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| 001 | 1010196 | ||
| 005 | 20230929112543.0 | ||
| 024 | 7 | _ | |a 10.1007/s11060-023-04367-7 |2 doi |
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| 024 | 7 | _ | |a 10.34734/FZJ-2023-03007 |2 datacite_doi |
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| 100 | 1 | _ | |a Meißner, Anna-Katharina |0 0000-0003-4150-7265 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Radiomics for the non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to non-small cell lung cancer |
| 260 | _ | _ | |a Dordrecht [u.a.] |c 2023 |b Springer Science + Business Media B.V |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 500 | _ | _ | |a „Open access publication funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 491111487“ |
| 520 | _ | _ | |a BackgroundThe expression level of the programmed cell death ligand 1 (PD-L1) appears to be a predictor for response to immunotherapy using checkpoint inhibitors in patients with non-small cell lung cancer (NSCLC). As differences in terms of PD-L1 expression levels in the extracranial primary tumor and the brain metastases may occur, a reliable method for the non-invasive assessment of the intracranial PD-L1 expression is, therefore of clinical value. Here, we evaluated the potential of radiomics for a non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to NSCLC.Patients and methodsFifty-three NSCLC patients with brain metastases from two academic neuro-oncological centers (group 1, n = 36 patients; group 2, n = 17 patients) underwent tumor resection with a subsequent immunohistochemical evaluation of the PD-L1 expression. Brain metastases were manually segmented on preoperative T1-weighted contrast-enhanced MRI. Group 1 was used for model training and validation, group 2 for model testing. After image pre-processing and radiomics feature extraction, a test-retest analysis was performed to identify robust features prior to feature selection. The radiomics model was trained and validated using random stratified cross-validation. Finally, the best-performing radiomics model was applied to the test data. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analyses.ResultsAn intracranial PD-L1 expression (i.e., staining of at least 1% or more of tumor cells) was present in 18 of 36 patients (50%) in group 1, and 7 of 17 patients (41%) in group 2. Univariate analysis identified the contrast-enhancing tumor volume as a significant predictor for PD-L1 expression (area under the ROC curve (AUC), 0.77). A random forest classifier using a four-parameter radiomics signature, including tumor volume, yielded an AUC of 0.83 ± 0.18 in the training data (group 1), and an AUC of 0.84 in the external test data (group 2).ConclusionThe developed radiomics classifiers allows for a non-invasive assessment of the intracranial PD-L1 expression in patients with brain metastases secondary to NSCLC with high accuracy. |
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| 536 | _ | _ | |a DFG project 491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487) |0 G:(GEPRIS)491111487 |c 491111487 |x 1 |
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| 700 | 1 | _ | |a Gutsche, Robin |0 P:(DE-Juel1)181076 |b 1 |u fzj |
| 700 | 1 | _ | |a Galldiks, Norbert |0 P:(DE-Juel1)143792 |b 2 |u fzj |
| 700 | 1 | _ | |a Kocher, Martin |0 P:(DE-Juel1)173675 |b 3 |u fzj |
| 700 | 1 | _ | |a Jünger, Stephanie T. |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Eich, Marie-Lisa |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Nogova, Lucia |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Araceli, Tommaso |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Schmidt, Nils Ole |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Ruge, Maximilian I. |0 P:(DE-HGF)0 |b 9 |
| 700 | 1 | _ | |a Goldbrunner, Roland |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Proescholdt, Martin |0 P:(DE-HGF)0 |b 11 |
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| 773 | _ | _ | |a 10.1007/s11060-023-04367-7 |g Vol. 163, no. 3, p. 597 - 605 |0 PERI:(DE-600)2007293-4 |n 3 |p 597 - 605 |t Journal of neuro-oncology |v 163 |y 2023 |x 0167-594x |
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