{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T23:55:03Z","timestamp":1793318103902,"version":"4.5.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T00:00:00Z","timestamp":1779753600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T00:00:00Z","timestamp":1779753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Deutsches Krebsforschungszentrum (DKFZ)"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur J Nucl Med Mol Imaging"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>\n                      To investigate the prognostic value of an artificial intelligence (AI)\u2013based semi-automated tool for longitudinal whole-body quantification of total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) on [\n                      <jats:sup>1<\/jats:sup>\n                      \u2078F]FDG PET\/CT in patients with metastatic melanoma undergoing immune checkpoint inhibitor (ICI) therapy, and to assess its prognostic relevance alongside established PET-based metabolic response criteria.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Forty-three patients with unresectable metastatic melanoma treated with ICIs underwent [\n                      <jats:sup>1<\/jats:sup>\n                      \u2078F]FDG PET\/CT at baseline (n\u2009=\u200943), after two cycles (interim; n\u2009=\u200941), and after four cycles of therapy (late; n\u2009=\u200943). Whole-body tumor segmentation was performed using a previously validated AI-based framework combining ensemble unsupervised segmentation and deep representation learning, followed by expert review. TMTV and TLG were calculated for each time point. Overall survival (OS) was analyzed using Kaplan\u2013Meier estimates, log-rank tests, and Cox proportional hazards regression. Multivariable models included LDH, AJCC stage, and ECOG performance status. Metabolic response was additionally assessed using EORTC, PERCIST, PERCIMT, imPERCIST5, and iPERCIST criteria.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Median follow up [95% CI] of the patient cohort from the date of baseline PET\/CT was 97.8\u00a0months [90.1\u2014134.5\u00a0months]. Automated whole-body segmentation and volumetric quantification were feasible in all examinations. In univariable Cox analysis, baseline TMTV and TLG showed non-significant associations with OS. At interim and late PET\/CT, higher TMTV and TLG were significantly associated with worse OS (interim: p\u2009=\u20090.04 for both; late: p\u2009=\u20090.03 for TMTV, p\u2009=\u20090.02 for TLG). Median-based dichotomization confirmed shorter OS in patients with elevated volumetric parameters with a statistically significant association for TLG (logrank p\u2009=\u20090.021) and a trend toward significance for TMTV (logrank p\u2009=\u20090.10) at baseline. At interim imaging, both TMTV (logrank p\u2009=\u20090.01) and TLG (logrank p\u2009=\u20090.04) were significantly associated with worse OS, with the strongest associations observed at late follow-up (both logrank p\u2009=\u20090.009). In multivariable analysis, elevated late TMTV and TLG remained independently associated with poorer OS, alongside increased LDH, whereas lower ECOG performance status independently predicted improved survival. Among the applied metabolic response criteria, at both interim and late PET\/CT, survival curves demonstrated a non-significant trend toward longer OS in responders (CMR\u2009+\u2009PMR) compared with non-responders (SMD\u2009+\u2009PMD), with this trend being most pronounced for PERCIMT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      AI-based longitudinal whole-body quantification of TMTV and TLG on [\n                      <jats:sup>1<\/jats:sup>\n                      \u2078F]FDG PET\/CT appears to provide independent prognostic information in metastatic melanoma patients undergoing immunotherapy. In particular, volumetric PET metrics derived from late follow-up imaging demonstrate robust association with OS and may outperform conventional response criteria. AI-driven whole-body tumor burden assessment may enhance objective risk stratification and support treatment monitoring in the era of immunotherapy.