This title appears in the Scientific Report :
2023
Please use the identifier:
http://dx.doi.org/10.1007/s11060-023-04367-7 in citations.
Please use the identifier: http://dx.doi.org/10.34734/FZJ-2023-03007 in citations.
Radiomics for the non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to non-small cell lung cancer
Radiomics for the non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to non-small cell lung cancer
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 a...
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Personal Name(s): | Meißner, Anna-Katharina (Corresponding author) |
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Gutsche, Robin / Galldiks, Norbert / Kocher, Martin / Jünger, Stephanie T. / Eich, Marie-Lisa / Nogova, Lucia / Araceli, Tommaso / Schmidt, Nils Ole / Ruge, Maximilian I. / Goldbrunner, Roland / Proescholdt, Martin / Grau, Stefan / Lohmann, Philipp | |
Contributing Institute: |
Physik der Medizinischen Bildgebung; INM-4 Kognitive Neurowissenschaften; INM-3 |
Published in: | Journal of neuro-oncology, 163 (2023) 3, S. 597 - 605 |
Imprint: |
Dordrecht [u.a.]
Springer Science + Business Media B.V
2023
|
DOI: |
10.1007/s11060-023-04367-7 |
DOI: |
10.34734/FZJ-2023-03007 |
Document Type: |
Journal Article |
Research Program: |
Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) Brain Dysfunction and Plasticity |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.34734/FZJ-2023-03007 in citations.
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. |