Estimating the risk of brain metastasis for patients newly diagnosed with cancer.

ABSTRACT

BACKGROUND: Brain metastases (BM) affect clinical management and prognosis but limited resources exist to estimate BM risk in newly diagnosed cancer patients. Additionally, guidelines for brain MRI screening are limited. We aimed to develop and validate models to predict risk of BM at diagnosis for the most common cancer types that spread to the brain. METHODS: Breast cancer, melanoma, kidney cancer, colorectal cancer (CRC), small cell lung cancer (SCLC), and non-small cell lung cancer (NSCLC) data were extracted from the National Cancer Database to evaluate for the variables associated with the presence of BM at diagnosis. Multivariable logistic regression (LR) models were developed and performance was evaluated with Area Under the Receiver Operating Characteristic Curve (AUC) and random-split training and testing datasets. Nomograms and a Webtool were created for each cancer type. RESULTS: We identify 4,828,305 patients from 2010-2018 (2,095,339 breast cancer, 472,611 melanoma, 407,627 kidney cancer, 627,090 CRC, 164,864 SCLC, and 1,060,774 NSCLC). The proportion of patients with BM at diagnosis is 0.3%, 1.5%, 1.3%, 0.3%, 16.0%, and 10.3% for breast cancer, melanoma, kidney cancer, CRC, SCLC, and NSCLC, respectively. The average AUC over 100 random splitting for the LR models is 0.9534 for breast cancer, 0.9420 for melanoma, 0.8785 for CRC, 0.9054 for kidney cancer, 0.7759 for NSCLC, and 0.6180 for SCLC. CONCLUSIONS: We develop accurate models that predict the BM risk at diagnosis for multiple cancer types. The nomograms and Webtool may aid clinicians in considering brain MRI at the time of initial cancer diagnosis.

When patients are diagnosed with cancer, it is unknown which patients have a significant risk of cancer spread to the brain. Cancer spread to the brain is important to diagnose since it changes how patients are treated and affects their prognosis. This study used a large national database of patients diagnosed with cancer and studied the characteristics that were associated with cancer spread to the brain. The results can be used by doctors to assess the risk of cancer spread to the brain and determine which patients with cancer may benefit most from brain imaging.