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00259-026-07915-1","type":"journal-article","created":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T07:46:33Z","timestamp":1779781593000},"page":"5907-5920","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Beyond target lesions: Prognostic value of longitudinal AI-derived whole-body [1\u2078F]FDG PET\/CT metrics in metastatic melanoma"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8739-8741","authenticated-orcid":false,"given":"Christos","family":"Sachpekidis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Theodoros P.","family":"Vagenas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marilena","family":"M\u00fcller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitrios","family":"Strauss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annette","family":"Kopp-Schneider","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leyun","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica C.","family":"Hassel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George K.","family":"Matsopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonia","family":"Dimitrakopoulou-Strauss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,26]]},"reference":[{"key":"7915_CR1","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1093\/jnci\/djq455","volume":"103","author":"Y Xing","year":"2011","unstructured":"Xing Y, Bronstein Y, Ross MI, et al. Contemporary diagnostic imaging modalities for the staging and surveillance of melanoma patients: a metaanalysis. J Natl Cancer Inst. 2011;103:129\u201342.","journal-title":"J Natl Cancer Inst"},{"key":"7915_CR2","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1007\/s00259-015-3187-2","volume":"43","author":"SC Sch\u00fcle","year":"2016","unstructured":"Sch\u00fcle SC, Eigentler TK, Garbe C, la Foug\u00e8re C, Nikolaou K, Pfannenberg C. Influence of (18F)-FDG PET\/CT on therapy management in patients with stage III\/IV malignant melanoma. Eur J Nucl Med Mol Imaging. 2016;43:482\u20138.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"5","key":"7915_CR3","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1007\/s00259-020-05068-3","volume":"48","author":"J van Sluis","year":"2021","unstructured":"van Sluis J, de Heer EC, Boellaard M, Jalving M, Brouwers AH, Boellaard R. Clinically feasible semi-automatic workflows for measuring metabolically active tumour volume in metastatic melanoma. Eur J Nucl Med Mol Imaging. 2021;48(5):1498\u2013510.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"9","key":"7915_CR4","doi-asserted-by":"publisher","first-page":"1768","DOI":"10.1007\/s00259-019-04383-8","volume":"46","author":"PF H\u00f8ilund-Carlsen","year":"2019","unstructured":"H\u00f8ilund-Carlsen PF, Edenbrandt L, Alavi A. Global disease score (GDS) is the name of the game! Eur J Nucl Med Mol Imaging. 2019;46(9):1768\u201372.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"1","key":"7915_CR5","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-17670-y","volume":"11","author":"C Robert","year":"2020","unstructured":"Robert C. A decade of immune-checkpoint inhibitors in cancer therapy. Nat Commun. 2020;11(1):3801.","journal-title":"Nat Commun"},{"key":"7915_CR6","doi-asserted-by":"publisher","first-page":"453","DOI":"10.3389\/fimmu.2019.00453","volume":"10","author":"DO Khair","year":"2019","unstructured":"Khair DO, Bax HJ, Mele S, et al. Combining immune checkpoint inhibitors: established and emerging targets and strategies to improve outcomes in melanoma. Front Immunol. 