Advances in the management of brain metastases from cancer of unknown primary

Machine Learning Applications for Differentiation of Glioma from Brain Metastasis-A Systematic Review

Simple Summary

We present a systematic review of published reports on machine learning (ML) applications for the differentiation of gliomas from brain metastases by summarizing study characteristics, strengths, and pitfalls. Based on these findings, we present recommendations for future research in this field.

Abstract

Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imaging (MRI) due to the similarity of imaging features in specific clinical circumstances. Multiple studies have investigated the use of machine learning (ML) models for non-invasive differentiation of glioma from brain metastasis. Many of the studies report promising classification results, however, to date, none have been implemented into clinical practice. After a screening of 12,470 studies, we included 29 eligible studies in our systematic review. From each study, we aggregated data on model design, development, and best classifiers, as well as quality of reporting according to the TRIPOD statement. In a subset of eligible studies, we conducted a meta-analysis of the reported AUC. It was found that data predominantly originated from single-center institutions (n = 25/29) and only two studies performed external validation. The median TRIPOD adherence was 0.48, indicating insufficient quality of reporting among surveyed studies. Our findings illustrate that despite promising classification results, reliable model assessment is limited by poor reporting of study design and lack of algorithm validation and generalizability. Therefore, adherence to quality guidelines and validation on outside datasets is critical for the clinical translation of ML for the differentiation of glioma and brain metastasis.

Brain Metastasis from Unknown Primary Tumour: Moving from Old Retrospective Studies to Clinical Trials on Targeted Agents

Simple Summary

Brain metastases (BMs) are the most common intracranial tumours in adults and occur up to 3–10 times more frequently than primary brain tumours. In up to 15% of patients with BM, the primary tumour cannot be identified. These cases are known as BM of cancer of unknown primary (CUP) (BM-CUP). The understanding of BM-CUP, despite its relative frequency and unfavourable outcome, is still incomplete and clear indications on management are missing. The aim of this review is to summarize current evidence on the diagnosis and treatment of BM-CUP.

Abstract

Brain metastases (BMs) are the most common intracranial tumours in adults and occur up to 3–10 times more frequently than primary brain tumours. BMs may be the cause of the neurological presenting symptoms in patients with otherwise previously undiagnosed cancer. In up to 15% of patients with BMs, the primary tumour cannot be identified. These cases are known as BM of cancer of unknown primary (CUP) (BM-CUP). CUP has an early and aggressive metastatic spread, poor response to chemotherapy, and poor prognosis. The pathogenesis of CUP seems to be characterized by a specific underlying pro-metastatic signature. The understanding of BM-CUP, despite its relative frequency and unfavourable outcome, is still incomplete and clear indications on management are missing. Advances in diagnostic tools, molecular characterization, and target therapy have shifted the paradigm in the approach to metastasis from CUP: while earlier studies stressed the importance of finding the primary tumour and deciding on treatment based on the primary diagnosis, most recent studies focus on the importance of identifying targetable molecular markers in the metastasis itself. The aim of this review is to summarize current evidence on BM-CUP, from the diagnosis and pathogenesis to the treatment, with a focus on available studies and ongoing clinical trials.