{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T15:09:44Z","timestamp":1790867384731,"version":"4.1.0"},"reference-count":54,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"25","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO"],"published-print":{"date-parts":[[2024,9]]},"abstract":"\n PURPOSE<\/jats:title>\n \n Artificial intelligence can reduce the time used by physicians on radiological assessments. For\n 18<\/jats:sup>\n F-fluorodeoxyglucose\u2013avid lymphomas, obtaining complete metabolic response (CMR) by end of treatment is prognostic.\n <\/jats:p>\n <\/jats:sec>\n \n METHODS<\/jats:title>\n \n Here, we present a deep learning\u2013based algorithm for fully automated treatment response assessments according to the Lugano 2014 classification. The proposed four-stage method, trained on a multicountry clinical trial (ClinicalTrials.gov identifier:\n NCT01287741<\/jats:ext-link>\n ) and tested in three independent multicenter and multicountry test sets on different non-Hodgkin lymphoma subtypes and different lines of treatment (ClinicalTrials.gov identifiers\n NCT02257567<\/jats:ext-link>\n ,\n NCT02500407<\/jats:ext-link>\n ; 20% holdout in ClinicalTrials.gov identifier\n NCT01287741<\/jats:ext-link>\n ), outputs the detected lesions at baseline and follow-up to enable focused radiologist review.\n <\/jats:p>\n <\/jats:sec>\n \n RESULTS<\/jats:title>\n \n The method's response assessment achieved high agreement with the adjudicated radiologic responses (eg, agreement for overall response assessment of 93%, 87%, and 85% in ClinicalTrials.gov identifiers\n NCT01287741<\/jats:ext-link>\n ,\n NCT02500407<\/jats:ext-link>\n , and\n NCT02257567<\/jats:ext-link>\n , respectively) similar to inter-radiologist agreement and was strongly prognostic of outcomes with a trend toward higher accuracy for death risk than adjudicated radiologic responses (hazard ratio for end of treatment by-model CMR of 0.123, 0.054, and 0.205 in ClinicalTrials.gov identifiers\n NCT01287741<\/jats:ext-link>\n ,\n NCT02500407<\/jats:ext-link>\n , and\n NCT02257567<\/jats:ext-link>\n , compared with, respectively, 0.226, 0.292, and 0.272 for CMR by the adjudicated responses). Furthermore, a radiologist review of the algorithm's assessments was conducted. The radiologist median review time was 1.38 minutes\/assessment, and no statistically significant differences were observed in the level of agreement of the radiologist with the model's response compared with the level of agreement of the radiologist with the adjudicated responses.\n <\/jats:p>\n <\/jats:sec>\n \n CONCLUSION<\/jats:title>\n These results suggest that the proposed method can be incorporated into radiologic response assessment workflows in cancer imaging for significant time savings and with performance similar to trained medical experts.<\/jats:p>\n <\/jats:sec>","DOI":"10.1200\/jco.23.01978","type":"journal-article","created":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T20:01:18Z","timestamp":1717704078000},"page":"2966-2977","update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":19,"title":["Automated Lugano Metabolic Response Assessment in\n 18<\/sup>\n F-Fluorodeoxyglucose\u2013Avid Non-Hodgkin Lymphoma With Deep Learning on\n 18<\/sup>\n F-Fluorodeoxyglucose\u2013Positron Emission Tomography"],"prefix":"10.1200","volume":"42","clinical-trial-number":[{"clinical-trial-number":"nct02257567","registry":"10.18810\/clinical-trials-gov"},{"clinical-trial-number":"nct01287741","registry":"10.18810\/clinical-trials-gov"},{"clinical-trial-number":"nct02500407","registry":"10.18810\/clinical-trials-gov"}],"author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3627-7670","authenticated-orcid":false,"given":"Skander","family":"Jemaa","sequence":"first","affiliation":[{"name":"Genentech, Inc, South San Francisco, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Souhila","family":"Ounadjela","sequence":"additional","affiliation":[{"name":"Genentech, Inc, South San Francisco, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyong","family":"Wang","sequence":"additional","affiliation":[{"name":"Genentech, Inc, South San Francisco, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarec C.","family":"El-Galaly","sequence":"additional","affiliation":[{"name":"Department of Hematology, Aalborg University Hospital, Aalborg, Denmark"},{"name":"Hematology Research Unit, Department of Hematology, Odense University Hospital, Odense, Denmark"},{"name":"Department of Clinical Research, University of Southern Denmark, Odense, Denmark"},{"name":"Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5478-8161","authenticated-orcid":false,"given":"Lale","family":"Kostakoglu","sequence":"additional","affiliation":[{"name":"Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Knapp","sequence":"additional","affiliation":[{"name":"F Hoffmann-La Roche, Basel, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7808-1769","authenticated-orcid":false,"given":"Grace","family":"Ku","sequence":"additional","affiliation":[{"name":"Genentech, Inc, South San Francisco, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa","family":"Musick","sequence":"additional","affiliation":[{"name":"Genentech, Inc, South San Francisco, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Denis","family":"Sahin","sequence":"additional","affiliation":[{"name":"F Hoffmann-La Roche, Basel, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael C.","family":"Wei","sequence":"additional","affiliation":[{"name":"Genentech, Inc, South San Francisco, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shen","family":"Yin","sequence":"additional","affiliation":[{"name":"Genentech, Inc, South San Francisco, 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