2019;10:453.","journal-title":"Front Immunol"},{"key":"7915_CR7","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1016\/S1470-2045(19)30483-8","volume":"20","author":"JC Hassel","year":"2019","unstructured":"Hassel JC. 5-year results for pembrolizumab treatment of advanced melanoma. Lancet Oncol. 2019;20:1187\u20139.","journal-title":"Lancet Oncol"},{"issue":"10304","key":"7915_CR8","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1016\/S0140-6736(21)01206-X","volume":"398","author":"MS Carlino","year":"2021","unstructured":"Carlino MS, Larkin J, Long GV. Immune checkpoint inhibitors in melanoma. Lancet. 2021;398(10304):1002\u201314.","journal-title":"Lancet"},{"key":"7915_CR9","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1038\/s41590-022-01141-1","volume":"23","author":"AC Huang","year":"2022","unstructured":"Huang AC, Zappasodi R. A decade of checkpoint blockade immunotherapy in melanoma: understanding the molecular basis for immune sensitivity and resistance. Nat Immunol. 2022;23:660\u201370.","journal-title":"Nat Immunol"},{"key":"7915_CR10","doi-asserted-by":"publisher","first-page":"7412","DOI":"10.1158\/1078-0432.CCR-09-1624","volume":"15","author":"JD Wolchok","year":"2009","unstructured":"Wolchok JD, et al. Guidelines for the evaluation of immune therapy activity in solid tumors: immune-related response criteria. Clin Cancer Res. 2009;15:7412\u201320.","journal-title":"Clin Cancer Res"},{"key":"7915_CR11","doi-asserted-by":"publisher","first-page":"e143","DOI":"10.1016\/S1470-2045(17)30074-8","volume":"18","author":"L Seymour","year":"2017","unstructured":"Seymour L, et al. iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics. Lancet Oncol. 2017;18:e143\u201352.","journal-title":"Lancet Oncol"},{"issue":"2","key":"7915_CR12","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1056\/NEJMra1703481","volume":"378","author":"MA Postow","year":"2018","unstructured":"Postow MA, Sidlow R, Hellmann MD. Immune-related adverse events associated with immune checkpoint blockade. N Engl J Med. 2018;378(2):158\u201368.","journal-title":"N Engl J Med"},{"issue":"3","key":"7915_CR13","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1093\/annonc\/mdz003","volume":"30","author":"E Borcoman","year":"2019","unstructured":"Borcoman E, Kanjanapan Y, Champiat S, et al. Novel patterns of response under immunotherapy. Ann Oncol. 2019;30(3):385\u201396. https:\/\/doi.org\/10.1093\/annonc\/mdz003.","journal-title":"Ann Oncol"},{"key":"7915_CR14","doi-asserted-by":"crossref","unstructured":"Das S, Johnson DB. Immune-related adverse events and antitumor efficacy of immune checkpoint inhibitors. J Immunother Cancer. 2019;7(1):306.","DOI":"10.1186\/s40425-019-0805-8"},{"issue":"2","key":"7915_CR15","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1148\/radiol.212481","volume":"304","author":"DJ Hughes","year":"2022","unstructured":"Hughes DJ, Subesinghe M, Taylor B, Bille A, Spicer J, Papa S, et al. 18F FDG PET\/CT and novel molecular imaging for directing immunotherapy in cancer. Radiology. 2022;304(2):246\u201364. https:\/\/doi.org\/10.1148\/radiol.212481.","journal-title":"Radiology"},{"key":"7915_CR16","doi-asserted-by":"publisher","DOI":"10.3389\/fonc","volume":"12","author":"AM Berz","year":"2022","unstructured":"Berz AM, Dromain C, Vietti-Violi N, Boughdad S, Duran R. Tumor response assessment on imaging following immunotherapy. Front Oncol. 2022;12:982983. https:\/\/doi.org\/10.3389\/fonc.","journal-title":"Front Oncol"},{"key":"7915_CR17","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1007\/s00259-020-04702-4","volume":"47","author":"N Aide","year":"2020","unstructured":"Aide N, De Pontdeville M, Lopci E. Evaluating response to immunotherapy with 18F-FDG PET\/CT: where do we stand? Eur J Nucl Med Mol Imaging. 2020;47:1019\u201321.