Should we screen for brain metastases in non‐small cell lung cancer?

Vinod, Shalini

doi:10.1111/1754-9485.12743

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…Considering that these markers are not specific to BM pathology, they cannot replace MRI but can complement it by aiding in prioritizing MRI scans for those at higher risk of BM. The debate surrounding the routine application of brain MRI for detecting BMs in patients with LC is centered on balancing the benefits of early detection and treatment against considerations such as cost, accessibility, and the relatively low incidence of asymptomatic BMs in the early stages of LC [ 29 , 30 ]. Therefore, personalized MRI screening, informed by these blood biomarkers―which are relatively more accessible and cheaper than MRI―can be expected mitigated the downsides of routine MRI evaluations and enhance their overall benefits.…”

Section: Discussionmentioning

confidence: 99%

Blood Neurofilament Light Chain and Glial Fibrillary Acidic Protein as Promising Screening Biomarkers for Brain Metastases in Patients with Lung Cancer

Kim,

Ahn,

Lee

et al. 2024

IJMS

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The diagnosis of brain metastases (BMs) in patients with lung cancer (LC) predominantly relies on magnetic resonance imaging (MRI), a method that is constrained by high costs and limited accessibility. This study explores the potential of serum neurofilament light chain (sNfL) and serum glial fibrillary acidic protein (sGFAP) as screening biomarkers for BMs in LC patients. We conducted a retrospective analysis of 700 LC cases at the National Cancer Center, Korea, from July 2020 to June 2022, measuring sNfL and sGFAP levels at initial LC diagnosis. The likelihood of BM was evaluated using multivariate analysis and a predictive nomogram. Additionally, we prospectively monitored 177 samples from 46 LC patients initially without BM. Patients with BMs (n= 135) had significantly higher median sNfL (52.5 pg/mL) and sGFAP (239.2 pg/mL) levels compared to those without BMs (n = 565), with medians of 17.8 pg/mL and 141.1 pg/mL, respectively (p < 0.001 for both). The nomogram, incorporating age, sNfL, and sGFAP, predicted BM with an area under the curve (AUC) of 0.877 (95% CI 0.84–0.914), showing 74.8% sensitivity and 83.5% specificity. Over nine months, 93% of samples from patients without BM remained below the cutoff, while all patients developing BMs showed increased levels at detection. A nomogram incorporating age, sNfL, and sGFAP provides a valuable tool for identifying LC patients at high risk for BM, thereby enabling targeted MRI screenings and enhancing diagnostic efficiency.

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Section: Discussionmentioning

confidence: 99%

Blood Neurofilament Light Chain and Glial Fibrillary Acidic Protein as Promising Screening Biomarkers for Brain Metastases in Patients with Lung Cancer

Kim,

Ahn,

Lee

et al. 2024

IJMS

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“…However, the cost of screening in an unselected population is considerable and the benefit is questionable, given the conflicting international screening guidelines and clinicians' possible tendency to conduct investigations in excess of the recommended stage (14,(33)(34)(35). In this study, we developed a "two-step" ANN model for predicting synchronous organspecific metastasis in LC patients.…”

Section: Discussionmentioning

confidence: 99%

“…Computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT) and positron emission tomography/computed tomography (PET/CT) are the common techniques to screen the distant metastasis in LC patients. However, routine DM screening to all LC patients is controversial because of low detection rate of asymptomatic patients, invasive operation, potential risk of adverse reactions, complex process and high cost (10)(11)(12)(13)(14). Therefore, there are strong requirements for the identification of a high-risk group with distant metastasis and the rationalization of DM screening in LC patients.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer

Gao

et al. 2022

Front. Oncol.

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BackgroundThis study aimed to develop an artificial neural network (ANN) model for predicting synchronous organ-specific metastasis in lung cancer (LC) patients.MethodsA total of 62,151 patients who diagnosed as LC without data missing between 2010 and 2015 were identified from Surveillance, Epidemiology, and End Results (SEER) program. The ANN model was trained and tested on an 75/25 split of the dataset. The receiver operating characteristic (ROC) curves, area under the curve (AUC) and sensitivity were used to evaluate and compare the ANN model with the random forest model.ResultsFor distant metastasis in the whole cohort, the ANN model had metrics AUC = 0.759, accuracy = 0.669, sensitivity = 0.906, and specificity = 0.613, which was better than the random forest model. For organ-specific metastasis in the cohort with distant metastasis, the sensitivity in bone metastasis, brain metastasis and liver metastasis were 0.913, 0.906 and 0.925, respectively. The most important variable was separate tumor nodules with 100% importance. The second important variable was visceral pleural invasion for distant metastasis, while histology for organ-specific metastasis.ConclusionsOur study developed a “two-step” ANN model for predicting synchronous organ-specific metastasis in LC patients. This ANN model may provide clinicians with more personalized clinical decisions, contribute to rationalize metastasis screening, and reduce the burden on patients and the health care system.

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Should we screen for brain metastases in non‐small cell lung cancer?

Cited by 2 publications

References 15 publications

Blood Neurofilament Light Chain and Glial Fibrillary Acidic Protein as Promising Screening Biomarkers for Brain Metastases in Patients with Lung Cancer

Blood Neurofilament Light Chain and Glial Fibrillary Acidic Protein as Promising Screening Biomarkers for Brain Metastases in Patients with Lung Cancer

Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer

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