guest ::
| Home > Publications database > Can 18F-NaF PET/CT before Autologous Stem Cell Transplantation Predict Survival in Multiple Myeloma? > print |
| Home > Publications database > Can 18F-NaF PET/CT before Autologous Stem Cell Transplantation Predict Survival in Multiple Myeloma? > print |
| 001 | 156826 | ||
| 005 | 20240229123117.0 | ||
| 024 | 7 | _ | |a 10.3390/cancers12051335 |2 doi |
| 024 | 7 | _ | |a pmid:32456181 |2 pmid |
| 024 | 7 | _ | |a pmc:PMC7281312 |2 pmc |
| 024 | 7 | _ | |a altmetric:82930281 |2 altmetric |
| 037 | _ | _ | |a DKFZ-2020-01143 |
| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Sachpekidis, Christos |0 P:(DE-He78)69d2d5247c019c2a2075502dc11bf0b2 |b 0 |e First author |u dkfz |
| 245 | _ | _ | |a Can 18F-NaF PET/CT before Autologous Stem Cell Transplantation Predict Survival in Multiple Myeloma? |
| 260 | _ | _ | |a Basel |c 2020 |b MDPI |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1600768782_15869 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a #EA:E060#LA:E060# |
| 520 | _ | _ | |a There is an unmet need for positron emission tomography (PET) radiotracers that can image bone disease in multiple myeloma (MM) in a more sensitive and specific way than the widely used 18F-fluorodeoxyglucose (18F-FDG). Sodium fluoride (18F-NaF) is a highly sensitive tracer of bone reconstruction, evolving as an important imaging agent for the assessment of malignant bone diseases. We attempted to investigate for the first time the prognostic significance of 18F-NaF PET/CT in newly diagnosed, symptomatic MM patients planned for autologous stem cell transplantation (ASCT). Forty-seven patients underwent dynamic and static PET/CT with 18F-NaF before treatment. After correlation with the respective findings on CT and 18F-FDG PET/CT that served as reference, the 18F-NaF PET findings were compared with established factors of high-risk disease, like cytogenetic abnormalities as well as bone marrow plasma cell infiltration rate. Furthermore, the impact of 18F-NaF PET/CT on progression-free survival (PFS) was analyzed. Correlation analysis revealed a moderate, significant correlation of the 18F-NaF parameters SUVaverage and K1 in reference tissue with bone marrow plasma cell infiltration rate. However, no significant correlation was observed regarding all other 18F-NaF PET parameters. Survival analysis revealed that patients with a pathologic 18F-NaF PET/CT have a shorter PFS (median = 36.2 months) than those with a physiologic scan (median = 55.6 months) (p = 0.02). Nevertheless, no quantitative 18F-NaF parameter could be shown to adversely affect PFS. In contrast, the respective analysis for quantitative dynamic 18F-FDG PET/CT revealed that the parameters SUVmax, fractional blood volume (VB), k3 and influx from reference tissue as well as SUVaverage from MM lesions had a significant negative impact on patient survival. The herein presented findings highlight the rather limited role of 18F-NaF PET/CT as a single PET approach in MM. |
| 536 | _ | _ | |a 315 - Imaging and radiooncology (POF3-315) |0 G:(DE-HGF)POF3-315 |c POF3-315 |f POF III |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, |
| 700 | 1 | _ | |a Kopp-Schneider, Annette |0 P:(DE-He78)bb6a7a70f976eb8df1769944bf913596 |b 1 |u dkfz |
| 700 | 1 | _ | |a Merz, Maximilian |b 2 |
| 700 | 1 | _ | |a Jauch, Anna |b 3 |
| 700 | 1 | _ | |a Raab, Marc-Steffen |b 4 |
| 700 | 1 | _ | |a Goldschmidt, Hartmut |b 5 |
| 700 | 1 | _ | |a Dimitrakopoulou-Strauss, Antonia |0 P:(DE-He78)b2df3652dfa3e19d5e96dfc53f44a992 |b 6 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.3390/cancers12051335 |g Vol. 12, no. 5, p. 1335 - |0 PERI:(DE-600)2527080-1 |n 5 |p 1335 |t Cancers |v 12 |y 2020 |x 2072-6694 |
| 909 | C | O | |p VDB |o oai:inrepo02.dkfz.de:156826 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 0 |6 P:(DE-He78)69d2d5247c019c2a2075502dc11bf0b2 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 1 |6 P:(DE-He78)bb6a7a70f976eb8df1769944bf913596 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 6 |6 P:(DE-He78)b2df3652dfa3e19d5e96dfc53f44a992 |
| 913 | 1 | _ | |a DE-HGF |l Krebsforschung |1 G:(DE-HGF)POF3-310 |0 G:(DE-HGF)POF3-315 |2 G:(DE-HGF)POF3-300 |v Imaging and radiooncology |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |b Gesundheit |
| 914 | 1 | _ | |y 2020 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b CANCERS : 2018 |d 2019-12-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2019-12-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2019-12-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0310 |2 StatID |b NCBI Molecular Biology Database |d 2019-12-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0320 |2 StatID |b PubMed Central |d 2019-12-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2019-12-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2019-12-21 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Blind peer review |d 2019-12-21 |
| 915 | _ | _ | |a Creative Commons Attribution CC BY (No Version) |0 LIC:(DE-HGF)CCBYNV |2 V:(DE-HGF) |b DOAJ |d 2019-12-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2019-12-21 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2019-12-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2019-12-21 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0111 |2 StatID |b Science Citation Index Expanded |d 2019-12-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2019-12-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2019-12-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2019-12-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2019-12-21 |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b CANCERS : 2018 |d 2019-12-21 |
| 915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |f 2019-12-21 |
| 915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2019-12-21 |
| 920 | 2 | _ | |0 I:(DE-He78)E060-20160331 |k E060 |l E060 KKE Nuklearmedizin |x 0 |
| 920 | 0 | _ | |0 I:(DE-He78)E060-20160331 |k E060 |l E060 KKE Nuklearmedizin |x 0 |
| 920 | 1 | _ | |0 I:(DE-He78)E060-20160331 |k E060 |l E060 KKE Nuklearmedizin |x 0 |
| 920 | 1 | _ | |0 I:(DE-He78)C060-20160331 |k C060 |l C060 Biostatistik |x 1 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-He78)E060-20160331 |
| 980 | _ | _ | |a I:(DE-He78)C060-20160331 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|