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"7","key":"7915_CR18","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1007\/s00259-022-05780-2","volume":"49","author":"E Lopci","year":"2022","unstructured":"Lopci E, et al. Joint EANM\/SNMMI\/ANZSNM practice guidelines\/procedure standards on recommended use of [18F]FDG PET\/CT imaging during immunomodulatory treatments in patients with solid tumors version 1.0. Eur J Nucl Med Mol Imaging. 2022;49(7):2323\u201341.","journal-title":"Eur J Nucl Med Mol Imaging."},{"issue":"4","key":"7915_CR19","first-page":"225","volume":"38","author":"A Dimitrakopoulou-Strauss","year":"2023","unstructured":"Dimitrakopoulou-Strauss A, Sachpekidis C, Hassel JC, Christopoulos P. Positron emission tomography-based immunoimaging for cancer patient stratification: toward a more holistic approach. Cancer Biother Radiopharm. 2023;38(4):225\u201331.","journal-title":"Cancer Biother Radiopharm"},{"key":"7915_CR20","doi-asserted-by":"publisher","first-page":"2301","DOI":"10.1007\/s00259-020-04757-3","volume":"47","author":"RD Seban","year":"2020","unstructured":"Seban RD, et al. Prognostic 18F-FDG PET biomarkers in metastatic mucosal and cutaneous melanoma treated with immune checkpoint inhibitors. Eur J Nucl Med Mol Imaging. 2020;47:2301\u201312.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"7915_CR21","first-page":"735","volume":"33","author":"K Ito","year":"2019","unstructured":"Ito K, et al. Prognostic value of total lesion glycolysis measured by FDG PET\/CT in metastatic melanoma treated with immune checkpoint inhibitors. Ann Nucl Med. 2019;33:735\u201343.","journal-title":"Ann Nucl Med"},{"issue":"3","key":"7915_CR22","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1053\/j.semnuclmed.2025.03.003","volume":"55","author":"A Safarian","year":"2025","unstructured":"Safarian A, Mirshahvalad SA, Farbod A, Nasrollahi H, Pirich C, Beheshti M. Artificial intelligence for tumor [18F]FDG-PET imaging: advancement and future trends-Part I. Semin Nucl Med. 2025;55(3):328\u201344.","journal-title":"Semin Nucl Med"},{"issue":"5","key":"7915_CR23","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1053\/j.semnuclmed.2025.06.012","volume":"55","author":"A Safarian","year":"2025","unstructured":"Safarian A, Mirshahvalad SA, Farbod A, Jung T, Nasrollahi H, Schweighofer-Zwink G, et al. Artificial Intelligence for tumor [18F]FDG PET imaging: advancements and future trends - Part II. Semin Nucl Med. 2025;55(5):856\u201368.","journal-title":"Semin Nucl Med"},{"key":"7915_CR24","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1109\/JBHI.2022.3230060","volume":"27","author":"TP Vagenas","year":"2023","unstructured":"Vagenas TP, et al. A decision support system for the identification of metastases of metastatic melanoma using whole-body FDG PET\/CT images. IEEE J Biomed Health Inform. 2023;27:1397\u2013408.","journal-title":"IEEE J Biomed Health Inform"},{"key":"7915_CR25","doi-asserted-by":"publisher","first-page":"3795","DOI":"10.1109\/TMI.2025.3566996","volume":"44","author":"TP Vagenas","year":"2025","unstructured":"Vagenas TP, et al. Representation learning in PET scans enhanced by semantic and 3D position-specific characteristics. IEEE Trans Med Imaging. 2025;44:3795\u2013808.","journal-title":"IEEE Trans Med Imaging"},{"key":"7915_CR26","unstructured":"Sachpekidis C, Vagenas TP, Hassel JC, Dimitrakopoulou-Strauss A, Matsopoulos GK. (2025) Streamlined Monitoring of Immunotherapy Response in Metastatic Melanoma via Longitudinal PET Quantification and SemiAutomatic Tumor Delineation. EANM Innovation: S1\u2013S1013."},{"issue":"9","key":"7915_CR27","doi-asserted-by":"publisher","first-page":"2699","DOI":"10.1007\/s00259-023-06243-y","volume":"50","author":"C Sachpekidis","year":"2023","unstructured":"Sachpekidis C, Weru V, Kopp-Schneider A, Hassel JC, Dimitrakopoulou-Strauss A. The prognostic value of [18F]FDG PET\/CT based response monitoring in metastatic melanoma patients undergoing immunotherapy: comparison of different metabolic criteria. Eur J Nucl Med Mol Imaging. 2023;50(9):2699\u2013714.