001     291566
005     20240712180732.0
024 7 _ |a 10.1200/PO.23.00613
|2 doi
024 7 _ |a pmid:38986047
|2 pmid
037 _ _ |a DKFZ-2024-01451
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Hummel, Manuela
|0 P:(DE-He78)fae4f3c76bbbd2fc21dd920b46945d42
|b 0
|e First author
245 _ _ |a Quantitative Integrative Survival Prediction in Multiple Myeloma Patients Treated With Bortezomib-Based Induction, High-Dose Therapy and Autologous Stem Cell Transplantation.
260 _ _ |a Alexandria, VA
|c 2024
|b American Society of Clinical Oncology
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 1720778213_17228
|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:C060#
520 _ _ |a Given the high heterogeneity in survival for patients with multiple myeloma, it would be clinically useful to quantitatively predict the individual survival instead of attributing patients to two to four risk groups as in current models, for example, revised International Staging System (R-ISS), R2-ISS, or Mayo-2022-score.Our aim was to develop a quantitative prediction tool for individual patient's 3-/5-year overall survival (OS) probability. We integrated established clinical and molecular risk factors into a comprehensive prognostic model and evaluated and validated its risk discrimination capabilities versus R-ISS, R2-ISS, and Mayo-2022-score.A nomogram for estimating OS probabilities was built on the basis of a Cox regression model. It allows one to translate the individual risk profile of a patient into 3-/5-year OS probabilities by attributing points to each prognostic factor and summing up all points. The nomogram was externally validated regarding discrimination and calibration. There was no obvious bias or overfitting of the prognostic index on the validation cohort. Resampling-based and external evaluation showed good calibration. The c-index of the model was similar on the training (0.76) and validation cohort (0.75) and significantly higher than for the R-ISS (P < .001) or R2-ISS (P < .01).In summary, we developed and validated individual quantitative nomogram-based OS prediction. Continuous risk assessment integrating molecular prognostic factors is superior to R-ISS, R2-ISS, or Mayo-2022-score alone.
536 _ _ |a 313 - Krebsrisikofaktoren und Prävention (POF4-313)
|0 G:(DE-HGF)POF4-313
|c POF4-313
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
650 _ 7 |a Bortezomib
|0 69G8BD63PP
|2 NLM Chemicals
650 _ 7 |a Antineoplastic Agents
|2 NLM Chemicals
650 _ 2 |a Multiple Myeloma: mortality
|2 MeSH
650 _ 2 |a Multiple Myeloma: therapy
|2 MeSH
650 _ 2 |a Multiple Myeloma: drug therapy
|2 MeSH
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Bortezomib: therapeutic use
|2 MeSH
650 _ 2 |a Male
|2 MeSH
650 _ 2 |a Female
|2 MeSH
650 _ 2 |a Middle Aged
|2 MeSH
650 _ 2 |a Transplantation, Autologous
|2 MeSH
650 _ 2 |a Nomograms
|2 MeSH
650 _ 2 |a Aged
|2 MeSH
650 _ 2 |a Prognosis
|2 MeSH
650 _ 2 |a Hematopoietic Stem Cell Transplantation
|2 MeSH
650 _ 2 |a Antineoplastic Agents: therapeutic use
|2 MeSH
650 _ 2 |a Induction Chemotherapy
|2 MeSH
650 _ 2 |a Adult
|2 MeSH
650 _ 2 |a Survival Rate
|2 MeSH
700 1 _ |a Hielscher, Thomas
|0 P:(DE-He78)743a4a82daab55306a2c88b9f6bf8c2f
|b 1
|u dkfz
700 1 _ |a Emde-Rajaratnam, Martina
|b 2
700 1 _ |a Salwender, Hans
|0 0000-0001-7803-0814
|b 3
700 1 _ |a Beck, Susanne
|b 4
700 1 _ |a Scheid, Christof
|0 0009-0007-6539-226X
|b 5
700 1 _ |a Bertsch, Uta
|b 6
700 1 _ |a Goldschmidt, Hartmut
|b 7
700 1 _ |a Jauch, Anna
|b 8
700 1 _ |a Moreaux, Jérôme
|0 0000-0002-5717-3207
|b 9
700 1 _ |a Seckinger, Anja
|b 10
700 1 _ |a Hose, Dirk
|0 0000-0003-0806-5223
|b 11
773 _ _ |a 10.1200/PO.23.00613
|g Vol. 8, no. 8, p. e2300613
|0 PERI:(DE-600)2964799-X
|n 8
|p e2300613
|t JCO precision oncology
|v 8
|y 2024
|x 2473-4284
909 C O |o oai:inrepo02.dkfz.de:291566
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 0
|6 P:(DE-He78)fae4f3c76bbbd2fc21dd920b46945d42
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 1
|6 P:(DE-He78)743a4a82daab55306a2c88b9f6bf8c2f
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-313
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Krebsrisikofaktoren und Prävention
|x 0
914 1 _ |y 2024
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b JCO PRECIS ONCOL : 2022
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2023-10-27
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2023-10-27
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2023-10-27
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2023-10-27
920 1 _ |0 I:(DE-He78)C060-20160331
|k C060
|l C060 Biostatistik
|x 0
920 0 _ |0 I:(DE-He78)C060-20160331
|k C060
|l C060 Biostatistik
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)C060-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21