ArticleNeuroradiology2023
MR imaging profile and histopathological characteristics of tumour vasculature, cell density and proliferation rate define two distinct growth patterns of human brain metastases from lung cancer.
Article in Neuroradiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects.Journal of medical radiation sciences · 2024Pooled it
- Habitat Model Based on Lung CT for Predicting Brain Metastasis in Patients with Non-Small Cell Lung Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Magnetic resonance imaging characteristics of small cell and non-small cell lung cancer brain metastases: a retrospective study.Journal of medicine and life · 2025Article
- Exploring the Role of Peritumoral Edema in Predicting Lung Cancer Subtypes Through T2-FLAIR Digital Subtraction Imaging of Metastatic Brain Tumors.Diagnostics (Basel, Switzerland) · 2025Article
- Visualizing the vascular-cellular microenvironment in lung cancer brain metastasis via multiparametric fusion of DCE-MRI and DWI.Frontiers in oncology · 2025Article
- Evaluation of brain metastasis edema in breast cancer patients as a marker for Ki-67 and cell count-A single center analysis.The neuroradiology journal · 2024Article
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14 authors.
Funding
Abstract
purposeNon-invasive prediction of the tumour of origin giving rise to brain metastases (BMs) using MRI measurements obtained in radiological routine and elucidating the biological basis by matched histopathological analysis.
methodsPreoperative MRI and histological parameters of 95 BM patients (female, 50; mean age 59.6 ± 11.5 years) suffering from different primary tumours were retrospectively analysed. MR features were assessed by region of interest (ROI) measurements of signal intensities on unenhanced T1-, T2-, diffusion-weighted imaging and apparent diffusion coefficient (ADC) normalised to an internal reference ROI. Furthermore, we assessed BM size and oedema as well as cell density, proliferation rate, microvessel density and vessel area as histopathological parameters.
resultsApplying recursive partitioning conditional inference trees, only histopathological parameters could stratify the primary tumour entities. We identified two distinct BM growth patterns depending on their proliferative status: Ki67
conclusionInterpretable and easy to obtain MRI features may not be sufficient to predict directly the primary tumour entity of BM but seem to have the potential to aid differentiating high- and low-proliferative BMs, such as SCLC and NSCLC.
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