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In Journal of translational medicine ; h5-index 0.0

BACKGROUND : This study aimed to establish and validate a nomogram for predicting brain metastasis in patients with bladder cancer (BCa) and assess various treatment modalities using a primary cohort comprising 234 patients with clinicopathologically-confirmed BCa from 2004 to 2015 in the National Cancer Database.

METHODS : Machine learning method and Cox model were used for nomogram construction. For BCa patients with brain metastasis, surgery of the primary site, chemotherapy, radiation therapy, palliative care, brain confinement of metastatic sites, and the Charlson/Deyo Score were predictive features identified for building the nomogram.

RESULTS : For the original 169 patients considered in the model, the areas under the receiver operating characteristic curve (AUC) were 0.823 (95% CI 0.758-0.889, P < 0.001) and 0.854 (95% CI 0.785-0.924, P < 0.001) for 0.5- and 1-year overall survival respectively. In the validation cohort, the nomogram displayed similar AUCs of 0.838 (95% CI 0.738-0.937, P < 0.001) and 0.809 (95% CI 0.680-0.939, P < 0.001), respectively. The high and low risk groups had median survivals of 1.91 and 5.09 months for the training cohort and 1.68 and 8.05 months for the validation set, respectively (both P < 0.0001).

CONCLUSIONS : Our prognostic nomogram provides a useful tool for overall survival prediction as well as assessing the risk and optimal treatment for BCa patients with brain metastasis.

Yao Zhixian, Zheng Zhong, Ke Wu, Wang Renjie, Mu Xingyu, Sun Feng, Wang Xiang, Garg Shivank, Shi Wenyin, He Yinyan, Liu Zhihong


Bladder cancer, Brain metastasis, Machine learning, Nomogram, Overall survival