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"12","key":"7915_CR28","doi-asserted-by":"publisher","first-page":"3709","DOI":"10.1007\/s00259-023-06327-9","volume":"50","author":"C Sachpekidis","year":"2023","unstructured":"Sachpekidis C, Stein-Thoeringer CK, Kopp-Schneider A, Weru V, Dimitrakopoulou-Strauss A, Hassel JC. Can physiologic colonic [18F]FDG uptake in PET\/CT imaging predict response to immunotherapy in metastatic melanoma? Eur J Nucl Med Mol Imaging. 2023;50(12):3709\u201322.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"7","key":"7915_CR29","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1109\/TMI.2004.828354","volume":"23","author":"SK Warfield","year":"2004","unstructured":"Warfield SK, Zou KH, Wells WM. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging. 2004;23(7):903\u201321.","journal-title":"IEEE Trans Med Imaging"},{"key":"7915_CR30","unstructured":"Bardes A, Ponce J, LeCun Y. 2021 VICReg: Variance-invariance-covariance regularization for self-supervised learning. https:\/\/arxiv.org\/abs\/2105.04906."},{"issue":"5","key":"7915_CR31","doi-asserted-by":"publisher","first-page":"e230024","DOI":"10.1148\/ryai.230024","volume":"5","author":"J Wasserthal","year":"2023","unstructured":"Wasserthal J, Breit HC, Meyer MT, Pradella M, Hinck D, Sauter AW, et al. TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images. Radiol Artif Intell. 2023Jul 5;5(5):e230024.","journal-title":"Radiol Artif Intell"},{"issue":"13","key":"7915_CR32","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1016\/S0959-8049(99)00229-4","volume":"35","author":"H Young","year":"1999","unstructured":"Young H, Baum R, Cremerius U, Herholz K, Hoekstra O, Lammertsma AA, et al. Measurement of clinical and subclinical tumour response using [18F]-fluorodeoxyglucose and positron emission tomography: review and 1999 EORTC recommendations. European Organization for Research and Treatment of Cancer (EORTC) PET Study Group. Eur J Cancer. 1999;35(13):1773\u201382.","journal-title":"Eur J Cancer"},{"issue":"Suppl 1(Suppl 1","key":"7915_CR33","doi-asserted-by":"publisher","first-page":"122S","DOI":"10.2967\/jnumed.108.057307","volume":"50","author":"RL Wahl","year":"2009","unstructured":"Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med. 2009;50(Suppl 1(Suppl 1)):122S-S150. https:\/\/doi.org\/10.2967\/jnumed.108.057307.","journal-title":"J Nucl Med"},{"key":"7915_CR34","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1007\/s00259-017-3870-6","volume":"45","author":"H Anwar","year":"2018","unstructured":"Anwar H, Sachpekidis C, Winkler J, Kopp-Schneider A, Haberkorn U, Hassel JC, et al. Absolute number of new lesions on 18F-FDG PET\/CT is more predictive of clinical response than SUV changes in metastatic melanoma patients receiving ipilimumab. Eur J Nucl Med Mol Imaging. 2018;45:376\u201383.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"7915_CR35","doi-asserted-by":"publisher","first-page":"335","DOI":"10.2967\/jnumed.118.213652","volume":"60","author":"K Ito","year":"2019","unstructured":"Ito K, Teng R, Sch\u00f6der H, Humm JL, Ni A, Michaud L, et al. F-18 FDG PET\/CT for monitoring of ipilimumab therapy in patients with metastatic melanoma. J Nucl Med. 2019;60:335\u201341.","journal-title":"J Nucl Med"},{"key":"7915_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13550-019-0473-1","volume":"9","author":"L Goldfarb","year":"2019","unstructured":"Goldfarb L, Duchemann B, Chouahnia K, Zelek L, Soussan M. Monitoring anti-PD-1-based immunotherapy in non-small cell lung cancer with FDG PET: introduction of iPERCIST. EJNMMI Res. 2019;9:1\u201310.","journal-title":"EJNMMI Res"},{"issue":"16","key":"7915_CR37","doi-asserted-by":"publisher","first-page":"4414","DOI":"10.1158\/1078-0432.CCR-20-0020","volume":"26","author":"L Basler","year":"2020","unstructured":"Basler L, Gabry\u015b HS, Hogan SA, et al. Radiomics, tumor volume, and blood biomarkers for early prediction of pseudoprogression in patients with metastatic melanoma treated with immune checkpoint inhibition. Clin Cancer Res. 2020;26(16):4414\u201325.","journal-title":"Clin Cancer Res"},{"key":"7915_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106902","volume":"221","author":"I Dirks","year":"2022","unstructured":"Dirks I, Keyaerts M, Neyns B, Vandemeulebroucke J. Computer-aided detection and segmentation of malignant melanoma lesions on whole-body 18F-FDG PET\/CT using an interpretable deep learning approach. Comput Methods Programs Biomed. 