Discordant and Converting Receptor Expressions in Brain Metastases from Breast Cancer: MRI-Based Non-Invasive Receptor Status Tracking.
Simple Summary: Discordance and conversion of receptor expressions in metastatic lesions and primary tumors is often observed in patients with brain metastases from breast cancer. Personalized therapy requires continuous monitoring of receptor expressions and dynamic adaptation of applied targeted t...
| Publicado en: | Cancers Vol. 15; no. 11; pp. 2880 - 2897 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
MDPI
Jun2023
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164215159&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164215159 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Jun2023 vid: 15 iid: 11 pid: 97109 pub: MDPI artinfo: ui: 164215159 164215159 164215159 10.3390/cancers15112880 164215159 ppf: 2880 ppct: 17 formats: tig: atl: Discordant and Converting Receptor Expressions in Brain Metastases from Breast Cancer: MRI-Based Non-Invasive Receptor Status Tracking. aug: au: Heitkamp, Alexander Madesta, Frederic Amberg, Sophia Wahaj, Schohla Schröder, Tanja Bechstein, Matthias Meyer, Lukas Broocks, Gabriel Hanning, Uta Gauer, Tobias Werner, René Fiehler, Jens Gellißen, Susanne Kniep, Helge C. affil: Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany sug: subj: Brain Neoplasms Prognosis Neoplasm Metastasis Prognosis Breast Neoplasms Receptors, Cell Surface Metabolism Magnetic Resonance Imaging Noninvasive Procedures Human Machine Learning Histocytochemistry Receptors, Progesterone Metabolism Receptors, Estrogen Metabolism Epidermal Growth Factor Receptors Metabolism Random Forest Algorithms Prediction Models Sensitivity and Specificity ab: Simple Summary: Discordance and conversion of receptor expressions in metastatic lesions and primary tumors is often observed in patients with brain metastases from breast cancer. Personalized therapy requires continuous monitoring of receptor expressions and dynamic adaptation of applied targeted treatment options. This study sought to evaluate if quantitative MR image features can predict the receptor status of brain metastases from breast cancer using machine learning algorithms. Results indicate that receptor status can be differentiated non-invasively based on routine MR imaging data. The proposed approach could allow non-invasive expression tracking at high frequencies, and may support dynamic treatment optimization for personalized therapies. Discordance and conversion of receptor expressions in metastatic lesions and primary tumors is often observed in patients with brain metastases from breast cancer. Therefore, personalized therapy requires continuous monitoring of receptor expressions and dynamic adaptation of applied targeted treatment options. Radiological in vivo techniques may allow receptor status tracking at high frequencies at low risk and cost. The present study aims to investigate the potential of receptor status prediction through machine-learning-based analysis of radiomic MR image features. The analysis is based on 412 brain metastases samples from 106 patients acquired between 09/2007 and 09/2021. Inclusion criteria were as follows: diagnosed cerebral metastases from breast cancer; histopathology reports on progesterone (PR), estrogen (ER), and human epidermal growth factor 2 (HER2) receptor status; and availability of MR imaging data. In total, 3367 quantitative features of T1 contrast-enhanced, T1 non-enhanced, and FLAIR images and corresponding patient age were evaluated utilizing random forest algorithms. Feature importance was assessed using Gini impurity measures. Predictive performance was tested using 10 permuted 5-fold cross-validation sets employing the 30 most important features of each training set. Receiver operating characteristic areas under the curves of the validation sets were 0.82 (95% confidence interval [0.78; 0.85]) for ER+, 0.73 [0.69; 0.77] for PR+, and 0.74 [0.70; 0.78] for HER2+. Observations indicate that MR image features employed in a machine learning classifier could provide high discriminatory accuracy in predicting the receptor status of brain metastases from breast cancer. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|