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

被引:2
|
作者
Miccio, Joseph A. [1 ]
Tian, Zizhong [2 ]
Mahase, Sean S. [1 ]
Lin, Christine [1 ,3 ]
Choi, Serah [3 ]
Zacharia, Brad E. [4 ]
Sheehan, Jason P. [5 ]
Brown, Paul D. [6 ]
Trifiletti, Daniel M. [7 ]
Palmer, Joshua D. [8 ]
Wang, Ming [9 ]
Zaorsky, Nicholas G. [3 ]
机构
[1] Penn State Canc Inst, Dept Radiat Oncol, Hershey, PA USA
[2] Penn State Coll Med, Dept Publ Hlth Sci, Div Biostat & Bioinformat, Hershey, PA USA
[3] Case Western Reserve Sch Med, Univ Hosp Seidman Canc Ctr, Dept Radiat Oncol, Cleveland, OH 44106 USA
[4] Penn State Canc Inst, Dept Neurosurg, Hershey, PA USA
[5] Univ Virginia, Sch Med, Dept Neurosurg, Charlottesville, VA USA
[6] Mayo Clin, Dept Radiat Oncol, Rochester, MN USA
[7] Mayo Clin, Dept Radiat Oncol, Jacksonville, FL USA
[8] Ohio State Univ, James Comprehens Canc Ctr, Dept Radiat Oncol, Columbus, OH USA
[9] Case Western Reserve Univ, Sch Med, Dept Populat & Quantitat Hlth Sci, Cleveland, OH USA
来源
COMMUNICATIONS MEDICINE | 2024年 / 4卷 / 01期
关键词
GRADED PROGNOSTIC ASSESSMENT; CELL CARCINOMA; SQUAMOUS-CELL; LUNG; SURVIVAL; SURVEILLANCE;
D O I
10.1038/s43856-024-00445-7
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
BackgroundBrain 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.MethodsBreast 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.ResultsWe 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.ConclusionsWe 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. Miccio et al. utilize the US National Cancer Database (NCDB) to develop models to estimate the risk of brain metastasis for newly-diagnosed cancers. They provide nomograms and a web tool for risk estimation across multiple cancer types. 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.
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页数:12
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