{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T21:00:10Z","timestamp":1776718810993,"version":"3.51.2"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031669576","type":"print"},{"value":"9783031669583","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-66958-3_18","type":"book-chapter","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T20:20:08Z","timestamp":1721766008000},"page":"242-255","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction of\u00a0Total Metabolic Tumor Volume from\u00a0Tissue-Wise FDG-PET\/CT Projections, Interpreted Using Cohort Saliency Analysis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5550-3575","authenticated-orcid":false,"given":"Sambit","family":"Tarai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2955-4958","authenticated-orcid":false,"given":"Elin","family":"Lundstr\u00f6m","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0253-9037","authenticated-orcid":false,"given":"Johan","family":"\u00d6fverstedt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8389-975X","authenticated-orcid":false,"given":"Hanna","family":"J\u00f6nsson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0202-9205","authenticated-orcid":false,"given":"Nouman","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8701-969X","authenticated-orcid":false,"given":"H\u00e5kan","family":"Ahlstr\u00f6m","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8205-7569","authenticated-orcid":false,"given":"Joel","family":"Kullberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,24]]},"reference":[{"key":"18_CR1","doi-asserted-by":"publisher","unstructured":"Cardoso, M.J., et\u00a0al.: MONAI: an open-source framework for deep learning in healthcare. arXiv preprint arXiv:2211.02701 (2022). https:\/\/doi.org\/10.48550\/arXiv.2211.02701","DOI":"10.48550\/arXiv.2211.02701"},{"issue":"3","key":"18_CR2","doi-asserted-by":"publisher","first-page":"386","DOI":"10.2967\/jnumed.110.082586","volume":"52","author":"AF Cashen","year":"2011","unstructured":"Cashen, A.F., Dehdashti, F., Luo, J., Homb, A., Siegel, B.A., Bartlett, N.L.: 18F-FDG PET\/CT for early response assessment in diffuse large b-cell lymphoma: poor predictive value of international harmonization project interpretation. J. Nucl. Med. 52(3), 386\u2013392 (2011). https:\/\/doi.org\/10.2967\/jnumed.110.082586","journal-title":"J. Nucl. Med."},{"issue":"1","key":"18_CR3","doi-asserted-by":"publisher","first-page":"2764","DOI":"10.1038\/s41598-019-39206-1","volume":"9","author":"A Diamant","year":"2019","unstructured":"Diamant, A., Chatterjee, A., Valli\u00e8res, M., Shenouda, G., Seuntjens, J.: Deep learning in head & neck cancer outcome prediction. Sci. Rep. 9(1), 2764 (2019). https:\/\/doi.org\/10.1038\/s41598-019-39206-1","journal-title":"Sci. Rep."},{"key":"18_CR4","doi-asserted-by":"publisher","first-page":"101745","DOI":"10.1016\/j.compmedimag.2020.101745","volume":"84","author":"S Ekstr\u00f6m","year":"2020","unstructured":"Ekstr\u00f6m, S., Malmberg, F., Ahlstr\u00f6m, H., Kullberg, J., Strand, R.: Fast graph-cut based optimization for practical dense deformable registration of volume images. Comput. Med. Imaging Graph. 84, 101745 (2020). https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101745","journal-title":"Comput. Med. Imaging Graph."},{"issue":"1","key":"18_CR5","doi-asserted-by":"publisher","first-page":"13111","DOI":"10.1038\/s41598-023-40218-1","volume":"13","author":"MC Ferr\u00e1ndez","year":"2023","unstructured":"Ferr\u00e1ndez, M.C., et al.: An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients. Sci. Rep. 13(1), 13111 (2023). https:\/\/doi.org\/10.1038\/s41598-023-40218-1","journal-title":"Sci. Rep."},{"issue":"1","key":"18_CR6","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1038\/s41597-022-01718-3","volume":"9","author":"S Gatidis","year":"2022","unstructured":"Gatidis, S., et al.: A whole-body FDG-PET\/CT dataset with manually annotated tumor lesions. Sci. Data 9(1), 601 (2022). https:\/\/doi.org\/10.1038\/s41597-022-01718-3","journal-title":"Sci. Data"},{"issue":"2","key":"18_CR7","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies, R.J., Kinahan, P.E., Hricak, H.: Radiomics: images are more than pictures, they are data. Radiology 278(2), 563\u2013577 (2016). https:\/\/doi.org\/10.1148\/radiol.2015151169","journal-title":"Radiology"},{"issue":"12","key":"18_CR8","doi-asserted-by":"publisher","first-page":"1925","DOI":"10.2967\/jnumed.121.263501","volume":"63","author":"KB Girum","year":"2022","unstructured":"Girum, K.B., et al.: 18F-FDG PET maximum-intensity projections and artificial intelligence: a win-win combination to easily measure prognostic biomarkers in DLBCL patients. J. Nucl. Med. 63(12), 1925\u20131932 (2022). https:\/\/doi.org\/10.2967\/jnumed.121.263501","journal-title":"J. Nucl. Med."