900098","article-title":"Solitary lung tumors and their spontaneous metastasis in athymic nude mice orthotopically implanted with human non-small cell lung cancer","volume":"2","author":"Yamaura","year":"2000","journal-title":"Neoplasia"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"369","DOI":"10.5625\/lar.2010.26.4.369","article-title":"The Incidence Rate and Severity of Orthotopic Lung Cancer in an Animal Model Depends on the Number of A549 Cells and Transplantation Period","volume":"26","author":"Lee","year":"2010","journal-title":"Lab. Anim. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1038\/laban.1217","article-title":"Improving quality of science through better animal welfare: The NC3Rs strategy","volume":"46","author":"Prescott","year":"2017","journal-title":"Lab. Anim."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1593\/neo.03481","article-title":"High-resolution X-ray microtomography for the detection of lung tumors in living mice","volume":"6","author":"Meurrens","year":"2004","journal-title":"Neoplasia"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"15353500200403184","DOI":"10.1162\/15353500200403184","article-title":"In Vivo Respiratory-Gated Micro-CT Imaging in Small-Animal Oncology Models","volume":"3","author":"Cavanaugh","year":"2004","journal-title":"Mol. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1097\/01.rli.0000160070.67270.05","article-title":"Murine lung tumor measurement using respiratory-gated micro-computed tomography","volume":"40","author":"Cody","year":"2005","journal-title":"Investig. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1593\/neo.81030","article-title":"A quantitative volumetric micro-computed tomography method to analyze lung tumors in genetically engineered mouse models","volume":"11","author":"Haines","year":"2009","journal-title":"Neoplasia"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1111\/j.1349-7006.2009.01199.x","article-title":"Quantification of mouse pulmonary cancer models by microcomputed tomography imaging","volume":"100","author":"Fushiki","year":"2009","journal-title":"Cancer Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1016\/j.ijrobp.2009.11.038","article-title":"Imaging primary lung cancers in mice to study radiation biology","volume":"76","author":"Kirsch","year":"2010","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4793","DOI":"10.1118\/1.3476454","article-title":"Longitudinal assessment of lung cancer progression in the mouse using in vivo micro-CT imaging","volume":"37","author":"Namati","year":"2010","journal-title":"Med. Phys."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/1756-9966-31-15","article-title":"Lung tumour growth kinetics in SPC-c-Raf-1-BB transgenic mice assessed by longitudinal in-vivo micro-CT quantification","volume":"31","author":"Rodt","year":"2012","journal-title":"J. Exp. Clin. Cancer Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1016\/j.media.2013.07.002","article-title":"Individual nodule tracking in micro-CT images of a longitudinal lung cancer mouse model","volume":"17","author":"Rudyanto","year":"2013","journal-title":"Med. Image Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1016\/j.media.2010.04.008","article-title":"Atlas-based whole-body segmentation of mice from low-contrast Micro-CT data","volume":"14","author":"Baiker","year":"2010","journal-title":"Med. Image Anal."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"20180364","DOI":"10.1259\/bjr.20180364","article-title":"Automatic multiatlas based organ at risk segmentation in mice","volume":"92","author":"Podesta","year":"2019","journal-title":"Br. J. Radiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1152\/japplphysiol.00465.2019","article-title":"Automated CT-derived skeletal muscle mass determination in lower hind limbs of mice using a 3D U-Net deep learning network","volume":"128","author":"Theys","year":"2020","journal-title":"J. Appl. Physiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5626","DOI":"10.1038\/s41467-020-19449-7","article-title":"Deep learning-enabled multi-organ segmentation in whole-body mouse scans","volume":"11","author":"Schoppe","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2140","DOI":"10.1101\/gad.1820209","article-title":"Contextual extracellular cues promote tumor cell EMT and metastasis by regulating miR-200 family expression","volume":"23","author":"Gibbons","year":"2009","journal-title":"Genes Dev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.radonc.2017.11.016","article-title":"ESTRO ACROP: Technology for precision small animal radiotherapy research: Optimal use and challenges","volume":"126","author":"Verhaegen","year":"2018","journal-title":"Radiother. Oncol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1118\/1.1374247","article-title":"AAPM protocol for 40-300 kV x-ray beam dosimetry in radiotherapy and radiobiology","volume":"28","author":"Ma","year":"2001","journal-title":"Med. Phys."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"20180447","DOI":"10.1259\/bjr.20180447","article-title":"The effect of different image reconstruction techniques on pre-clinical quantitative imaging and dual-energy CT","volume":"92","author":"Vaniqui","year":"2019","journal-title":"Br. J. Radiol."},{"key":"ref_32","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Transactions on Petri Nets and Other Models of Concurrency XV, Springer Science and Business Media LLC.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.-A. (2016, January 25\u201328). V-net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4568","DOI":"10.1002\/mp.13141","article-title":"Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017","volume":"45","author":"Yang","year":"2018","journal-title":"Med. Phys."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11174","DOI":"10.1038\/s41598-021-90599-4","article-title":"Automatic detect lung node with deep learning in segmentation and imbalance data labeling","volume":"11","author":"Chiu","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Islam, M., Vibashan, V.S., Jose, V.J.M., Wijethilake, N., Utkarsh, U., and Ren, H. (2020). Brain Tumor Segmentation and Survival Prediction Using 3D Attention UNet. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer.","DOI":"10.1007\/978-3-030-46640-4_25"},{"key":"ref_38","first-page":"839","article-title":"Validation of image segmentation by estimating rater bias and variance","volume":"9","author":"Warfield","year":"2006","journal-title":"Med. Image Comput. Comput. Assist. Interv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1007\/s00330-018-5695-5","article-title":"Inter-observer variability of manual contour delineation of structures in CT","volume":"29","author":"Joskowicz","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"27940","DOI":"10.1038\/srep27940","article-title":"Multimodal Correlative Preclinical Whole Body Imaging and Segmentation","volume":"6","author":"Dafni","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"245014","DOI":"10.1088\/1361-6560\/ab59a4","article-title":"Prediction of major torso organs in low-contrast micro-CT images of mice using a two-stage deeply supervised fully convolutional network","volume":"64","author":"Wang","year":"2019","journal-title":"Phys. Med. Biol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"20180446","DOI":"10.1259\/bjr.20180446","article-title":"Exploring the feasibility of a clinical proton beam with an adaptive aperture for pre-clinical research","volume":"92","author":"Almeida","year":"2019","journal-title":"Br. J. Radiol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"20180744","DOI":"10.1259\/bjr.20180744","article-title":"Dose painting by dynamic irradiation delivery on an image-guided small animal radiotherapy platform","volume":"92","author":"Verde","year":"2019","journal-title":"Br. J. Radiol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10994-012-5310-y","article-title":"A theory of transfer learning with applications to active learning","volume":"90","author":"Yang","year":"2013","journal-title":"Mach. Learn."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/13\/18\/4585\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:01:32Z","timestamp":1760166092000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/13\/18\/4585"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,13]]},"references-count":44,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["cancers13184585"],"URL":"https:\/\/doi.org\/10.3390\/cancers13184585","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,13]]}}}