2022;221:106902.","journal-title":"Comput Methods Programs Biomed"},{"issue":"16","key":"7915_CR39","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15164083","volume":"15","author":"I Dirks","year":"2023","unstructured":"Dirks I, Keyaerts M, Dirven I, Neyns B, Vandemeulebroucke J. Development and validation of a predictive model for metastatic melanoma patients treated with Pembrolizumab based on automated analysis of whole-body [18F]FDG PET\/CT imaging and clinical features. Cancers (Basel). 2023;15(16):4083.","journal-title":"Cancers (Basel)"},{"issue":"2","key":"7915_CR40","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12020388","volume":"12","author":"A Flaus","year":"2022","unstructured":"Flaus A, Habouzit V, de Leiris N, Vuillez JP, Leccia MT, Simonson M, et al. Outcome prediction at patient level derived from pre-treatment 18F-FDG PET due to machine learning in metastatic melanoma treated with anti-PD1 treatment. Diagnostics. 2022;12(2):388.","journal-title":"Diagnostics"},{"issue":"9","key":"7915_CR41","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12092102","volume":"12","author":"T K\u00fcstner","year":"2022","unstructured":"K\u00fcstner T, Vogel J, Hepp T, Forschner A, Pfannenberg C, Schmidt H, et al. Development of a hybrid-imaging-based prognostic index for metastasized-melanoma patients in whole-body 18F-FDG PET\/CT and PET\/MRI data. Diagnostics. 2022;12(9):2102.","journal-title":"Diagnostics"}],"container-title":["European Journal of Nuclear Medicine and Molecular Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00259-026-07915-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00259-026-07915-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00259-026-07915-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T18:16:55Z","timestamp":1785521815000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00259-026-07915-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,26]]},"references-count":41,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["7915"],"URL":"https:\/\/doi.org\/10.1007\/s00259-026-07915-1","relation":{},"ISSN":["1619-7070","1619-7089"],"issn-type":[{"value":"1619-7070","type":"print"},{"value":"1619-7089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,26]]},"assertion":[{"value":"18 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.","order":1,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Informed consent was obtained from all participants enrolled in the study.","order":2,"name":"Ethics","label":"Consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Consent to publish has been received from all participants.","order":3,"name":"Ethics","label":"Consent to publish","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Jessica C. Hassel discloses Speaker: Amgen, Bristol-Myers Squibb, GSK, Immunocore, Merck Sharp and Dohme, Novartis Pharma, Pierre Fabre, Sanofi-Aventis U.S. LLC; Sunpharma; Research grant\/contract: Bristol-Myers Squibb, Sanofi, Sunpharma; Consultant\/Advisory Board: Bristol-Myers Squibb, GSK, Immunocore, Merck Sharp and Dohme, Novartis Pharma, Pierre Fabre Pharmaceuticals Inc., Philogen, Onkowissen, Sanofi-Aventis U.S. LLC, Sun Pharmaceutical Industries Inc. Christos Sachpekidis is an associate editor in this journal. All other authors declare no conflicts of interest.","order":4,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}