Optimal PET-based radiomic signature construction based on the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma.
Purpose: To develop and externally validate models incorporating a PET radiomics signature (R-signature) obtained by the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma (DLBCL). Methods: A total of 383 patients with DLBCL from two medical centres b...
| Published in: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 49; no. 8; pp. 2902 - 2917 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Jul2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157529117&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157529117 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Jul2022 vid: 49 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 157529117 155168412 10.1007/s00259-022-05717-9 157529117 ppf: 2902 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Optimal PET-based radiomic signature construction based on the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma. aug: au: Jiang, Chong Li, Ang Teng, Yue Huang, Xiangjun Ding, Chongyang Chen, Jianxin Xu, Jingyan Zhou, Zhengyang affil: Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 210000, Nanjing, China sug: ab: Purpose: To develop and externally validate models incorporating a PET radiomics signature (R-signature) obtained by the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma (DLBCL). Methods: A total of 383 patients with DLBCL from two medical centres between 2011 and 2019 were included. The cross-combination method was used on three types of PET radiomics features from the training cohort to generate 49 feature selection-classification candidates based on 7 different machine learning models. The R-signature was then built by selecting the optimal candidates based on their progression-free survival (PFS) and overall survival (OS). Cox regression analysis was used to develop the survival prediction models. The calibration, discrimination, and clinical utility of the models were assessed and externally validated. Results: The R-signatures determined by 12 and 31 radiomics features were significantly associated with PFS and OS, respectively (P<0.05). The combined models that incorporated R-signatures, metabolic metrics, and clinical risk factors exhibited significant prognostic superiority over the clinical models, PET-based models, and the National Comprehensive Cancer Network International Prognostic Index in terms of both PFS (C-index: 0.801 vs. 0.732 vs. 0.785 vs. 0.720, respectively) and OS (C-index: 0.807 vs. 0.740 vs. 0.773 vs. 0.726, respectively). For external validation, the C-indices were 0.758 vs. 0.621 vs. 0.732 vs. 0.673 and 0.794 vs. 0.696 vs. 0.781 vs. 0.708 in the PFS and OS analyses, respectively. The calibration curves showed good consistency, and the decision curve analysis supported the clinical utility of the combined model. Conclusion: The R-signature could be used as a survival predictor for DLBCL, and its combination with clinical factors may allow for accurate risk stratification. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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