% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Salome:267557,
      author       = {P. Salome$^*$ and F. Sforazzini$^*$ and G. Grugnara and A.
                      Kudak$^*$ and M. Dostal$^*$ and C. Herold-Mende and S.
                      Heiland and J. Debus$^*$ and A. Abdollahi$^*$ and M.
                      Knoll$^*$},
      title        = {{MR} {I}ntensity {N}ormalization {M}ethods {I}mpact
                      {S}equence {S}pecific {R}adiomics {P}rognostic {M}odel
                      {P}erformance in {P}rimary and {R}ecurrent {H}igh-{G}rade
                      {G}lioma.},
      journal      = {Cancers},
      volume       = {15},
      number       = {3},
      issn         = {2072-6694},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {DKFZ-2023-00335},
      pages        = {965},
      year         = {2023},
      note         = {#EA:E210#LA:E210#},
      abstract     = {This study investigates the impact of different intensity
                      normalization (IN) methods on the overall survival (OS)
                      radiomics models' performance of MR sequences in primary
                      (pHGG) and recurrent high-grade glioma (rHGG).MR scans
                      acquired before radiotherapy were retrieved from two
                      independent cohorts (rHGG C1: 197, pHGG C2: 141) from
                      multiple scanners (15, 14). The sequences are T1 weighted
                      (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR.
                      Sequence-specific significant features (SF) associated with
                      OS, extracted from the tumour volume, were derived after
                      applying 15 different IN methods. Survival analyses were
                      conducted using Cox proportional hazard (CPH) and Poisson
                      regression (POI) models. A ranking score was assigned based
                      on the 10-fold cross-validated (CV) concordance index (C-I),
                      mean square error (MSE), and the Akaike information
                      criterion (AICs), to evaluate the methods'
                      performance.Scatter plots of the 10-CV C-I and MSE against
                      the AIC showed an impact on the survival predictions between
                      the IN methods and MR sequences (C1/C2 C-I range:
                      0.62-0.71/0.61-0.72, MSE range: 0.20-0.42/0.13-0.22). White
                      stripe showed stable results for T1wce (C1/C2 C-I:
                      0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15)
                      and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed
                      consistent prediction results for T2w models. They were also
                      the top-performing methods for T1w in C2 (Combat: 0.67,
                      0.13; HM: 0.67, 0.13); however, only HM achieved high
                      predictions in C1 (0.66, 0.22). After eliminating IN
                      impacted SF using Spearman's rank-order correlation
                      coefficient, a mean decrease in the C-I and MSE of 0.05 and
                      0.03 was observed in all four sequences.The IN method
                      impacted the predictive power of survival models; thus,
                      performance is sequence-dependent.},
      keywords     = {high-grade glioma (Other) / image preprocessing (Other) /
                      intensity harmonization (Other) / intensity standardization
                      (Other) / multiparametric MRI (Other) / radiomics signatures
                      (Other)},
      cin          = {E210 / E050 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)E210-20160331 / I:(DE-He78)E050-20160331 /
                      I:(DE-He78)HD01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36765922},
      doi          = {10.3390/cancers15030965},
      url          = {https://inrepo02.dkfz.de/record/267557},
}