TY  - JOUR
AU  - Visonà, Giovanni
AU  - Spiller, Lisa M
AU  - Hahn, Sophia
AU  - Hattingen, Elke
AU  - Vogl, Thomas J
AU  - Schweikert, Gabriele
AU  - Bankov, Katrin
AU  - Demes, Melanie
AU  - Reis, Henning
AU  - Wild, Peter
AU  - Zeiner, Pia S
AU  - Acker, Fabian
AU  - Sebastian, Martin
AU  - Wenger, Katharina J
TI  - Machine-Learning-Aided Prediction of Brain Metastases Development in Non-Small-Cell Lung Cancers.
JO  - Clinical lung cancer
VL  - 24
IS  - 8
SN  - 1525-7304
CY  - Dallas, Tex.
PB  - Cancer Information Group
M1  - DKFZ-2023-01847
SP  - e311-e322
PY  - 2023
N1  - 2023 Dec;24(8):e311-e322
AB  - Non-small-cell lung cancer (NSCLC) shows a high incidence of brain metastases (BM). Early detection is crucial to improve clinical prospects. We trained and validated classifier models to identify patients with a high risk of developing BM, as they could potentially benefit from surveillance brain MRI.Consecutive patients with an initial diagnosis of NSCLC from January 2011 to April 2019 and an in-house chest-CT scan (staging) were retrospectively recruited at a German lung cancer center. Brain imaging was performed at initial diagnosis and in case of neurological symptoms (follow-up). Subjects lost to follow-up or still alive without BM at the data cut-off point (12/2020) were excluded. Covariates included clinical and/or 3D-radiomics-features of the primary tumor from staging chest-CT. Four machine learning models for prediction (80/20 training) were compared. Gini Importance and SHAP were used as measures of importance; sensitivity, specificity, area under the precision-recall curve, and Matthew's Correlation Coefficient as evaluation metrics.Three hundred and ninety-five patients compromised the clinical cohort. Predictive models based on clinical features offered the best performance (tuned to maximize recall: sensitivity∼70
KW  - Interpretable machine learning (Other)
KW  - NSCLC (Other)
KW  - Predictive models (Other)
KW  - Radiomics (Other)
KW  - Secondary brain cancer (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:37689579
DO  - DOI:10.1016/j.cllc.2023.08.002
UR  - https://inrepo02.dkfz.de/record/282721
ER  -