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@ARTICLE{Sun:302986,
      author       = {Y. Sun and H. Dong and W. Jin and H. Xuan and Z. Yuan and
                      L. Käsmann$^*$ and L. Shen and T. Wang and X. Ye and M.
                      Zeng},
      title        = {{E}nhancing the intraoperative identification of high-grade
                      patterns in invasive lung adenocarcinoma via radiomics.},
      journal      = {Translational Lung Cancer Research},
      volume       = {14},
      number       = {6},
      issn         = {2218-6751},
      address      = {[Erscheinungsort nicht ermittelbar]},
      publisher    = {[Verlag nicht ermittelbar]},
      reportid     = {DKFZ-2025-01433},
      pages        = {2145 - 2158},
      year         = {2025},
      abstract     = {High-grade patterns (HGPs) are important for surgical
                      decision-making in patients with invasive lung
                      adenocarcinoma (IAC), but the sensitivity of intraoperative
                      frozen section (FS) is not high. Radiomics has the potential
                      to improve the sensitivity of intraoperative detection. The
                      purpose of the present study was to evaluate the value of
                      combining radiomics with FS analysis for predicting HGPs in
                      patients with clinical T1 (cT1) IAC.Data from a total of 490
                      patients who were surgically diagnosed with IAC from January
                      2019 to April 2019 were retrospectively analyzed; the
                      patients were randomly divided into a training set (n=392)
                      and a test set (n=98). The presence of HGPs (micropapillary,
                      solid, and complex glandular patterns) was evaluated
                      according to the final pathology (FP). Radiomics features
                      were extracted from thin-slice computed tomography (CT)
                      images, and feature selection was performed via the mutual
                      information method and least absolute shrinkage and
                      selection operator regression algorithm. The radiomics (R),
                      FS, and radiomics-frozen section (R-FS) models were
                      established to predict the presence of HGPs in FP. The area
                      under the receiver operating characteristic (ROC) curve, the
                      precision-recall curve, the calibration curve, and decision
                      curve analysis were used to evaluate model performances. The
                      permutation importance algorithm (PIA) and local
                      interpretable model-agnostic explanations (LIME) were used
                      to provide interpretations for the R model. Additionally,
                      the predictive performance was compared among tumors with
                      different CT densities.The R and R-FS models outperformed
                      the FS model, with the R-FS model achieving the best area
                      under the curve value of 0.907 $(95\%$ confidence interval:
                      0.830-0.956) in the test set. PIA and LIME determined the
                      interpretability of outputs from both the overall model and
                      individual sample perspectives. Among the three models, the
                      R model performed best in pure ground-glass nodules and
                      pure-solid tumors.Radiomics could function as a
                      complementary check to FS to provide a more sensible and
                      accurate intraoperative identification of HGPs as compared
                      to the use of FS alone, thus better informing clinical
                      decision-making.},
      keywords     = {Radiomics (Other) / frozen section (FS) (Other) /
                      high-grade pattern (HGP) (Other) / lung adenocarcinoma
                      (LUAD) (Other) / machine learning (Other) / non-high-grade
                      pattern (non-HGP) (Other)},
      cin          = {MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40673081},
      pmc          = {pmc:PMC12261232},
      doi          = {10.21037/tlcr-2025-504},
      url          = {https://inrepo02.dkfz.de/record/302986},
}