},{"issue":"23","key":"18_CR9","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.1093\/eurheartj\/ehw302","volume":"38","author":"BA Goldstein","year":"2017","unstructured":"Goldstein, B.A., Navar, A.M., Carter, R.E.: Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. Eur. Heart J. 38(23), 1805\u20131814 (2017). https:\/\/doi.org\/10.1093\/eurheartj\/ehw302","journal-title":"Eur. Heart J."},{"issue":"44","key":"18_CR10","doi-asserted-by":"publisher","first-page":"e5131","DOI":"10.1097\/MD.0000000000005131","volume":"95","author":"C Hu","year":"2016","unstructured":"Hu, C., Liu, C.P., Cheng, J.S., Chiu, Y.L., Chan, H.P., Peng, N.J.: Application of whole-body FDG-PET for cancer screening in a cohort of hospital employees. Medicine 95(44), e5131 (2016). https:\/\/doi.org\/10.1097\/MD.0000000000005131","journal-title":"Medicine"},{"key":"18_CR11","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"issue":"2","key":"18_CR12","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Meth. 18(2), 203\u2013211 (2021). https:\/\/doi.org\/10.1038\/s41592-020-01008-z","journal-title":"Nat. Meth."},{"issue":"1 suppl","key":"18_CR13","doi-asserted-by":"publisher","first-page":"28S","DOI":"10.1109\/CVPR.2017.243","volume":"48","author":"O Israel","year":"2007","unstructured":"Israel, O., Kuten, A.: Early detection of cancer recurrence: 18F-FDG PET\/CT can make a difference in diagnosis and patient care. J. Nucl. Med. 48(1 suppl), 28S-35S (2007). https:\/\/doi.org\/10.1109\/CVPR.2017.243","journal-title":"J. Nucl. Med."},{"issue":"1","key":"18_CR14","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1186\/s12938-023-01173-0","volume":"22","author":"H J\u00f6nsson","year":"2023","unstructured":"J\u00f6nsson, H., Ahlstr\u00f6m, H., Kullberg, J.: Spatial mapping of tumor heterogeneity in whole-body PET-CT: a feasibility study. Biomed. Eng. Online 22(1), 110 (2023). https:\/\/doi.org\/10.1186\/s12938-023-01173-0","journal-title":"Biomed. Eng. Online"},{"issue":"1","key":"18_CR15","doi-asserted-by":"publisher","first-page":"18768","DOI":"10.1038\/s41598-022-23361-z","volume":"12","author":"H J\u00f6nsson","year":"2022","unstructured":"J\u00f6nsson, H., et al.: An image registration method for voxel-wise analysis of whole-body oncological PET-CT. Sci. Rep. 12(1), 18768 (2022). https:\/\/doi.org\/10.1038\/s41598-022-23361-z","journal-title":"Sci. Rep."},{"issue":"7","key":"18_CR16","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.3324\/haematol.2021.278663","volume":"107","author":"L Kostakoglu","year":"2022","unstructured":"Kostakoglu, L., et al.: Total metabolic tumor volume as a survival predictor for patients with diffuse large B-cell lymphoma in the GOYA study. Haematologica 107(7), 1633 (2022). https:\/\/doi.org\/10.3324\/haematol.2021.278663","journal-title":"Haematologica"},{"key":"18_CR17","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","volume":"13","author":"K Kourou","year":"2015","unstructured":"Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I.: Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13, 8\u201317 (2015). https:\/\/doi.org\/10.1016\/j.csbj.2014.11.005","journal-title":"Comput. Struct. Biotechnol. J."},{"issue":"1","key":"18_CR18","doi-asserted-by":"publisher","first-page":"10425","DOI":"10.1038\/s41598-017-08925-8","volume":"7","author":"J Kullberg","year":"2017","unstructured":"Kullberg, J., et al.: Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies. Sci. Rep. 7(1), 10425 (2017). https:\/\/doi.org\/10.1038\/s41598-017-08925-8","journal-title":"Sci. Rep."},{"issue":"3","key":"18_CR19","doi-asserted-by":"publisher","first-page":"e210178","DOI":"10.1148\/ryai.210178","volume":"4","author":"T Langner","year":"2022","unstructured":"Langner, T., Mart\u00ednez Mora, A., Strand, R., Ahlstr\u00f6m, H., Kullberg, J.: MIMIR: deep regression for automated analysis of UK biobank MRI scans. Radiol. Artif. Intell. 4(3), e210178 (2022). https:\/\/doi.org\/10.1148\/ryai.210178","journal-title":"Radiol. Artif. Intell."},{"key":"18_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1007\/978-3-030-59713-9_58","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"T Langner","year":"2020","unstructured":"Langner, T., Strand, R., Ahlstr\u00f6m, H., Kullberg, J.: Large-scale inference of liver fat with\u00a0neural networks on UK biobank body\u00a0MRI. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 602\u2013611. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_58"},{"issue":"5","key":"18_CR21","doi-asserted-by":"publisher","first-page":"1430","DOI":"10.1109\/TMI.2019.2950092","volume":"39","author":"T Langner","year":"2019","unstructured":"Langner, T., Wikstr\u00f6m, J., Bjerner, T., Ahlstr\u00f6m, H., Kullberg, J.: Identifying morphological indicators of aging with neural networks on large-scale whole-body MRI. IEEE Trans. Med. Imaging 39(5), 1430\u20131437 (2019). https:\/\/doi.org\/10.1109\/TMI.2019.2950092","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"18_CR22","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.ijrobp.2007.04.036","volume":"69","author":"P Lee","year":"2007","unstructured":"Lee, P., et al.: Metabolic tumor burden predicts for disease progression and death in lung cancer. Int. J. Radiat. Oncol. Biol. Phys. 69(2), 328\u2013333 (2007). https:\/\/doi.org\/10.1016\/j.ijrobp.2007.04.036","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"issue":"30","key":"18_CR23","doi-asserted-by":"publisher","first-page":"3618","DOI":"10.1200\/JCO.2016.66.9440","volume":"34","author":"M Meignan","year":"2016","unstructured":"Meignan, M., et al.: Baseline metabolic tumor volume predicts outcome in high-tumor-burden follicular lymphoma: a pooled analysis of three multicenter studies. J. Clin. Oncol. 34(30), 3618\u20133626 (2016). https:\/\/doi.org\/10.1200\/JCO.2016.66.9440","journal-title":"J. Clin. Oncol."},{"issue":"21","key":"18_CR24","doi-asserted-by":"publisher","first-page":"2352","DOI":"10.1200\/JCO.21.02063","volume":"40","author":"NG Mikhaeel","year":"2022","unstructured":"Mikhaeel, N.G., et al.: Proposed new dynamic prognostic index for diffuse large B-cell lymphoma: international metabolic prognostic index. J. Clin. Oncol. 40(21), 2352 (2022). https:\/\/doi.org\/10.1200\/JCO.21.02063","journal-title":"J. Clin. Oncol."},{"issue":"1","key":"18_CR25","doi-asserted-by":"publisher","first-page":"23195","DOI":"10.1038\/s41598-021-02734-w","volume":"11","author":"P Pinochet","year":"2021","unstructured":"Pinochet, P., Texte, E., Stamatoullas-Bastard, A., Vera, P., Mihailescu, S.D., Becker, S.: Prognostic value of baseline metabolic tumour volume in advanced-stage Hodgkin\u2019s lymphoma. Sci. Rep. 11(1), 23195 (2021). https:\/\/doi.org\/10.1038\/s41598-021-02734-w","journal-title":"Sci. Rep."},{"key":"18_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III 18. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"18_CR27","doi-asserted-by":"publisher","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.74","DOI":"10.1109\/ICCV.2017.74"},{"issue":"4","key":"18_CR28","doi-asserted-by":"publisher","first-page":"2972","DOI":"10.21037\/tcr.2020.03.16","volume":"9","author":"LF Shen","year":"2020","unstructured":"Shen, L.F., Zhou, S.H., Yu, Q.: Predicting response to radiotherapy in tumors with PET\/CT: when and how. Transl. Cancer Res. 9(4), 2972 (2020). https:\/\/doi.org\/10.21037\/tcr.2020.03.16","journal-title":"Transl. Cancer Res."},{"key":"18_CR29","doi-asserted-by":"publisher","first-page":"e26414","DOI":"10.1016\/j.heliyon.2024.e26414","volume":"10","author":"S Tarai","year":"2024","unstructured":"Tarai, S., et al.: Improved automated tumor segmentation in whole-body 3D scans using multi-directional 2D projection-based priors. Heliyon 10, e26414 (2024). https:\/\/doi.org\/10.1016\/j.heliyon.2024.e26414","journal-title":"Heliyon"},{"issue":"16","key":"18_CR30","doi-asserted-by":"publisher","first-page":"1396","DOI":"10.1182\/blood.2019003526","volume":"135","author":"L Vercellino","year":"2020","unstructured":"Vercellino, L., et al.: High total metabolic tumor volume at baseline predicts survival independent of response to therapy. Blood J. Am. Soc. Hematol. 135(16), 1396\u20131405 (2020). https:\/\/doi.org\/10.1182\/blood.2019003526","journal-title":"Blood J. Am. Soc. Hematol."}],"container-title":["Lecture Notes in Computer Science","Medical Image Understanding and Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-66958-3_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T07:04:43Z","timestamp":1729580683000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-66958-3_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031669576","9783031669583"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-66958-3_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"24 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIUA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference on Medical Image Understanding and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Manchester","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miua2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miua2024.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}